Publications

2024

• F. Bickford Smith , A. Foster , T. Rainforth , Making better use of unlabelled data in Bayesian active learning, International Conference on Artificial Intelligence and Statistics, 2024.
  

  @article{bickfordsmith2024making,
    author = {Bickford Smith, Freddie and Foster, Adam and Rainforth, Tom},
    year = {2024},
    title = {Making better use of unlabelled data in {Bayesian} active learning},
    journal = {International Conference on Artificial Intelligence and Statistics}
  }
  

• T. Rainforth , A. Foster , D. R. Ivanova , F. Bickford Smith , Modern Bayesian experimental design, Statistical Science, 2024.
  

  @article{rainforth2023modern,
    author = {Rainforth, Tom and Foster, Adam and Ivanova, Desi R. and Bickford Smith, Freddie},
    year = {2024},
    title = {Modern {Bayesian} experimental design},
    journal = {Statistical Science}
  }
  

2023

• F. Bickford Smith , A. Kirsch , S. Farquhar , Y. Gal , A. Foster , T. Rainforth , Prediction-oriented Bayesian active learning, International Conference on Artificial Intelligence and Statistics, 2023.
  

  @article{bickfordsmith2023prediction,
    author = {Bickford Smith, Freddie and Kirsch, Andreas and Farquhar, Sebastian and Gal, Yarin and Foster, Adam and Rainforth, Tom},
    year = {2023},
    title = {Prediction-oriented {Bayesian} active learning},
    journal = {International Conference on Artificial Intelligence and Statistics}
  }
  

• S. Bouabid , J. Fawkes , D. Sejdinovic , Returning The Favour: When Regression Benefits From Probabilistic Causal Knowledge, arXiv preprint arXiv:2301.11214, 2023.
  

  @article{bouabid2023returning,
    title = {Returning The Favour: When Regression Benefits From Probabilistic Causal Knowledge},
    author = {Bouabid, Shahine and Fawkes, Jake and Sejdinovic, Dino},
    journal = {arXiv preprint arXiv:2301.11214},
    year = {2023}
  }
  

• T. S. Richardson , R. J. Evans , J. M. Robins , I. Shpitser , Nested Markov properties for acyclic directed mixed graphs, Annals of Statistics, vol. 51, no. 1, 334–361, 2023.
  

  @article{richardson23nested,
    title = {Nested Markov properties for acyclic directed mixed graphs},
    author = {Richardson, Thomas S and Evans, R. J. and Robins, James M and Shpitser, Ilya},
    journal = {Annals of Statistics},
    volume = {51},
    number = {1},
    pages = {334--361},
    year = {2023},
    publisher = {Institute of Mathematical Statistics},
    archiveprefix = {arXiv},
    eprint = {1701.06686}
  }
  

• R. J. Evans , V. Didelez , Parameterizing and simulating from causal models, Journal of the Royal Statistical Society, Series B (with discussion), 2023.
  

  @article{evans23simulating,
    title = {Parameterizing and simulating from causal models},
    author = {Evans, R. J. and Didelez, Vanessa},
    journal = {Journal of the Royal Statistical Society, Series B (with discussion)},
    year = {2023},
    archiveprefix = {arXiv},
    eprint = {2109.03694}
  }
  

• R. J. Evans , Latent-free equivalent mDAGs, Algebraic Statistics, 2023.
  

  @article{evans23latent,
    title = {Latent-free equivalent mDAGs},
    author = {Evans, R. J.},
    journal = {Algebraic Statistics},
    year = {2023},
    archiveprefix = {arXiv},
    eprint = {2209.06534}
  }
  

• T. Reichelt , L. Ong , T. Rainforth , Pitfalls of Full Bayesian Inference in Universal Probabilistic Programming, in POPL Workshop on Languages for Inference (LAFI), 2023.
  

  @inproceedings{reichelt2023pitfalls,
    title = {Pitfalls of Full Bayesian Inference in Universal Probabilistic Programming},
    author = {Reichelt, Tim and Ong, Luke and Rainforth, Tom},
    year = {2023},
    booktitle = {POPL Workshop on Languages for Inference (LAFI)}
  }
  

2022

• C. U. Carmona , G. Nicholls , Scalable Semi-Modular Inference with Variational Meta-Posteriors, Apr. 2022.
  
  @article{Carmona2022scalable,
    author = {Carmona, C. U. and Nicholls, G.K.},
    month = apr,
    title = {Scalable Semi-Modular Inference with Variational Meta-Posteriors},
    year = {2022}
  }
  

• G. K. Nicholls , J. E. Lee , C. H. Wu , C. U. Carmona , Valid belief updates for prequentially additive loss functions arising in Semi-Modular Inference, Jan. 2022.
  
  @article{Nicholls2022,
    author = {Nicholls, G. K. and Lee, J. E. and Wu, C. H. and Carmona, C. U.},
    month = jan,
    title = {Valid belief updates for prequentially additive loss functions arising in Semi-Modular Inference},
    year = {2022}
  }
  

• T. G. J. Rudner , F. Bickford Smith , Q. Feng , Y. W. Teh , Y. Gal , Continual learning via sequential function-space variational inference, International Conference on Machine Learning, 2022.
  

  @article{rudner2022continual,
    author = {Rudner, Tim G. J. and Bickford Smith, Freddie and Feng, Qixuan and Teh, Yee Whye and Gal, Yarin},
    year = {2022},
    title = {Continual learning via sequential function-space variational inference},
    journal = {International Conference on Machine Learning}
  }
  

• S. Bouabid , D. Watson-Parris , D. Sejdinovic , Bayesian inference for aerosol vertical profiles, in NeurIPS Workshop on Tackling Climate Change with Machine Learning, 2022.
  

  @inproceedings{bouabid2022bayesian,
    title = {Bayesian inference for aerosol vertical profiles},
    author = {Bouabid, Shahine and Watson-Parris, Duncan and Sejdinovic, Dino},
    year = {2022},
    booktitle = {NeurIPS Workshop on Tackling Climate Change with Machine Learning}
  }
  

• S. Bouabid , D. Watson-Parris , S. Stefanović , A. Nenes , D. Sejdinovic , AODisaggregation: toward global aerosol vertical profiles, arXiv preprint arXiv:2205.04296, 2022.
  

  @article{bouabid2022aodisaggregation,
    title = {AODisaggregation: toward global aerosol vertical profiles},
    author = {Bouabid, Shahine and Watson-Parris, Duncan and Stefanovi{\'c}, Sofija and Nenes, Athanasios and Sejdinovic, Dino},
    journal = {arXiv preprint arXiv:2205.04296},
    year = {2022}
  }
  

• D. Watson-Parris , Y. Rao , D. Olivié , Ø. Seland , P. Nowack , G. Camps-Valls , P. Stier , S. Bouabid , M. Dewey , E. Fons , . others , ClimateBench v1. 0: A Benchmark for Data-Driven Climate Projections, Journal of Advances in Modeling Earth Systems, vol. 14, no. 10, e2021MS002954, 2022.
  

  @article{watson2022climatebench,
    title = {ClimateBench v1. 0: A Benchmark for Data-Driven Climate Projections},
    author = {Watson-Parris, Duncan and Rao, Yuhan and Olivi{\'e}, Dirk and Seland, {\O}yvind and Nowack, Peer and Camps-Valls, Gustau and Stier, Philip and Bouabid, Shahine and Dewey, Maura and Fons, Emilie and others},
    journal = {Journal of Advances in Modeling Earth Systems},
    volume = {14},
    number = {10},
    pages = {e2021MS002954},
    year = {2022},
    publisher = {Wiley Online Library}
  }
  

• O. Clivio , F. Falck , B. Lehmann , G. Deligiannidis , C. Holmes , Neural score matching for high-dimensional causal inference, in International Conference on Artificial Intelligence and Statistics, 2022, 7076–7110.
  

  @inproceedings{clivio2022neural,
    title = {Neural score matching for high-dimensional causal inference},
    author = {Clivio, Oscar and Falck, Fabian and Lehmann, Brieuc and Deligiannidis, George and Holmes, Chris},
    booktitle = {International Conference on Artificial Intelligence and Statistics},
    pages = {7076--7110},
    year = {2022},
    organization = {PMLR}
  }
  

• Y. Shi , V. De Bortoli , G. Deligiannidis , A. Doucet , Conditional Simulation Using Diffusion Schr\backslash" odinger Bridges, arXiv preprint arXiv:2202.13460, 2022.
  

  @article{shi2022conditional,
    title = {Conditional Simulation Using Diffusion Schr$\backslash$" odinger Bridges},
    author = {Shi, Yuyang and De Bortoli, Valentin and Deligiannidis, George and Doucet, Arnaud},
    journal = {arXiv preprint arXiv:2202.13460},
    year = {2022}
  }
  

• E. Clerico , A. Shidani , G. Deligiannidis , A. Doucet , Chained Generalisation Bounds, in COLT 2022, 2022, no. arXiv:2203.00977.
  

  @inproceedings{clerico2022chained,
    title = {Chained Generalisation Bounds},
    author = {Clerico, Eugenio and Shidani, Amitis and Deligiannidis, George and Doucet, Arnaud},
    booktitle = {COLT 2022},
    number = {arXiv:2203.00977},
    year = {2022}
  }
  

• A. Campbell , J. Benton , V. De Bortoli , T. Rainforth , G. Deligiannidis , A. Doucet , A Continuous Time Framework for Discrete Denoising Models, arXiv preprint arXiv:2205.14987, 2022.
  

  @article{campbell2022continuous,
    title = {A Continuous Time Framework for Discrete Denoising Models},
    author = {Campbell, Andrew and Benton, Joe and De Bortoli, Valentin and Rainforth, Tom and Deligiannidis, George and Doucet, Arnaud},
    journal = {arXiv preprint arXiv:2205.14987},
    year = {2022}
  }
  

• A. Shidani , G. Deligiannidis , A. Doucet , Ranking in Contextual Multi-Armed Bandits, arXiv preprint arXiv:2207.00109, 2022.
  

  @article{shidani2022ranking,
    title = {Ranking in Contextual Multi-Armed Bandits},
    author = {Shidani, Amitis and Deligiannidis, George and Doucet, Arnaud},
    journal = {arXiv preprint arXiv:2207.00109},
    year = {2022}
  }
  

• E. Clerico , G. Deligiannidis , B. Guedj , A. Doucet , A PAC-Bayes bound for deterministic classifiers, arXiv preprint arXiv:2209.02525, 2022.
  

  @article{clerico2022pac,
    title = {A PAC-Bayes bound for deterministic classifiers},
    author = {Clerico, Eugenio and Deligiannidis, George and Guedj, Benjamin and Doucet, Arnaud},
    journal = {arXiv preprint arXiv:2209.02525},
    year = {2022}
  }
  

• K. Kusi-Mensah , R. Tamambang , T. Bella-Awusah , S. Ogunmola , A. Afolayan , E. Toska , L. Hertzog , W. Rudgard , R. J. Evans , O. Omigbodun , Accelerating progress towards the sustainable development goals for adolescents in Ghana: a cross-sectional study, Psychology, Health & Medicine, vol. 27, no. sup1, 49–66, 2022.
  

  @article{kusi22accelerating,
    title = {Accelerating progress towards the sustainable development goals for adolescents in Ghana: a cross-sectional study},
    author = {Kusi-Mensah, Kwabena and Tamambang, Rita and Bella-Awusah, Tolulope and Ogunmola, Segun and Afolayan, Adeola and Toska, Elona and Hertzog, Lucas and Rudgard, William and Evans, R. J. and Omigbodun, Olayinka},
    journal = {Psychology, Health \& Medicine},
    volume = {27},
    number = {sup1},
    pages = {49--66},
    year = {2022},
    publisher = {Taylor \& Francis}
  }
  

• J. Fawkes , R. J. Evans , D. Sejdinovic , Selection, ignorability and challenges with causal fairness, in Conference on Causal Learning and Reasoning, 2022, 275–289.
  

  @inproceedings{fawkes22selection,
    title = {Selection, ignorability and challenges with causal fairness},
    author = {Fawkes, Jake and Evans, R. J. and Sejdinovic, Dino},
    booktitle = {Conference on Causal Learning and Reasoning},
    pages = {275--289},
    year = {2022},
    organization = {PMLR},
    archiveprefix = {arXiv},
    eprint = {2202.13774}
  }
  

• B. Yao , R. J. Evans , Algebraic properties of HTC-identifiable graphs, Algebraic Statistics, vol. 13, no. 1, 19–39, 2022.
  

  @article{yao22algebraic,
    title = {Algebraic properties of HTC-identifiable graphs},
    author = {Yao, Bohao and Evans, R. J.},
    journal = {Algebraic Statistics},
    volume = {13},
    number = {1},
    pages = {19--39},
    year = {2022},
    publisher = {Mathematical Sciences Publishers}
  }
  

• F. Falck , C. Williams , D. Danks , G. Deligiannidis , C. Yau , C. Holmes , A. Doucet , M. Willetts , A Multi-Resolution Framework for U-Nets with Applications to Hierarchical VAEs, Advances in Neural Information Processing Systems, 2022.
  

  @article{falck2022unet,
    title = {A Multi-Resolution Framework for U-Nets with Applications to Hierarchical VAEs},
    author = {Falck, Fabian and Williams, Christopher and Danks, Dominic and Deligiannidis, George and Yau, Christopher and Holmes, Chris and Doucet, Arnaud and Willetts, Matthew},
    journal = {Advances in Neural Information Processing Systems},
    year = {2022}
  }
  

• J. Fawkes , R. Evans , D. Sejdinovic , Selection, Ignorability and Challenges With Causal Fairness, arXiv preprint arXiv:2202.13774, 2022.
  

  @article{fawkes2022selection,
    title = {Selection, Ignorability and Challenges With Causal Fairness},
    author = {Fawkes, Jake and Evans, Robin and Sejdinovic, Dino},
    journal = {arXiv preprint arXiv:2202.13774},
    year = {2022}
  }
  

• J. Kossen , S. Farquhar , Y. Gal , T. Rainforth , Active Surrogate Estimators: An Active Learning Approach to Label-Efficient Model Evaluation, in Advances in Neural Information Processing Systems, 2022.
  
  @inproceedings{kossen2022active,
    author = {Kossen, Jannik and Farquhar, Sebastian and Gal, Yarin and Rainforth, Tom},
    booktitle = {Advances in Neural Information Processing Systems},
    title = {Active Surrogate Estimators: An Active Learning Approach to Label-Efficient Model Evaluation},
    year = {2022}
  }
  

• T. Reichelt , L. Ong , T. Rainforth , Rethinking Variational Inference for Probabilistic Programs with Stochastic Support, in Advances in Neural Information Processing Systems, 2022.
  

  @inproceedings{reichelt2022rethinking,
    title = {Rethinking Variational Inference for Probabilistic Programs with Stochastic Support},
    author = {Reichelt, Tim and Ong, Luke and Rainforth, Tom},
    year = {2022},
    booktitle = {Advances in Neural Information Processing Systems}
  }
  

• T. Reichelt , A. Goliński , L. Ong , T. Rainforth , Expectation programming: Adapting probabilistic programming systems to estimate expectations efficiently, in Proceedings of the Thirty-Eighth Conference on Uncertainty in Artificial Intelligence, 2022.
  
  @inproceedings{reichelt2022expectation,
    title = {Expectation programming: Adapting probabilistic programming systems to estimate expectations efficiently},
    author = {Reichelt, Tim and Goli{\'n}ski, Adam and Ong, Luke and Rainforth, Tom},
    booktitle = {Proceedings of the Thirty-Eighth Conference on Uncertainty in Artificial Intelligence},
    year = {2022}
  }
  

2021

• G. Deligiannidis , D. Paulin , A. Bouchard-Côté , A. Doucet , Randomized Hamiltonian Monte Carlo as scaling limit of the bouncy particle sampler and dimension-free convergence rates, Annals of Applied Probability, vol. 31, no. 6, 2612–2662, 2021.
  

  @article{deligiannidis2021randomized,
    title = {Randomized Hamiltonian Monte Carlo as scaling limit of the bouncy particle sampler and dimension-free convergence rates},
    author = {Deligiannidis, George and Paulin, Daniel and Bouchard-C{\^o}t{\'e}, Alexandre and Doucet, Arnaud},
    journal = {Annals of Applied Probability},
    volume = {31},
    number = {6},
    pages = {2612--2662},
    year = {2021}
  }
  

• G. Deligiannidis , S. Gouëzel , Z. Kosloff , Boundary of the Range of a random walk and the F\backslash" olner property, Electronic Journal of Probability, vol. 26, 1–39, 2021.
  

  @article{deligiannidis2021boundary,
    title = {Boundary of the Range of a random walk and the F$\backslash$" olner property},
    author = {Deligiannidis, George and Gou{\"e}zel, Sebastien and Kosloff, Zemer},
    journal = {Electronic Journal of Probability},
    volume = {26},
    pages = {1--39},
    year = {2021}
  }
  

• G. Deligiannidis , S. Maurer , M. V. Tretyakov , Random walk algorithm for the Dirichlet problem for parabolic integro-differential equation, BIT Numerical Mathematics, vol. 61, no. 4, 1223–1269, 2021.
  

  @article{deligiannidis2021random,
    title = {Random walk algorithm for the Dirichlet problem for parabolic integro-differential equation},
    author = {Deligiannidis, George and Maurer, S and Tretyakov, Michael V},
    journal = {BIT Numerical Mathematics},
    volume = {61},
    number = {4},
    pages = {1223--1269},
    year = {2021},
    publisher = {Springer Netherlands}
  }
  

• A. Corenflos , J. Thornton , G. Deligiannidis , A. Doucet , Differentiable particle filtering via entropy-regularized optimal transport, in International Conference on Machine Learning, 2021, 2100–2111.
  

  @inproceedings{corenflos2021differentiable,
    title = {Differentiable particle filtering via entropy-regularized optimal transport},
    author = {Corenflos, Adrien and Thornton, James and Deligiannidis, George and Doucet, Arnaud},
    booktitle = {International Conference on Machine Learning},
    pages = {2100--2111},
    year = {2021},
    organization = {PMLR}
  }
  

• A. Camuto , G. Deligiannidis , M. A. Erdogdu , M. Gurbuzbalaban , U. Simsekli , L. Zhu , Fractal structure and generalization properties of stochastic optimization algorithms, NeurIPS (Spotlight), vol. 34, 18774–18788, 2021.
  

  @article{camuto2021fractal,
    title = {Fractal structure and generalization properties of stochastic optimization algorithms},
    author = {Camuto, Alexander and Deligiannidis, George and Erdogdu, Murat A and Gurbuzbalaban, Mert and Simsekli, Umut and Zhu, Lingjiong},
    journal = {NeurIPS (Spotlight)},
    volume = {34},
    pages = {18774--18788},
    year = {2021}
  }
  

• E. Clerico , G. Deligiannidis , A. Doucet , Wide stochastic networks: Gaussian limit and PAC-Bayesian training, arXiv preprint arXiv:2106.09798, 2021.
  

  @article{clerico2021wide,
    title = {Wide stochastic networks: Gaussian limit and PAC-Bayesian training},
    author = {Clerico, Eugenio and Deligiannidis, George and Doucet, Arnaud},
    journal = {arXiv preprint arXiv:2106.09798},
    year = {2021}
  }
  

• G. Deligiannidis , V. De Bortoli , A. Doucet , Quantitative uniform stability of the iterative proportional fitting procedure, arXiv preprint arXiv:2108.08129, 2021.
  

  @article{deligiannidis2021quantitative,
    title = {Quantitative uniform stability of the iterative proportional fitting procedure},
    author = {Deligiannidis, George and De Bortoli, Valentin and Doucet, Arnaud},
    journal = {arXiv preprint arXiv:2108.08129},
    year = {2021}
  }
  

• E. Clerico , G. Deligiannidis , A. Doucet , Conditional Gaussian PAC-Bayes, in Accepted at AISTATS 2022, 2021, no. arXiv preprint arXiv:2110.11886.
  

  @inproceedings{clerico2021conditional,
    title = {Conditional Gaussian PAC-Bayes},
    author = {Clerico, Eugenio and Deligiannidis, George and Doucet, Arnaud},
    booktitle = {Accepted at AISTATS 2022},
    number = {arXiv preprint arXiv:2110.11886},
    year = {2021}
  }
  

• E. Khribch , G. Deligiannidis , D. Paulin , On Mixing Times of Metropolized Algorithm With Optimization Step (MAO): A New Framework, arXiv preprint arXiv:2112.00565, 2021.
  

  @article{khribch2021mixing,
    title = {On Mixing Times of Metropolized Algorithm With Optimization Step (MAO): A New Framework},
    author = {Khribch, EL and Deligiannidis, George and Paulin, Daniel},
    journal = {arXiv preprint arXiv:2112.00565},
    year = {2021}
  }
  

• R. J. Evans , Dependency in DAG models with hidden variables, in Uncertainty in Artificial Intelligence, 2021, 813–822.
  

  @inproceedings{evans21dependency,
    title = {Dependency in DAG models with hidden variables},
    author = {Evans, R. J.},
    booktitle = {Uncertainty in Artificial Intelligence},
    pages = {813--822},
    year = {2021},
    organization = {PMLR},
    archiveprefix = {arXiv},
    eprint = {2106.07523}
  }
  

• E. Černis , R. J. Evans , A. Ehlers , D. Freeman , Dissociation in relation to other mental health conditions: An exploration using network analysis, Journal of Psychiatric Research, vol. 136, 460–467, 2021.
  

  @article{cernis21dissociation,
    title = {Dissociation in relation to other mental health conditions: An exploration using network analysis},
    author = {{\v{C}}ernis, Emma and Evans, R. J. and Ehlers, Anke and Freeman, Daniel},
    journal = {Journal of Psychiatric Research},
    volume = {136},
    pages = {460--467},
    year = {2021},
    publisher = {Elsevier}
  }
  

• F. Falck , H. Zhang , M. Willetts , G. Nicholson , C. Yau , C. Holmes , Multi-Facet Clustering Variational Autoencoders, Advances in Neural Information Processing Systems, 2021.
  
  @article{falck2021mfcvae,
    title = {Multi-Facet Clustering Variational Autoencoders},
    author = {Falck, Fabian and Zhang, Haoting and Willetts, Matthew and Nicholson, George and Yau, Christopher and Holmes, Chris},
    journal = {Advances in Neural Information Processing Systems},
    year = {2021}
  }
  

• T. Farghly , P. Rebeschini , Time-independent Generalization Bounds for SGLD in Non-convex Settings, in Advances in Neural Information Processing Systems 34, 2021.
  
  We establish generalization error bounds for stochastic gradient Langevin dynamics (SGLD) with constant learning rate under the assumptions of dissipativity and smoothness, a setting that has received increased attention in the sampling/optimization literature. Unlike existing bounds for SGLD in non-convex settings, ours are time-independent and decay to zero as the sample size increases. Using the framework of uniform stability, we establish time-independent bounds by exploiting the Wasserstein contraction property of the Langevin diffusion, which also allows us to circumvent the need to bound gradients using Lipschitz-like assumptions. Our analysis also supports variants of SGLD that use different discretization methods, incorporate Euclidean projections, or use non-isotropic noise.

  @inproceedings{farghly2021time,
    title = {Time-independent Generalization Bounds for SGLD in Non-convex Settings},
    author = {Farghly, Tyler and Rebeschini, Patrick},
    booktitle = {Advances in Neural Information Processing Systems 34},
    year = {2021},
    publisher = {Curran Associates, Inc.}
  }
  

• E. Fong , C. Holmes , S. G. Walker , Martingale Posterior Distributions, arXiv preprint arXiv:2103.15671, 2021.
  

  @article{Fong2021,
    title = {Martingale Posterior Distributions},
    author = {Fong, Edwin and Holmes, Chris and Walker, Stephen G},
    journal = {arXiv preprint arXiv:2103.15671},
    year = {2021}
  }
  

• A. Foster , R. Pukdee , T. Rainforth , Improving Transformation Invariance in Contrastive Representation Learning, International Conference on Learning Representations (ICLR), 2021.
  
  We propose methods to strengthen the invariance properties of representations obtained by contrastive learning. While existing approaches implicitly induce a degree of invariance as representations are learned, we look to more directly enforce invariance in the encoding process. To this end, we first introduce a training objective for contrastive learning that uses a novel regularizer to control how the representation changes under transformation. We show that representations trained with this objective perform better on downstream tasks and are more robust to the introduction of nuisance transformations at test time. Second, we propose a change to how test time representations are generated by introducing a feature averaging approach that combines encodings from multiple transformations of the original input, finding that this leads to across the board performance gains. Finally, we introduce the novel Spirograph dataset to explore our ideas in the context of a differentiable generative process with multiple downstream tasks, showing that our techniques for learning invariance are highly beneficial.

  @article{foster2021improving,
    title = {Improving Transformation Invariance in Contrastive Representation Learning},
    author = {Foster, Adam and Pukdee, Rattana and Rainforth, Tom},
    year = {2021},
    journal = {International Conference on Learning Representations (ICLR)}
  }
  

• A. Foster , D. R. Ivanova , I. Malik , T. Rainforth , Deep Adaptive Design: Amortizing Sequential Bayesian Experimental Design, International Conference on Machine Learning (ICML, long presentation), 2021.
  
  We introduce Deep Adaptive Design (DAD), a general method for amortizing the cost of performing sequential adaptive experiments using the framework of Bayesian optimal experimental design (BOED). Traditional sequential BOED approaches require substantial computational time at each stage of the experiment. This makes them unsuitable for most real-world applications, where decisions must typically be made quickly. DAD addresses this restriction by learning an amortized design network upfront and then using this to rapidly run (multiple) adaptive experiments at deployment time. This network takes as input the data from previous steps, and outputs the next design using a single forward pass; these design decisions can be made in milliseconds during the live experiment. To train the network, we introduce contrastive information bounds that are suitable objectives for the sequential setting, and propose a customized network architecture that exploits key symmetries. We demonstrate that DAD successfully amortizes the process of experimental design, outperforming alternative strategies on a number of problems.

  @article{foster2021deep,
    title = {{Deep Adaptive Design: Amortizing Sequential Bayesian Experimental Design}},
    author = {Foster, Adam and Ivanova, Desi R and Malik, Ilyas and Rainforth, Tom},
    year = {2021},
    journal = {International Conference on Machine Learning (ICML, long presentation)}
  }
  

• E. Mathieu , A. Foster , Y. W. Teh , On Contrastive Representations of Stochastic Processes, 35th Conference on Neural Information Processing Systems (NeurIPS 2021), 2021.
  
  Learning representations of stochastic processes is an emerging problem in machine learning with applications from meta-learning to physical object models to time series. Typical methods rely on exact reconstruction of observations, but this approach breaks down as observations become high-dimensional or noise distributions become complex. To address this, we propose a unifying framework for learning contrastive representations of stochastic processes (CReSP) that does away with exact reconstruction. We dissect potential use cases for stochastic process representations, and propose methods that accommodate each. Empirically, we show that our methods are effective for learning representations of periodic functions, 3D objects and dynamical processes. Our methods tolerate noisy high-dimensional observations better than traditional approaches, and the learned representations transfer to a range of downstream tasks.

  @article{mathieu2021contrastive,
    title = {{On Contrastive Representations of Stochastic Processes}},
    author = {Mathieu, Emile and Foster, Adam and Teh, Yee Whye},
    year = {2021},
    journal = {35th Conference on Neural Information Processing Systems (NeurIPS 2021)}
  }
  

• D. R. Ivanova , A. Foster , S. Kleinegesse , M. U. Gutmann , T. Rainforth , Implicit Deep Adaptive Design: Policy-Based Experimental Design without Likelihoods, 35th Conference on Neural Information Processing Systems (NeurIPS 2021), 2021.
  
  We introduce implicit Deep Adaptive Design (iDAD), a new method for performing adaptive experiments in real-time with implicit models. iDAD amortizes the cost of Bayesian optimal experimental design (BOED) by learning a design policy network upfront, which can then be deployed quickly at the time of the experiment. The iDAD network can be trained on any model which simulates differentiable samples, unlike previous design policy work that requires a closed form likelihood and conditionally independent experiments. At deployment, iDAD allows design decisions to be made in milliseconds, in contrast to traditional BOED approaches that require heavy computation during the experiment itself. We illustrate the applicability of iDAD on a number of experiments, and show that it provides a fast and effective mechanism for performing adaptive design with implicit models.

  @article{ivanova2021implicit,
    title = {{Implicit Deep Adaptive Design: Policy-Based Experimental Design without Likelihoods}},
    author = {Ivanova, Desi R and Foster, Adam and Kleinegesse, Steven and Gutmann, Michael U and Rainforth, Tom},
    year = {2021},
    journal = {35th Conference on Neural Information Processing Systems (NeurIPS 2021)}
  }
  

• J. Kossen , N. Band , C. Lyle , A. N. Gomez , T. Rainforth , Y. Gal , Self-Attention Between Datapoints: Going Beyond Individual Input-Output Pairs in Deep Learning, Advances in Neural Information Processing Systems, 2021.
  
  @article{kossen2021self,
    title = {{Self-Attention Between Datapoints: Going Beyond Individual Input-Output Pairs in Deep Learning}},
    author = {Kossen, Jannik and Band, Neil and Lyle, Clare and Gomez, Aidan N and Rainforth, Tom and Gal, Yarin},
    journal = {Advances in Neural Information Processing Systems},
    year = {2021}
  }
  

• J. Kossen , S. Farquhar , Y. Gal , T. Rainforth , Active Testing: Sample-Efficient Model Evaluation, International Conference on Machine Learning, 2021.
  
  @article{kossen2021active,
    title = {{Active Testing: Sample-Efficient Model Evaluation}},
    author = {Kossen, Jannik and Farquhar, Sebastian and Gal, Yarin and Rainforth, Tom},
    journal = {International Conference on Machine Learning},
    year = {2021}
  }
  

• P. J. Ball , C. Lu , J. Parker-Holder , S. Roberts , Augmented World Models Facilitate Zero-Shot Dynamics Generalization From a Single Offline Environment, International Conference on Machine Learning, 2021.
  
  @article{ball2021augmented,
    title = {Augmented World Models Facilitate Zero-Shot Dynamics Generalization From a Single Offline Environment},
    author = {Ball, Philip J. and Lu, Cong and Parker-Holder, Jack and Roberts, Stephen},
    year = {2021},
    journal = {International Conference on Machine Learning}
  }
  

• X. Wan , V. Nguyen , H. Ha , B. Ru , C. Lu , M. A. Osborne , Think Global and Act Local: Bayesian Optimisation over High-Dimensional Categorical and Mixed Search Spaces, International Conference on Machine Learning, 2021.
  
  @article{wan2021think,
    title = {Think Global and Act Local: Bayesian Optimisation over High-Dimensional Categorical and Mixed Search Spaces},
    author = {Wan, Xingchen and Nguyen, Vu and Ha, Huong and Ru, Binxin and Lu, Cong and Osborne, Michael A.},
    year = {2021},
    journal = {International Conference on Machine Learning}
  }
  

• L. Zintgraf , L. Feng , C. Lu , M. Igl , K. Hartikainen , K. Hofmann , S. Whiteson , Exploration in Approximate Hyper-State Space for Meta Reinforcement Learning, International Conference on Machine Learning, 2021.
  
  @article{zintgraf2021exploration,
    title = {Exploration in Approximate Hyper-State Space for Meta Reinforcement Learning},
    author = {Zintgraf, Luisa and Feng, Leo and Lu, Cong and Igl, Maximilian and Hartikainen, Kristian and Hofmann, Katja and Whiteson, Shimon},
    year = {2021},
    journal = {International Conference on Machine Learning}
  }
  

• T. G. J. Rudner , C. Lu , M. A. Osborne , Y. Gal , Y. W. Teh , On Pathologies in KL-Regularized Reinforcement Learning from Expert Demonstrations, ICLR 2021 RobustML Workshop, 2021.
  
  @article{rudner2021pathologies,
    title = {On Pathologies in KL-Regularized Reinforcement Learning from Expert Demonstrations},
    author = {Rudner, Tim G. J. and Lu, Cong and Osborne, Michael A. and Gal, Yarin and Teh, Yee Whye},
    year = {2021},
    journal = {ICLR 2021 RobustML Workshop}
  }
  

• S. Zafar , G. K. Nicholls , Measuring diachronic sense change: new models and Monte Carlo methods for Bayesian inference, arXiv preprint arXiv:2105.00819 JRSSC (\it to appear), 2021.
  

  @article{zafar21,
    title = {Measuring diachronic sense change: new models and {M}onte {C}arlo methods for {B}ayesian inference},
    author = {Zafar, S. and Nicholls, G. K.},
    journal = {arXiv preprint arXiv:2105.00819 {JRSSC ({\it to appear})}},
    year = {2021}
  }
  

• J. E. Lee , G. K. Nicholls , Tree based credible set estimation, Statistics and Computing, vol. 31, 69, 2021.
  

  @article{lee21,
    title = {Tree based credible set estimation},
    author = {Lee, J. E. and Nicholls, G. K.},
    journal = {Statistics and Computing},
    pages = {69},
    volume = {31},
    note = {arXiv preprint arXiv:2012.13837},
    year = {2021}
  }
  

• D. Richards , J. Mourtada , L. Rosasco , Asymptotics of Ridge(less) Regression under General Source Condition , in Proceedings of The 24th International Conference on Artificial Intelligence and Statistics, 2021, vol. 130, 3889–3897.
  
  @inproceedings{richards2021asymptotics,
    title = { Asymptotics of Ridge(less) Regression under General Source Condition },
    author = {Richards, Dominic and Mourtada, Jaouad and Rosasco, Lorenzo},
    booktitle = {Proceedings of The 24th International Conference on Artificial Intelligence and Statistics},
    pages = {3889--3897},
    year = {2021},
    volume = {130},
    series = {Proceedings of Machine Learning Research},
    month = {13--15 Apr},
    publisher = {PMLR}
  }
  

• D. Richards , M. Rabbat , Learning with Gradient Descent and Weakly Convex Losses , in Proceedings of The 24th International Conference on Artificial Intelligence and Statistics, 2021, vol. 130, 1990–1998.
  
  We study the learning performance of gradient descent when the empirical risk is weakly convex, namely, the smallest negative eigenvalue of the empirical risk’s Hessian is bounded in magnitude. By showing that this eigenvalue can control the stability of gradient descent, generalisation error bounds are proven that hold under a wider range of step sizes compared to previous work. Out of sample guarantees are then achieved by decomposing the test error into generalisation, optimisation and approximation errors, each of which can be bounded and traded off with respect to algorithmic parameters, sample size and magnitude of this eigenvalue. In the case of a two layer neural network, we demonstrate that the empirical risk can satisfy a notion of local weak convexity, specifically, the Hessian’s smallest eigenvalue during training can be controlled by the normalisation of the layers, i.e., network scaling. This allows test error guarantees to then be achieved when the population risk minimiser satisfies a complexity assumption. By trading off the network complexity and scaling, insights are gained into the implicit bias of neural network scaling, which are further supported by experimental findings.

  @inproceedings{richards2021learning,
    title = { Learning with Gradient Descent and Weakly Convex Losses },
    author = {Richards, Dominic and Rabbat, Mike},
    booktitle = {Proceedings of The 24th International Conference on Artificial Intelligence and Statistics},
    pages = {1990--1998},
    year = {2021},
    volume = {130},
    series = {Proceedings of Machine Learning Research},
    month = {13--15 Apr},
    publisher = {PMLR}
  }
  

• S. L. Chau , S. Bouabid , D. Sejdinovic , Deconditional Downscaling with Gaussian Processes, in Advances in Neural Information Processing Systems (NeurIPS), 2021.
  
  Refining low-resolution (LR) spatial fields with high-resolution (HR) information is challenging as the diversity of spatial datasets often prevents direct matching of observations. Yet, when LR samples are modeled as aggregate conditional means of HR samples with respect to a mediating variable that is globally observed, the recovery of the underlying fine-grained field can be framed as taking an "inverse" of the conditional expectation, namely a deconditioning problem. In this work, we introduce conditional mean processes (CMP), a new class of Gaussian Processes describing conditional means. By treating CMPs as inter-domain features of the underlying field, a posterior for the latent field can be established as a solution to the deconditioning problem. Furthermore, we show that this solution can be viewed as a two-staged vector-valued kernel ridge regressor and show that it has a minimax optimal convergence rate under mild assumptions. Lastly, we demonstrate its proficiency in a synthetic and a real-world atmospheric field downscaling problem, showing substantial improvements over existing methods.

  @inproceedings{ChaBouSej2021,
    title = {{Deconditional Downscaling with Gaussian Processes}},
    author = {Chau, Siu Lun and Bouabid, Shahine and Sejdinovic, Dino},
    year = {2021},
    booktitle = {Advances in Neural Information Processing Systems (NeurIPS)}
  }
  

• S. L. Chau , J. Ton , J. Gonzalez , Y. W. Teh , D. Sejdinovic , BayesIMP: Uncertainty Quantification for Causal Data Fusion, in Advances in Neural Information Processing Systems (NeurIPS), 2021.
  
  While causal models are becoming one of the mainstays of machine learning, the problem of uncertainty quantification in causal inference remains challenging. In this paper, we study the causal data fusion problem, where datasets pertaining to multiple causal graphs are combined to estimate the average treatment effect of a target variable. As data arises from multiple sources and can vary in quality and quantity, principled uncertainty quantification becomes essential. To that end, we introduce Bayesian Interventional Mean Processes, a framework which combines ideas from probabilistic integration and kernel mean embeddings to represent interventional distributions in the reproducing kernel Hilbert space, while taking into account the uncertainty within each causal graph. To demonstrate the utility of our uncertainty estimation, we apply our method to the Causal Bayesian Optimisation task and show improvements over state-of-the-art methods.

  @inproceedings{ChaTonGonTehSej2021,
    title = {{BayesIMP: Uncertainty Quantification for Causal Data Fusion}},
    author = {Chau, Siu Lun and Ton, Jean-Francois and Gonzalez, Javier and Teh, Yee Whye and Sejdinovic, Dino},
    year = {2021},
    booktitle = {Advances in Neural Information Processing Systems (NeurIPS)}
  }
  

• R. Hu , G. K. Nicholls , D. Sejdinovic , Large Scale Tensor Regression using Kernels and Variational Inference, Machine Learning, 2021.
  
  We outline an inherent weakness of tensor factorization models when latent factors are expressed as a function of side information and propose a novel method to mitigate this weakness. We coin our method Kernel Fried Tensor (KFT) and present it as a large scale forecasting tool for high dimensional data. Our results show superior performance against LightGBM and Field Aware Factorization Machines (FFM), two algorithms with proven track records widely used in industrial forecasting. We also develop a variational inference framework for KFT and associate our forecasts with calibrated uncertainty estimates on three large scale datasets. Furthermore, KFT is empirically shown to be robust against uninformative side information in terms of constants and Gaussian noise.

  @article{HuNicSej2021,
    title = {{{Large Scale Tensor Regression using Kernels and Variational Inference}}},
    author = {Hu, R. and Nicholls, G. K. and Sejdinovic, D.},
    journal = {Machine Learning},
    year = {2021}
  }
  

• T. Fernandez , A. Gretton , D. Rindt , D. Sejdinovic , A Kernel Log-Rank Test of Independence for Right-Censored Data, Journal of the American Statistical Association, 2021.
  
  With the incorporation of new data gathering methods in clinical research, it becomes fundamental for survival analysis techniques to deal with high-dimensional or/and non-standard covariates. In this paper we introduce a general non-parametric independence test between right-censored survival times and covariates taking values on a general (not necessarily Euclidean) space X. We show that our test statistic has a dual interpretation, first in terms of the supremum of a potentially infinite collection of weight-indexed log-rank tests, with weight functions belonging to a reproducing kernel Hilbert space (RKHS) of functions; and second, as the norm of the difference of embeddings of certain finite measures into the RKHS, similar to the Hilbert-Schmidt Independence Criterion (HSIC) test-statistic. We study the asymptotic properties of the test, finding sufficient conditions to ensure that our test is omnibus. The test statistic can be computed straightforwardly, and the rejection threshold is obtained via an asymptotically consistent Wild-Bootstrap procedure. We perform extensive simulations demonstrating that our testing procedure generally performs better than competing approaches in detecting complex nonlinear dependence.

  @article{FerGreRinSej2021,
    author = {Fernandez, Tamara and Gretton, Arthur and Rindt, David and Sejdinovic, Dino},
    title = {{{A Kernel Log-Rank Test of Independence for Right-Censored Data}}},
    journal = {Journal of the American Statistical Association},
    year = {2021},
    doi = {10.1080/01621459.2021.1961784}
  }
  

• V. Nguyen , S. B. Orbell , D. T. Lennon , H. Moon , F. Vigneau , L. C. Camenzind , L. Yu , D. M. Zumbühl , G. A. D. Briggs , M. A. Osborne , D. Sejdinovic , N. Ares , Deep Reinforcement Learning for Efficient Measurement of Quantum Devices, npj Quantum Information, vol. 7, no. 100, 2021.
  
  Deep reinforcement learning is an emerging machine-learning approach that can teach a computer to learn from their actions and rewards similar to the way humans learn from experience. It offers many advantages in automating decision processes to navigate large parameter spaces. This paper proposes an approach to the efficient measurement of quantum devices based on deep reinforcement learning. We focus on double quantum dot devices, demonstrating the fully automatic identification of specific transport features called bias triangles. Measurements targeting these features are difficult to automate, since bias triangles are found in otherwise featureless regions of the parameter space. Our algorithm identifies bias triangles in a mean time of <30 min, and sometimes as little as 1 min. This approach, based on dueling deep Q-networks, can be adapted to a broad range of devices and target transport features. This is a crucial demonstration of the utility of deep reinforcement learning for decision making in the measurement and operation of quantum devices.

  @article{Nguyen2021,
    title = {Deep Reinforcement Learning for Efficient Measurement of Quantum Devices},
    author = {Nguyen, V. and Orbell, S. B. and Lennon, D. T. and Moon, H. and Vigneau, F. and Camenzind, L. C. and Yu, L. and Zumbühl, D. M. and Briggs, G. A. D. and Osborne, M. A. and Sejdinovic, D. and Ares, N.},
    year = {2021},
    volume = {7},
    number = {100},
    journal = {{npj Quantum Information}},
    doi = {10.1038/s41534-021-00434-x}
  }
  

• A. Caterini , R. Cornish , D. Sejdinovic , A. Doucet , Variational Inference with Continuously-Indexed Normalizing Flows, in Uncertainty in Artificial Intelligence (UAI), 2021.
  
  Continuously-indexed flows (CIFs) have recently achieved improvements over baseline normalizing flows on a variety of density estimation tasks. CIFs do not possess a closed-form marginal density, and so, unlike standard flows, cannot be plugged in directly to a variational inference (VI) scheme in order to produce a more expressive family of approximate posteriors. However, we show here how CIFs can be used as part of an auxiliary VI scheme to formulate and train expressive posterior approximations in a natural way. We exploit the conditional independence structure of multi-layer CIFs to build the required auxiliary inference models, which we show empirically yield low-variance estimators of the model evidence. We then demonstrate the advantages of CIFs over baseline flows in VI problems when the posterior distribution of interest possesses a complicated topology, obtaining improved results in both the Bayesian inference and maximum likelihood settings.

  @inproceedings{CatCorSejDou2021,
    author = {Caterini, Anthony and Cornish, Rob and Sejdinovic, Dino and Doucet, Arnaud},
    title = {{{Variational Inference with Continuously-Indexed Normalizing Flows}}},
    booktitle = {Uncertainty in Artificial Intelligence (UAI)},
    year = {2021}
  }
  

• Z. Li , J. Ton , D. Oglic , D. Sejdinovic , Towards A Unified Analysis of Random Fourier Features, Journal of Machine Learning Research (JMLR), vol. 22, no. 108, 1–51, 2021.
  
  Random Fourier features is a widely used, simple, and effective technique for scaling up kernel methods. The existing theoretical analysis of the approach, however, remains focused on specific learning tasks and typically gives pessimistic bounds which are at odds with the empirical results. We tackle these problems and provide the first unified risk analysis of learning with random Fourier features using the squared error and Lipschitz continuous loss functions. In our bounds, the trade-off between the computational cost and the learning risk convergence rate is problem specific and expressed in terms of the regularization parameter and the number of effective degrees of freedom. We study both the standard random Fourier features method for which we improve the existing bounds on the number of features required to guarantee the corresponding minimax risk convergence rate of kernel ridge regression, as well as a data-dependent modification which samples features proportional to ridge leverage scores and further reduces the required number of features. As ridge leverage scores are expensive to compute, we devise a simple approximation scheme which provably reduces the computational cost without loss of statistical efficiency. Our empirical results illustrate the effectiveness of the proposed scheme relative to the standard random Fourier features method.

  @article{LiTonOglSej2021,
    author = {Li, Zhu and Ton, Jean-Francois and Oglic, Dino and Sejdinovic, Dino},
    title = {{{Towards A Unified Analysis of Random Fourier Features}}},
    journal = {Journal of Machine Learning Research (JMLR)},
    volume = {22},
    number = {108},
    year = {2021},
    pages = {1--51}
  }
  

• X. Pu , S. L. Chau , X. Dong , D. Sejdinovic , Kernel-based Graph Learning from Smooth Signals: A Functional Viewpoint, IEEE Transactions on Signal and Information Processing over Networks, vol. 7, 192–207, 2021.
  
  The problem of graph learning concerns the construction of an explicit topological structure revealing the relationship between nodes representing data entities, which plays an increasingly important role in the success of many graph-based representations and algorithms in the field of machine learning and graph signal processing. In this paper, we propose a novel graph learning framework that incorporates the node-side and observation-side information, and in particular the covariates that help to explain the dependency structures in graph signals. To this end, we consider graph signals as functions in the reproducing kernel Hilbert space associated with a Kronecker product kernel, and integrate functional learning with smoothness-promoting graph learning to learn a graph representing the relationship between nodes. The functional learning increases the robustness of graph learning against missing and incomplete information in the graph signals. In addition, we develop a novel graph-based regularisation method which, when combined with the Kronecker product kernel, enables our model to capture both the dependency explained by the graph and the dependency due to graph signals observed under different but related circumstances, e.g. different points in time. The latter means the graph signals are free from the i.i.d. assumptions required by the classical graph learning models. Experiments on both synthetic and real-world data show that our methods outperform the state-of-the-art models in learning a meaningful graph topology from graph signals, in particular under heavy noise, missing values, and multiple dependency.

  @article{PuChaDonSej2021,
    author = {Pu, Xingyue and Chau, Siu Lun and Dong, Xiaowen and Sejdinovic, Dino},
    title = {{{Kernel-based Graph Learning from Smooth Signals: A Functional Viewpoint}}},
    journal = {IEEE Transactions on Signal and Information Processing over Networks},
    doi = {10.1109/TSIPN.2021.3059995},
    volume = {7},
    pages = {192--207},
    year = {2021}
  }
  

• D. Rindt , D. Sejdinovic , D. Steinsaltz , Consistency of permutation tests of independence using distance covariance, HSIC and dHSIC, Stat, vol. 10, no. 1, e364, 2021.
  
  The Hilbert–Schmidt independence criterion (HSIC) and its d-variable extension dHSIC are measures of (joint) dependence between random variables. While combining these statistics with a permutation test has become a popular method of testing the null hypothesis of (joint) independence, it had thus far not been proved that this results in a consistent test. In this work, we provide a simple proof that the permutation test with the test statistic HSIC or dHSIC is indeed consistent when using characteristic kernels. That is, we prove that under each alternative hypothesis, the power of these permutation tests indeed converges to 1 as the sample size converges to infinity. Since the test is consistent for each number of permutations, we further give a brief discussion of how the number of permutations relates to the power of the test and how the number of permutations may be selected in practice.

  @article{RinSejSte2021,
    author = {Rindt, David and Sejdinovic, Dino and Steinsaltz, David},
    title = {{{Consistency of permutation tests of independence using distance covariance, HSIC and dHSIC}}},
    journal = {Stat},
    doi = {10.1002/sta4.364},
    volume = {10},
    number = {1},
    pages = {e364},
    year = {2021}
  }
  

• J. Ton , L. Chan , Y. W. Teh , D. Sejdinovic , Noise Contrastive Meta Learning for Conditional Density Estimation using Kernel Mean Embeddings, in Artificial Intelligence and Statistics (AISTATS), 2021, PMLR 130:1099–1107.
  
  Current meta-learning approaches focus on learning functional representations of relationships between variables, i.e. estimating conditional expectations in regression. In many applications, however, the conditional distributions cannot be meaningfully summarized solely by expectation (due to e.g. multimodality). We introduce a novel technique for meta-learning conditional densities, which combines neural representation and noise contrastive estimation together with well-established literature in conditional mean embeddings into reproducing kernel Hilbert spaces. The method shows significant improvements over standard density estimation methods on synthetic and real-world data, by leveraging shared representations across multiple conditional density estimation tasks.

  @inproceedings{TonChaTehSej2021,
    author = {Ton, Jean-Francois and Chan, Leung and Teh, Yee Whye and Sejdinovic, Dino},
    title = {{{Noise Contrastive Meta Learning for Conditional Density Estimation using Kernel Mean Embeddings}}},
    pages = {PMLR 130:1099--1107},
    year = {2021},
    booktitle = {Artificial Intelligence and Statistics (AISTATS)}
  }
  

• J. Ton , D. Sejdinovic , K. Fukumizu , Meta Learning for Causal Direction, in Proceedings of the AAAI Conference on Artificial Intelligence, 2021, vol. 35, no. 11, 9897–9905.
  
  The inaccessibility of controlled randomized trials due to inherent constraints in many fields of science has been a fundamental issue in causal inference. In this paper, we focus on distinguishing the cause from effect in the bivariate setting under limited observational data. Based on recent developments in meta learning as well as in causal inference, we introduce a novel generative model that allows distinguishing cause and effect in the small data setting. Using a learnt task variable that contains distributional information of each dataset, we propose an end-to-end algorithm that makes use of similar training datasets at test time. We demonstrate our method on various synthetic as well as real-world data and show that it is able to maintain high accuracy in detecting directions across varying dataset sizes.

  @inproceedings{TonSejFuk2021,
    author = {Ton, Jean-Francois and Sejdinovic, Dino and Fukumizu, Kenji},
    title = {{{Meta Learning for Causal Direction}}},
    booktitle = {Proceedings of the AAAI Conference on Artificial Intelligence},
    volume = {35},
    number = {11},
    pages = {9897--9905},
    year = {2021}
  }
  

• R. Hu , D. Sejdinovic , Robust Deep Interpretable Features for Binary Image Classification, in Proceedings of the Northern Lights Deep Learning Workshop, 2021, vol. 2.
  
  The problem of interpretability for binary image classification is considered through the lens of kernel two-sample tests and generative modeling. A feature extraction framework coined Deep Interpretable Features is developed, which is used in combination with IntroVAE, a generative model capable of high-resolution image synthesis. Experimental results on a variety of datasets, including COVID-19 chest x-rays demonstrate the benefits of combining deep generative models with the ideas from kernel-based hypothesis testing in moving towards more robust interpretable deep generative models.

  @inproceedings{HuSej2021,
    author = {Hu, Robert and Sejdinovic, Dino},
    title = {{{Robust Deep Interpretable Features for Binary Image Classification}}},
    booktitle = {Proceedings of the Northern Lights Deep Learning Workshop},
    year = {2021},
    volume = {2},
    doi = {10.7557/18.5708}
  }
  

• G. S. Blair , R. Bassett , L. Bastin , L. Beevers , M. I. Borrajo , M. Brown , S. L. Dance , A. Dionescu , L. Edwards , M. A. Ferrario , R. Fraser , H. Fraser , S. Gardner , P. Henrys , T. Hey , S. Homann , C. Huijbers , J. Hutchison , P. Jonathan , R. Lamb , S. Laurie , A. Leeson , D. Leslie , M. McMillan , V. Nundloll , O. Oyebamiji , J. Phillipson , V. Pope , R. Prudden , S. Reis , M. Salama , F. Samreen , D. Sejdinovic , W. Simm , R. Street , L. Thornton , R. Towe , J. V. Hey , M. Vieno , J. Waller , J. Watkins , The Role of Digital Technologies in Responding to the Grand Challenges of the Natural Environment: The Windermere Accord, Patterns, vol. 2, no. 1, 100156, 2021.
  
  Summary Digital technology is having a major impact on many areas of society, and there is equal opportunity for impact on science. This is particularly true in the environmental sciences as we seek to understand the complexities of the natural environment under climate change. This perspective presents the outcomes of a summit in this area, a unique cross-disciplinary gathering bringing together environmental scientists, data scientists, computer scientists, social scientists, and representatives of the creative arts. The key output of this workshop is an agreed vision in the form of a framework and associated roadmap, captured in the Windermere Accord. This accord envisions a new kind of environmental science underpinned by unprecedented amounts of data, with technological advances leading to breakthroughs in taming uncertainty and complexity, and also supporting openness, transparency, and reproducibility in science. The perspective also includes a call to build an international community working in this important area.

  @article{Blairetal2021,
    title = {The Role of Digital Technologies in Responding to the Grand Challenges of the Natural Environment: The {W}indermere Accord},
    journal = {Patterns},
    volume = {2},
    number = {1},
    pages = {100156},
    year = {2021},
    issn = {2666-3899},
    doi = {https://doi.org/10.1016/j.patter.2020.100156},
    author = {Blair, Gordon S. and Bassett, Richard and Bastin, Lucy and Beevers, Lindsay and Borrajo, Maribel Isabel and Brown, Mike and Dance, Sarah L. and Dionescu, Ada and Edwards, Liz and Ferrario, Maria Angela and Fraser, Rob and Fraser, Harriet and Gardner, Simon and Henrys, Peter and Hey, Tony and Homann, Stuart and Huijbers, Chantal and Hutchison, James and Jonathan, Phil and Lamb, Rob and Laurie, Sophie and Leeson, Amber and Leslie, David and McMillan, Malcolm and Nundloll, Vatsala and Oyebamiji, Oluwole and Phillipson, Jordan and Pope, Vicky and Prudden, Rachel and Reis, Stefan and Salama, Maria and Samreen, Faiza and Sejdinovic, Dino and Simm, Will and Street, Roger and Thornton, Lauren and Towe, Ross and Hey, Joshua Vande and Vieno, Massimo and Waller, Joanne and Watkins, John},
    keywords = {digital technologies, digital environment, data science, environmental science}
  }
  

• A. Campbell , Y. Shi , T. Rainforth , A. Doucet , Online Variational Filtering and Parameter Learning, in Advances in Neural Information Processing Systems, 2021.
  
  We present a variational method for online state estimation and parameter learning in state-space models (SSMs), a ubiquitous class of latent variable models for sequential data. As per standard batch variational techniques, we use stochastic gradients to simultaneously optimize a lower bound on the log evidence with respect to both model parameters and a variational approximation of the states’ posterior distribution. However, unlike existing approaches, our method is able to operate in an entirely online manner, such that historic observations do not require revisitation after being incorporated and the cost of updates at each time step remains constant, despite the growing dimensionality of the joint posterior distribution of the states. This is achieved by utilizing backward decompositions of this joint posterior distribution and of its variational approximation, combined with Bellman-type recursions for the evidence lower bound and its gradients. We demonstrate the performance of this methodology across several examples, including high-dimensional SSMs and sequential Variational Auto-Encoders.

  @inproceedings{campbell2021online,
    title = {Online Variational Filtering and Parameter Learning},
    author = {Campbell, Andrew and Shi, Yuyang and Rainforth, Tom and Doucet, Arnaud},
    booktitle = {Advances in Neural Information Processing Systems},
    year = {2021}
  }
  

• Y. Shi , R. Cornish , On Multilevel Monte Carlo Unbiased Gradient Estimation for Deep Latent Variable Models, in Proceedings of The 24th International Conference on Artificial Intelligence and Statistics, 2021.
  
  Standard variational schemes for training deep latent variable models rely on biased gradient estimates of the target objective. Techniques based on the Evidence Lower Bound (ELBO), and tighter variants obtained via importance sampling, produce biased gradient estimates of the true log-likelihood. The family of Reweighted Wake-Sleep (RWS) methods further relies on a biased estimator of the inference objective, which biases training of the encoder also. In this work, we show how Multilevel Monte Carlo (MLMC) can provide a natural framework for debiasing these methods with two different estimators. We prove rigorously that this approach yields unbiased gradient estimators with finite variance under reasonable conditions. Furthermore, we investigate methods that can reduce variance and ensure finite variance in practice. Finally, we show empirically that the proposed unbiased estimators outperform IWAE and other debiasing method on a variety of applications at the same expected cost.

  @inproceedings{shi2021multilevel,
    title = {On Multilevel Monte Carlo Unbiased Gradient Estimation for Deep Latent Variable Models},
    author = {Shi, Yuyang and Cornish, Rob},
    booktitle = {Proceedings of The 24th International Conference on Artificial Intelligence and Statistics},
    year = {2021}
  }
  

• M. Willetts , A. Camuto , T. Rainforth , S. Roberts , C. Holmes , Improving VAEs’ Robustness to Adversarial Attack, in International Conference on Learning Representations (ICLR), 2021.
  
  @inproceedings{Willetts2019VAEAdvRobustness,
    archiveprefix = {arXiv},
    arxivid = {1906.00230},
    author = {Willetts, Matthew and Camuto, Alexander and Rainforth, Tom and Roberts, Stephen and Holmes, Chris},
    eprint = {1906.00230},
    booktitle = {International Conference on Learning Representations (ICLR)},
    title = {{Improving VAEs' Robustness to Adversarial Attack}},
    year = {2021}
  }
  

• A. Camuto , M. Willetts , B. Paige , C. Holmes , S. Roberts , Learning Bijective Feature Maps for Linear ICA, in International Conference on Artificial Intelligence and Statistics (AISTATS), 2021.
  

  @inproceedings{Camuto2020BijectivaICA,
    archiveprefix = {arXiv},
    arxivid = {2002.07766v4},
    author = {Camuto, Alexander and Willetts, Matthew and Paige, Brooks and Holmes, Chris and Roberts, Stephen},
    eprint = {2002.07766v4},
    booktitle = {International Conference on Artificial Intelligence and Statistics (AISTATS)},
    title = {{Learning Bijective Feature Maps for Linear ICA}},
    year = {2021}
  }
  

• A. Camuto , M. Willetts , S. Roberts , C. Holmes , T. Rainforth , Towards a Theoretical Understanding of the Robustness of Variational Autoencoders, in International Conference on Artificial Intelligence and Statistics (AISTATS), 2021.
  
  @inproceedings{Camuto2020VAERobustnessTheory,
    archiveprefix = {arXiv},
    arxivid = {2007.07365},
    author = {Camuto, Alexander and Willetts, Matthew and Roberts, Stephen and Holmes, Chris and Rainforth, Tom},
    eprint = {2007.07365},
    booktitle = {International Conference on Artificial Intelligence and Statistics (AISTATS)},
    title = {{Towards a Theoretical Understanding of the Robustness of Variational Autoencoders}},
    year = {2021}
  }
  

• A. Camuto , M. Willetts , Variational Autoencoders: A Harmonic Perspective, in arXiv preprint, 2021.
  

  @inproceedings{Camuto2021harmonic,
    archiveprefix = {arXiv},
    arxivid = {2105.14866},
    author = {Camuto, Alexander and Willetts, Matthew},
    eprint = {2105.14866},
    journal = {arXiv preprint},
    title = {{Variational Autoencoders: A Harmonic Perspective}},
    year = {2021}
  }
  

• B. Barrett , A. Camuto , M. Willetts , T. Rainforth , Certifiably Robust Variational Autoencoders , in arXiv preprint, 2021.
  

  @inproceedings{Barrett2021certifiable,
    archiveprefix = {arXiv},
    arxivid = {2102.07559},
    author = {Barrett, Ben and Camuto, Alexander and Willetts, Matthew and Rainforth, Tom},
    eprint = {2102.07559},
    journal = {arXiv preprint},
    title = {{Certifiably Robust Variational Autoencoders
    }},
    year = {2021}
  }
  

• M. Willetts , B. Paige , I Don’t Need u: Identifiable Non-Linear ICA Without Side Information, in arXiv preprint, 2021.
  

  @inproceedings{Willetts2021idontneedu,
    archiveprefix = {arXiv},
    arxivid = {2106.05238},
    author = {Willetts, Matthew and Paige, Brooks},
    eprint = {2106.05238},
    journal = {arXiv preprint},
    title = {{I Don't Need u: Identifiable Non-Linear ICA Without Side Information}},
    year = {2021}
  }
  

• J. Xu , H. Kim , T. Rainforth , Y. W. Teh , Group Equivariant Subsampling, in Neural Information Processing Systems (NeurIPS), 2021.
  
  Project: tencent-lsml

  Subsampling is used in convolutional neural networks (CNNs) in the form of pooling or strided convolutions, to reduce the spatial dimensions of feature maps and to allow the receptive fields to grow exponentially with depth. However, it is known that such subsampling operations are not translation equivariant, unlike convolutions that are translation equivariant. Here, we first introduce translation equivariant subsampling/upsampling layers that can be used to construct exact translation equivariant CNNs. We then generalise these layers beyond translations to general groups, thus proposing group equivariant subsampling/upsampling. We use these layers to construct group equivariant autoencoders (GAEs) that allow us to learn low-dimensional equivariant representations. We empirically verify on images that the representations are indeed equivariant to input translations and rotations, and thus generalise well to unseen positions and orientations. We further use GAEs in models that learn object-centric representations on multi-object datasets, and show improved data efficiency and decomposition compared to non-equivariant baselines.

  @inproceedings{xu2021group,
    title = {Group Equivariant Subsampling},
    author = {Xu, Jin and Kim, Hyunjik and Rainforth, Tom and Teh, Yee Whye},
    booktitle = {Neural Information Processing Systems (NeurIPS)},
    year = {2021}
  }
  

2020

• S. M. Schmon , G. Deligiannidis , A. Doucet , M. K. Pitt , Large-sample asymptotics of the pseudo-marginal method, Biometrika, Jul. 2020.
  
  @article{Schmon2020b,
    author = {Schmon, Sebastian M and Deligiannidis, George and Doucet, Arnaud and Pitt, Michael K},
    journal = {Biometrika},
    title = {{Large-sample asymptotics of the pseudo-marginal method}},
    year = {2020},
    month = jul,
    doi = {10.1093/biomet/asaa044}
  }
  

• P. Harder , W. Jones , R. Lguensat , S. Bouabid , J. Fulton , D. Quesada-Chacón , A. Marcolongo , S. Stefanović , Y. Rao , P. Manshausen , D. Watson-Parris , NightVision: Generating Nighttime Satellite Imagery from Infra-Red Observations, in NeurIPS Workshop on Tackling Climate Change with Machine Learning, 2020.
  

  @inproceedings{harder2020nightvision,
    title = {NightVision: Generating Nighttime Satellite Imagery from Infra-Red Observations},
    author = {Harder, Paula and Jones, William and Lguensat, Redouane and Bouabid, Shahine and Fulton, James and Quesada-Chacón, Dánell and Marcolongo, Aris and Stefanović, Sofija and Rao, Yuhan and Manshausen, Peter and Watson-Parris, Duncan},
    year = {2020},
    booktitle = {NeurIPS Workshop on Tackling Climate Change with Machine Learning}
  }
  

• S. Bouabid , M. Chernetskiy , M. Rischard , J. Gamper , Predicting Landsat Reflectance with Deep Generative Fusion, in NeurIPS Workshop on Tackling Climate Change with Machine Learning, 2020.
  

  @inproceedings{bouabid2020predicting,
    title = {Predicting Landsat Reflectance with Deep Generative Fusion},
    author = {Bouabid, Shahine and Chernetskiy, Maxim and Rischard, Maxime and Gamper, Jevgenij},
    year = {2020},
    booktitle = {NeurIPS Workshop on Tackling Climate Change with Machine Learning}
  }
  

• F. Faizi , P. Buigues , G. Deligiannidis , E. Rosta , Simulated tempering with irreversible Gibbs sampling techniques, Journal of Chemical Physics, vol. 153, no. 21, 2020.
  

  @article{faizi2020simulated,
    author = {Faizi, F. and Buigues, P.J. and Deligiannidis, G. and Rosta, E.},
    title = {Simulated tempering with irreversible Gibbs sampling techniques},
    journal = {Journal of Chemical Physics},
    year = {2020},
    volume = {153},
    number = {21}
  }
  

• F. Faizi , G. Deligiannidis , E. Rosta , Efficient Irreversible Monte Carlo Samplers, Journal of Chemical Theory and Computation, vol. 16, no. 4, 2124–2138, 2020.
  

  @article{faizi2020efficient,
    author = {Faizi, F. and Deligiannidis, G. and Rosta, E.},
    title = {Efficient Irreversible Monte Carlo Samplers},
    journal = {Journal of Chemical Theory and Computation},
    year = {2020},
    volume = {16},
    number = {4},
    pages = {2124-2138}
  }
  

• S. Schmon , A. Doucet , G. Deligiannidis , Bernoulli race particle filters, in AISTATS 2019 - 22nd International Conference on Artificial Intelligence and Statistics, 2020.
  

  @conference{schmon2019bernoulli,
    author = {Schmon, S.M. and Doucet, A. and Deligiannidis, G.},
    title = {Bernoulli race particle filters},
    journal = {AISTATS 2019 - 22nd International Conference on Artificial Intelligence and Statistics},
    year = {2020}
  }
  

• L. Middleton , G. Deligiannidis , A. Doucet , P. Jacob , Unbiased markov chain monte carlo for intractable target distributions, Electronic Journal of Statistics, vol. 14, no. 2, 2842–2891, 2020.
  

  @article{middleton2020unbiaseda,
    author = {Middleton, L. and Deligiannidis, G. and Doucet, A. and Jacob, P.E.},
    title = {Unbiased markov chain monte carlo for intractable target distributions},
    journal = {Electronic Journal of Statistics},
    year = {2020},
    volume = {14},
    number = {2},
    pages = {2842-2891}
  }
  

• J. Heng , A. Bishop , G. Deligiannidis , A. Doucet , Controlled sequential monte carlo, Annals of Statistics, vol. 48, no. 5, 2904–2929, 2020.
  

  @article{heng2019controlled,
    author = {Heng, J. and Bishop, A.N. and Deligiannidis, G. and Doucet, A.},
    title = {Controlled sequential monte carlo},
    journal = {Annals of Statistics},
    year = {2020},
    volume = {48},
    number = {5},
    pages = {2904-2929}
  }
  

• L. Middleton , G. Deligiannidis , A. Doucet , P. Jacob , Unbiased smoothing using particle independent metropolis-hastings, in AISTATS 2019 - 22nd International Conference on Artificial Intelligence and Statistics, 2020.
  

  @conference{middleton2019unbbiasedb,
    author = {Middleton, L. and Deligiannidis, G. and Doucet, A. and Jacob, P.E.},
    title = {Unbiased smoothing using particle independent metropolis-hastings},
    journal = {AISTATS 2019 - 22nd International Conference on Artificial Intelligence and Statistics},
    year = {2020}
  }
  

• S. Schmon , G. Deligiannidis , A. Doucet , M. Pitt , Large sample asymptotics of the pseudo-marginal method, Biometrika, 2020.
  

  @article{schmon2018large,
    title = {Large sample asymptotics of the pseudo-marginal method},
    author = {Schmon, S.M. and Deligiannidis, G. and Doucet, A. and Pitt, M.K.},
    journal = {Biometrika},
    year = {2020}
  }
  

• G. Deligiannidis , A. Doucet , S. Rubenthaler , Ensemble Rejection Sampling, aarXiv:2001.0988, 2020.
  

  @article{deligiannidis2020ensemble,
    title = {Ensemble Rejection Sampling},
    author = {Deligiannidis, G. and Doucet, A. and Rubenthaler, S.},
    journal = {aarXiv:2001.0988},
    year = {2020}
  }
  

• R. Cornish , A. Caterini , G. Deligiannidis , A. Doucet , Relaxing bijectivity constraints with continuously indexed normalising flows, in ICML, 2020, 2133–2143.
  

  @inproceedings{cornish2020relaxing,
    title = {Relaxing bijectivity constraints with continuously indexed normalising flows},
    author = {Cornish, R. and Caterini, A. and Deligiannidis, G. and Doucet, A.},
    booktitle = {ICML},
    pages = {2133--2143},
    year = {2020},
    organization = {PMLR}
  }
  

• S. Hayou , E. Clerico , B. He , G. Deligiannidis , A. Doucet , J. Rousseau , Stable ResNet, AISTATS 2021, 2020.
  

  @article{hayou2020stable,
    title = {Stable ResNet},
    author = {Hayou, S. and Clerico, E. and He, B. and Deligiannidis, G. and Doucet, A. and Rousseau, J.},
    booktitle = {AISTATS 2021},
    journal = {arXiv:2010.12859},
    year = {2020}
  }
  

• U. Simsekli , O. Sener , G. Deligiannidis , M. Erdogdu , Hausdorff Dimension, Heavy Tails, and Generalization in Neural Networks, NeurIPS (Spotlight), vol. 33, 2020.
  

  @article{simsekli2020hausdorff,
    title = {Hausdorff Dimension, Heavy Tails, and Generalization in Neural Networks},
    author = {Simsekli, U. and Sener, O. and Deligiannidis, G. and Erdogdu, M.A.},
    journal = {NeurIPS (Spotlight)},
    volume = {33},
    year = {2020}
  }
  

• R. J. Evans , Model selection and local geometry, Annals of Statistics, no. 6, 3514–3544, 2020.
  

  @article{evans20,
    author = {Evans, R. J.},
    journal = {Annals of Statistics},
    title = {Model selection and local geometry},
    year = {2020},
    issue = {6},
    pages = {3514--3544},
    archiveprefix = {arXiv},
    eprint = {1801.08364}
  }
  

• Z. Hu , R. J. Evans , Faster algorithms for Markov equivalence, in Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence (UAI-20), 2020, vol. 2020.
  

  @inproceedings{hu20,
    author = {Hu, Z. and Evans, R. J.},
    title = {Faster algorithms for {M}arkov equivalence},
    booktitle = {Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence (UAI-20)},
    volume = {2020},
    year = {2020}
  }
  

• E. Fong , C. Holmes , On the marginal likelihood and cross-validation, Biometrika, vol. 107, no. 2, 489–496, 2020.
  

  @article{Fong2020,
    title = {On the marginal likelihood and cross-validation},
    author = {Fong, Edwin and Holmes, CC},
    journal = {Biometrika},
    volume = {107},
    number = {2},
    pages = {489--496},
    year = {2020},
    publisher = {Oxford University Press}
  }
  

• A. Foster , M. Jankowiak , M. O’Meara , Y. W. Teh , T. Rainforth , A Unified Stochastic Gradient Approach to Designing Bayesian-Optimal Experiments, International Conference on Artificial Intelligence and Statistics (AISTATS), 2020.
  
  Project: tencent-lsml

  We introduce a fully stochastic gradient based approach to Bayesian optimal experimental design (BOED). This is achieved through the use of variational lower bounds on the expected information gain (EIG) of an experiment that can be simultaneously optimized with respect to both the variational and design parameters. This allows the design process to be carried out through a single unified stochastic gradient ascent procedure, in contrast to existing approaches that typically construct an EIG estimator on a pointwise basis, before passing this estimator to a separate optimizer. We show that this, in turn, leads to more efficient BOED schemes and provide a number of a different variational objectives suited to different settings. Furthermore, we show that our gradient-based approaches are able to provide effective design optimization in substantially higher dimensional settings than existing approaches.

  @article{foster2020unified,
    title = {{A Unified Stochastic Gradient Approach to Designing Bayesian-Optimal Experiments}},
    author = {Foster, Adam and Jankowiak, Martin and O'Meara, Matthew and Teh, Yee Whye and Rainforth, Tom},
    journal = {International Conference on Artificial Intelligence and Statistics (AISTATS)},
    year = {2020}
  }
  

• K. Märtens , C. Yau , BasisVAE: Translation-invariant feature-level clustering with Variational Autoencoders, International Conference on Artificial Intelligence and Statistics (AISTATS), 2020.
  
  Variational Autoencoders (VAEs) provide a flexible and scalable framework for non-linear dimensionality reduction. However, in application domains such as genomics where data sets are typically tabular and high-dimensional, a black-box approach to dimensionality reduction does not provide sufficient insights. Common data analysis workflows additionally use clustering techniques to identify groups of similar features. This usually leads to a two-stage process, however, it would be desirable to construct a joint modelling framework for simultaneous dimensionality reduction and clustering of features. In this paper, we propose to achieve this through the BasisVAE: a combination of the VAE and a probabilistic clustering prior, which lets us learn a one-hot basis function representation as part of the decoder network. Furthermore, for scenarios where not all features are aligned, we develop an extension to handle translation-invariant basis functions. We show how a collapsed variational inference scheme leads to scalable and efficient inference for BasisVAE, demonstrated on various toy examples as well as on single-cell gene expression data.

  @article{martens2020basisVAE,
    title = {{BasisVAE}: {Translation}-invariant feature-level clustering with {Variational} {Autoencoders}},
    journal = {International Conference on Artificial Intelligence and Statistics (AISTATS)},
    author = {Märtens, Kaspar and Yau, Christopher},
    year = {2020}
  }
  

• K. Märtens , C. Yau , Neural Decomposition: Functional ANOVA with Variational Autoencoders, International Conference on Artificial Intelligence and Statistics (AISTATS), 2020.
  
  Variational Autoencoders (VAEs) have become a popular approach for dimensionality reduction. However, despite their ability to identify latent low-dimensional structures embedded within high-dimensional data, these latent representations are typically hard to interpret on their own. Due to the black-box nature of VAEs, their utility for healthcare and genomics applications has been limited. In this paper, we focus on characterising the sources of variation in Conditional VAEs. Our goal is to provide a feature-level variance decomposition, i.e. to decompose variation in the data by separating out the marginal additive effects of latent variables z and fixed inputs c from their non-linear interactions. We propose to achieve this through what we call Neural Decomposition – an adaptation of the well-known concept of functional ANOVA variance decomposition from classical statistics to deep learning models. We show how identifiability can be achieved by training models subject to constraints on the marginal properties of the decoder networks. We demonstrate the utility of our Neural Decomposition on a series of synthetic examples as well as high-dimensional genomics data.

  @article{martens2020neural,
    title = {Neural {Decomposition}: {Functional} {ANOVA} with {Variational} {Autoencoders}},
    journal = {International Conference on Artificial Intelligence and Statistics (AISTATS)},
    author = {Märtens, Kaspar and Yau, Christopher},
    year = {2020}
  }
  

• E. Mathieu , M. Nickel , Riemannian Continuous Normalizing Flows, in Advances in Neural Information Processing Systems 33, 2020.
  
  Normalizing flows have shown great promise for modelling flexible probability distributions in a computationally tractable way. However, whilst data is often naturally described on Riemannian manifolds such as spheres, torii, and hyperbolic spaces, most normalizing flows implicitly assume a flat geometry, making them either misspecified or ill-suited in these situations. To overcome this problem, we introduce Riemannian continuous normalizing flows, a model which admits the parametrization of flexible probability measures on smooth manifolds by defining flows as the solution to ordinary differential equations. We show that this approach can lead to substantial improvements on both synthetic and real-world data when compared to standard flows or previously introduced projected flows.

  @inproceedings{mathieu2019Riemannian,
    title = {Riemannian Continuous Normalizing Flows},
    author = {Mathieu, Emile and Nickel, Maximilian},
    booktitle = {Advances in Neural Information Processing Systems 33},
    year = {2020},
    publisher = {Curran Associates, Inc.}
  }
  

• H. Xing , G. K. Nicholls , J. E. Lee , Distortion estimates for approximate Bayesian inference, in Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence (UAI), 2020, vol. 124, 1208–1217.
  

  @inproceedings{Xing20UAI,
    author = {Xing, H. and Nicholls, G. K. and Lee, J. E.},
    title = {Distortion estimates for approximate {B}ayesian inference},
    booktitle = {Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence (UAI)},
    pages = {1208-1217},
    year = {2020},
    volume = {124},
    publisher = {PMLR}
  }
  

• C. Carmona , G. K. Nicholls , Semi-Modular Inference: enhanced learning in multi-modular models by tempering the influence of components, in Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics, AISTATS, 2020, vol. 108, 4226–4235.
  

  @inproceedings{carmona2020aistats,
    author = {Carmona, C.U. and Nicholls, Geoff K.},
    title = {{S}emi-{M}odular {I}nference: enhanced learning in multi-modular models by tempering the influence of components},
    booktitle = {Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics, {AISTATS}},
    volume = {108},
    pages = {4226-4235},
    year = {2020},
    publisher = {PMLR}
  }
  

• M. Roeling , G. K. Nicholls , Imputation of attributes in networked data using Bayesian Autocorrelation Regression Models, Social Networks, vol. 62, 24–32, 2020.
  

  @article{roeling20,
    title = {Imputation of attributes in networked data using {B}ayesian Autocorrelation Regression Models},
    journal = {Social Networks},
    volume = {62},
    pages = {24-32},
    year = {2020},
    author = {Roeling, M.P. and Nicholls, G. K.}
  }
  

• M. Moores , G. K. Nicholls , A. Pettitt , K. Mengersen , Scalable Bayesian inference for the inverse temperature of a hidden Potts model, Bayesian Analysis, vol. 15, 1–27, 2020.
  

  @article{moores2018scalable,
    title = {Scalable Bayesian inference for the inverse temperature of a hidden Potts model},
    author = {Moores, M. and Nicholls, G. K. and Pettitt, A. and Mengersen, K.},
    journal = {Bayesian Analysis},
    volume = {15},
    pages = {1-27},
    year = {2020},
    publisher = {International Society for Bayesian Analysis}
  }
  

• D. Richards , P. Rebeschini , Graph-Dependent Implicit Regularisation for Distributed Stochastic Subgradient Descent, Journal of Machine Learning Research, vol. 21, no. 34, 1–44, 2020.
  
  @article{JMLR:v21:18-638,
    author = {Richards, Dominic and Rebeschini, Patrick},
    title = {Graph-Dependent Implicit Regularisation for Distributed Stochastic Subgradient Descent},
    journal = {Journal of Machine Learning Research},
    year = {2020},
    volume = {21},
    number = {34},
    pages = {1-44}
  }
  

• D. Richards , P. Rebeschini , L. Rosasco , Decentralised Learning with Random Features and Distributed Gradient Descent, in Proceedings of the 37th International Conference on Machine Learning, 2020, vol. 119, 8105–8115.
  
  @inproceedings{richards2020decentralised,
    title = {Decentralised Learning with Random Features and Distributed Gradient Descent},
    author = {Richards, Dominic and Rebeschini, Patrick and Rosasco, Lorenzo},
    booktitle = {Proceedings of the 37th International Conference on Machine Learning},
    pages = {8105--8115},
    year = {2020},
    volume = {119},
    series = {Proceedings of Machine Learning Research},
    month = {13--18 Jul},
    publisher = {PMLR}
  }
  

• T. Joy , S. M. Schmon , P. Torr , S. Narayanaswamy , T. Rainforth , Rethinking Semi–Supervised Learning in VAEs, https://arxiv.org/abs/2006.10102, 2020.
  

  @article{Joy2020,
    author = {Joy, Tom and Schmon, Sebastian M and Torr, Philipp and Narayanaswamy, Siddharth and Rainforth, Tom},
    journal = {https://arxiv.org/abs/2006.10102},
    title = {Rethinking Semi–Supervised Learning in VAEs},
    year = {2020}
  }
  

• S. Groha , S. M. Schmon , A. Gusev , Neural ODEs for Multi-state Survival Analysis, https://arxiv.org/abs/2006.04893, 2020.
  

  @article{Groha2020,
    author = {Groha, Stefan and Schmon, Sebastian M and Gusev, Alexander},
    title = {Neural ODEs for Multi-state Survival Analysis},
    journal = {https://arxiv.org/abs/2006.04893},
    year = {2020}
  }
  

• S. M. Schmon , P. W. Cannon , J. Knoblauch , Generalized Posteriors in Approximate Bayesian Computation. 2020.
  

  @misc{schmon2020generalized,
    title = {Generalized Posteriors in Approximate Bayesian Computation},
    author = {Schmon, Sebastian M and Cannon, Patrick W and Knoblauch, Jeremias},
    year = {2020},
    eprint = {2011.08644},
    archiveprefix = {arXiv},
    primaryclass = {stat.ME}
  }
  

• D. Rindt , D. Sejdinovic , D. Steinsaltz , A kernel and optimal transport based test of independence between covariates and right-censored lifetimes, International Journal of Biostatistics, 2020.
  
  We propose a nonparametric test of independence, termed optHSIC, between a covariate and a right-censored lifetime. Because the presence of censoring creates a challenge in applying the standard permutation-based testing approaches, we use optimal transport to transform the censored dataset into an uncensored one, while preserving the relevant dependencies. We then apply a permutation test using the kernel-based dependence measure as a statistic to the transformed dataset. The type 1 error is proven to be correct in the case where censoring is independent of the covariate. Experiments indicate that optHSIC has power against a much wider class of alternatives than Cox proportional hazards regression and that it has the correct type 1 control even in the challenging cases where censoring strongly depends on the covariate.

  @article{RinSejSte2020,
    author = {Rindt, David and Sejdinovic, Dino and Steinsaltz, David},
    title = {A kernel and optimal transport based test of independence between covariates and right-censored lifetimes},
    journal = {International Journal of Biostatistics},
    year = {2020},
    doi = {10.1515/ijb-2020-0022}
  }
  

• N. M. Esbroeck , D. T. Lennon , H. Moon , V. Nguyen , F. Vigneau , L. C. Camenzind , L. Yu , D. Zumbuehl , G. A. D. Briggs , D. Sejdinovic , N. Ares , Quantum device fine-tuning using unsupervised embedding learning, New Journal of Physics, vol. 22, no. 9, 095003, 2020.
  
  Quantum devices with a large number of gate electrodes allow for precise control of device parameters. This capability is hard to fully exploit due to the complex dependence of these parameters on applied gate voltages. We experimentally demonstrate an algorithm capable of fine-tuning several device parameters at once. The algorithm acquires a measurement and assigns it a score using a variational auto-encoder. Gate voltage settings are set to optimise this score in real-time in an unsupervised fashion. We report fine-tuning times of a double quantum dot device within approximately 40 min.

  @article{Esbroecketal2020,
    author = {van Esbroeck, Nina M. and Lennon, Dominic T. and Moon, Hyungil and Nguyen, Vu and Vigneau, Florian and Camenzind, Leon C. and Yu, Liuqi and Zumbuehl, Dominik and Briggs, G. Andrew D. and Sejdinovic, Dino and Ares, Natalia},
    title = {Quantum device fine-tuning using unsupervised embedding learning},
    journal = {New Journal of Physics},
    year = {2020},
    doi = {10.1088/1367-2630/abb64c},
    volume = {22},
    number = {9},
    pages = {095003}
  }
  

• H. Moon , D. T. Lennon , J. Kirkpatrick , N. M. Esbroeck , L. C. Camenzind , L. Yu , F. Vigneau , D. M. Zumbühl , G. A. D. Briggs , M. A. Osborne , D. Sejdinovic , E. A. Laird , N. Ares , Machine learning enables completely automatic tuning of a quantum device faster than human experts, Nature Communications, vol. 11, no. 4161, 2020.
  
  Variability is a problem for the scalability of semiconductor quantum devices. The parameter space is large, and the operating range is small. Our statistical tuning algorithm searches for specific electron transport features in gate-defined quantum dot devices with a gate voltage space of up to eight dimensions. Starting from the full range of each gate voltage, our machine learning algorithm can tune each device to optimal performance in a median time of under 70 minutes. This performance surpassed our best human benchmark (although both human and machine performance can be improved). The algorithm is approximately 180 times faster than an automated random search of the parameter space, and is suitable for different material systems and device architectures. Our results yield a quantitative measurement of device variability, from one device to another and after thermal cycling. Our machine learning algorithm can be extended to higher dimensions and other technologies.

  @article{Moonetal2020,
    title = {Machine learning enables completely automatic tuning of a quantum device faster than human experts},
    author = {Moon, H. and Lennon, D. T. and Kirkpatrick, J. and van Esbroeck, N. M. and Camenzind, L. C. and Yu, Liuqi and Vigneau, F. and Zumb\"uhl, D. M. and Briggs, G. A. D. and Osborne, M. A and Sejdinovic, D. and Laird, E. A. and Ares, N.},
    journal = {Nature Communications},
    volume = {11},
    number = {4161},
    year = {2020},
    doi = {10.1038/s41467-020-17835-9}
  }
  

• T. Rudner , D. Sejdinovic , Y. Gal , Inter-domain Deep Gaussian Processes, in International Conference on Machine Learning (ICML), 2020, PMLR 119:8286–8294.
  
  Inter-domain Gaussian processes (GPs) allow for high flexibility and low computational cost when performing approximate inference in GP models. They are particularly suitable for modeling data exhibiting global function behavior but are limited to stationary covariance functions and thus fail to model non-stationary data effectively. We propose Inter-domain Deep Gaussian Processes with RKHS Fourier Features, an extension of shallow inter-domain GPs that combines the advantages of inter-domain and deep Gaussian processes (DGPs) and demonstrate how to leverage existing approximate inference approaches to perform simple and scalable approximate inference on Inter-domain Deep Gaussian Processes. We assess the performance of our method on a wide range of prediction problems and demonstrate that it outperforms inter-domain GPs and DGPs on challenging large-scale and high-dimensional real-world datasets exhibiting both global behavior as well as a high-degree of non-stationarity.

  @inproceedings{RudSejGal2020,
    author = {Rudner, T.G.J. and Sejdinovic, D. and Gal, Y.},
    title = {{{Inter-domain Deep Gaussian Processes}}},
    booktitle = {International Conference on Machine Learning (ICML)},
    pages = {PMLR 119:8286--8294},
    year = {2020}
  }
  

• D. Sejdinovic , Discussion of ‘Functional models for time-varying random objects’ by Dubey and Müller, Journal of the Royal Statistical Society: Series B, vol. 82, no. 2, 312–313, 2020.
  
  The discussion focuses on metric covariance, a new association measure between paired random objects in a metric space, developed by Dubey and Müller, and on its relationship with other similar concepts which have previously appeared in the literature, including distance covariance by Székely et al, as well as its generalisations which rely on the formalism of reproducing kernel Hilbert spaces (RKHS).

  @article{Sej2020-discussion,
    author = {Sejdinovic, Dino},
    title = {{{Discussion of `Functional models for time-varying random objects' by Dubey and M\"uller}}},
    journal = {Journal of the Royal Statistical Society: Series B},
    volume = {82},
    number = {2},
    pages = {312--313},
    year = {2020}
  }
  

• J. Amersfoort , L. Smith , Y. W. Teh , Y. Gal , Uncertainty Estimation Using a Single Deep Deterministic Neural Network, International Conference on Machine Learning, 2020.
  

  @article{van2020uncertainty,
    title = {Uncertainty Estimation Using a Single Deep Deterministic Neural Network},
    author = {van Amersfoort, Joost and Smith, Lewis and Teh, Yee Whye and Gal, Yarin},
    booktitle = {International Conference on Machine Learning},
    year = {2020}
  }
  

• M. Willetts , X. Miscouridou , S. Roberts , C. Holmes , Relaxed-Responsibility Hierarchical Discrete VAEs, arXiv preprint, 2020.
  

  @article{Willetts2020HierarchicalDiscrete,
    archiveprefix = {arXiv},
    arxivid = {2007.07307v1},
    author = {Willetts, Matthew and Miscouridou, Xenia and Roberts, Stephen and Holmes, Chris},
    eprint = {2007.07307v1},
    journal = {arXiv preprint},
    title = {{Relaxed-Responsibility Hierarchical Discrete VAEs}},
    year = {2020}
  }
  

• A. Camuto , M. Willetts , U. Şimşekli , S. Roberts , C. Holmes , Explicit Regularisation in Gaussian Noise Injections, in Advances in Neural Information Processing Systems (NeurIPS), 2020.
  
  @inproceedings{Camuto2020ExplicitReg,
    archiveprefix = {arXiv},
    arxivid = {2007.07368},
    author = {Camuto, Alexander and Willetts, Matthew and Şimşekli, Umut and Roberts, Stephen and Holmes, Chris},
    booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
    eprint = {2007.07368},
    title = {{Explicit Regularisation in Gaussian Noise Injections}},
    year = {2020}
  }
  

• M. Willetts , S. Roberts , C. Holmes , Semi-Unsupervised Learning: Clustering and Classifying using Ultra-Sparse Labels, in IEEE Conference on Big Data – Special Session on Machine Learning for Big Data, 2020.
  

  @inproceedings{Willetts2020SemiUnsupervised,
    archiveprefix = {arXiv},
    arxivid = {1901.08560},
    author = {Willetts, Matthew and Roberts, Stephen and Holmes, Chris},
    booktitle = {IEEE Conference on Big Data – Special Session on Machine Learning for Big Data},
    eprint = {1901.08560},
    title = {{Semi-Unsupervised Learning: Clustering and Classifying using Ultra-Sparse Labels}},
    year = {2020}
  }
  

• J. Xu , J. Ton , H. Kim , A. R. Kosiorek , Y. W. Teh , MetaFun: Meta-Learning with Iterative Functional Updates, in International Conference on Machine Learning (ICML), 2020.
  
  Project: tencent-lsml

  We develop a functional encoder-decoder approach to supervised meta-learning, where labeled data is encoded into an infinite-dimensional functional representation rather than a finite-dimensional one. Furthermore, rather than directly producing the representation, we learn a neural update rule resembling functional gradient descent which iteratively improves the representation. The final representation is used to condition the decoder to make predictions on unlabeled data. Our approach is the first to demonstrates the success of encoder-decoder style meta-learning methods like conditional neural processes on large-scale few-shot classification benchmarks such as miniImageNet and tieredImageNet, where it achieves state-of-the-art performance.

  @inproceedings{xu2019metafun,
    title = {MetaFun: Meta-Learning with Iterative Functional Updates},
    author = {Xu, Jin and Ton, Jean-Francois and Kim, Hyunjik and Kosiorek, Adam R and Teh, Yee Whye},
    booktitle = {International Conference on Machine Learning (ICML)},
    year = {2020}
  }
  

• D. Tolpin , Y. Zhou , H. Yang , Stochastically Differentiable Probabilistic Programs, arXiv preprint arXiv:2003.00704, 2020.
  

  @article{tolpin2020stochastically,
    title = {Stochastically Differentiable Probabilistic Programs},
    author = {Tolpin, David and Zhou, Yuan and Yang, Hongseok},
    journal = {arXiv preprint arXiv:2003.00704},
    year = {2020}
  }
  

2019

• E. Dupont , A. Doucet , Y. W. Teh , Augmented Neural ODEs, in Advances in Neural Information Processing Systems 32, H. Wallach, H. Larochelle, A. Beygelzimer, F. d’ Alché-Buc, E. Fox, and R. Garnett, Eds. Curran Associates, Inc., 2019, 3134–3144.
  
  @incollection{NIPS2019_8577,
    title = {Augmented Neural ODEs},
    author = {Dupont, Emilien and Doucet, Arnaud and Teh, Yee Whye},
    booktitle = {Advances in Neural Information Processing Systems 32},
    editor = {Wallach, H. and Larochelle, H. and Beygelzimer, A. and d\textquotesingle Alch\'{e}-Buc, F. and Fox, E. and Garnett, R.},
    pages = {3134--3144},
    year = {2019},
    month = dec,
    publisher = {Curran Associates, Inc.}
  }
  

• P. Rebeschini , S. Tatikonda , Locality in Network Optimization, IEEE Transactions on Control of Network Systems, vol. 6, no. 2, 487–500, Jun. 2019.
  

  @article{8370673,
    author = {{Rebeschini}, P. and {Tatikonda}, S.},
    journal = {IEEE Transactions on Control of Network Systems},
    title = {Locality in Network Optimization},
    year = {2019},
    volume = {6},
    number = {2},
    pages = {487-500},
    keywords = {computational complexity;graph theory;network theory (graphs);optimisation;probability;localization mechanism;general approach;correlation decays;locality;min-cost network flow problem;network optimization;optimal points;analogous notions;bias-variance tradeoff;random variables;probability theory;Optimization;Correlation;Perturbation methods;Sensitivity;Laplace equations;Convergence;Green's function methods;Bias–variance;decay of correlation;Green's function;Laplacian;network flow;sensitivity of optimal points},
    doi = {10.1109/TCNS.2018.2843164},
    issn = {2372-2533},
    month = jun
  }
  

• E. Nalisnick , A. Matsukawa , Y. W. Teh , D. Gorur , B. Lakshminarayanan , Hybrid Models with Deep and Invertible Features, in International Conference on Machine Learning (ICML), 2019.
  
  We propose a neural hybrid model consisting of a linear model defined on a set of features computed by a deep, invertible transformation (i.e. a normalizing flow). An attractive property of our model is that both p(features), the density of the features, and p(targets|features), the predictive distribution, can be computed exactly in a single feed-forward pass. We show that our hybrid model, despite the invertibility constraints, achieves similar accuracy to purely predictive models. Yet the generative component remains a good model of the input features despite the hybrid optimization objective. This offers additional capabilities such as detection of out-of-distribution inputs and enabling semi-supervised learning. The availability of the exact joint density p(targets, features) also allows us to compute many quantities readily, making our hybrid model a useful building block for downstream applications of probabilistic deep learning.

  @inproceedings{pmlr-v97-nalisnick19b,
    title = {Hybrid Models with Deep and Invertible Features},
    author = {Nalisnick, Eric and Matsukawa, Akihiro and Teh, Yee Whye and Gorur, Dilan and Lakshminarayanan, Balaji},
    booktitle = {International Conference on Machine Learning (ICML)},
    year = {2019},
    series = {Proceedings of Machine Learning Research},
    month = jun,
    publisher = {PMLR}
  }
  

• J. Lee , Y. Lee , J. Kim , A. Kosiorek , S. Choi , Y. W. Teh , Set Transformer: A Framework for Attention-based Permutation-Invariant Neural Networks, in International Conference on Machine Learning (ICML), 2019.
  
  Project: bigbayes

  Many machine learning tasks such as multiple instance learning, 3D shape recognition, and few-shot image classification are defined on sets of instances. Since solutions to such problems do not depend on the order of elements of the set, models used to address them should be permutation invariant. We present an attention-based neural network module, the Set Transformer, specifically designed to model interactions among elements in the input set. The model consists of an encoder and a decoder, both of which rely on attention mechanisms. In an effort to reduce computational complexity, we introduce an attention scheme inspired by inducing point methods from sparse Gaussian process literature. It reduces the computation time of self-attention from quadratic to linear in the number of elements in the set. We show that our model is theoretically attractive and we evaluate it on a range of tasks, demonstrating the state-of-the-art performance compared to recent methods for set-structured data.

  @inproceedings{pmlr-v97-lee19d,
    title = {Set Transformer: A Framework for Attention-based Permutation-Invariant Neural Networks},
    author = {Lee, Juho and Lee, Yoonho and Kim, Jungtaek and Kosiorek, Adam and Choi, Seungjin and Teh, Yee Whye},
    booktitle = {International Conference on Machine Learning (ICML)},
    year = {2019},
    series = {Proceedings of Machine Learning Research},
    month = jun,
    publisher = {PMLR}
  }
  

• L. T. Elliott , M. De Iorio , S. Favaro , K. Adhikari , Y. W. Teh , Modeling Population Structure Under Hierarchical Dirichlet Processes, Bayesian Analysis, Jun. 2019.
  
  Project: bigbayes

  We propose a Bayesian nonparametric model to infer population admixture, extending the hierarchical Dirichlet process to allow for correlation between loci due to linkage disequilibrium. Given multilocus genotype data from a sample of individuals, the proposed model allows inferring and classifying individuals as unadmixed or admixed, inferring the number of subpopulations ancestral to an admixed population and the population of origin of chromosomal regions. Our model does not assume any specific mutation process, and can be applied to most of the commonly used genetic markers. We present a Markov chain Monte Carlo (MCMC) algorithm to perform posterior inference from the model and we discuss some methods to summarize the MCMC output for the analysis of population admixture. Finally, we demonstrate the performance of the proposed model in a real application, using genetic data from the ectodysplasin-A receptor (EDAR) gene, which is considered to be ancestry-informative due to well-known variations in allele frequency as well as phenotypic effects across ancestry. The structure analysis of this dataset leads to the identification of a rare haplotype in Europeans. We also conduct a simulated experiment and show that our algorithm outperforms parametric methods.

  @article{elliott2019,
    author = {Elliott, Lloyd T. and De Iorio, Maria and Favaro, Stefano and Adhikari, Kaustubh and Teh, Yee Whye},
    doi = {10.1214/17-BA1093},
    firstavailable = {2018-05-19T02:03:09Z},
    fjournal = {Bayesian Analysis},
    journal = {Bayesian Analysis},
    publisher = {International Society for Bayesian Analysis},
    title = {Modeling Population Structure Under Hierarchical Dirichlet Processes},
    month = jun,
    year = {2019}
  }
  

• S. Webb , T. Rainforth , Y. W. Teh , M. P. Kumar , A Statistical Approach to Assessing Neural Network Robustness, in International Conference on Learning Representations (ICLR), 2019.
  
  Project: bigbayes

  We present a new approach to assessing the robustness of neural networks based on estimating the proportion of inputs for which a property is violated. Specifically, we estimate the probability of the event that the property is violated under an input model. Our approach critically varies from the formal verification framework in that when the property can be violated, it provides an informative notion of how robust the network is, rather than just the conventional assertion that the network is not verifiable. Furthermore, it provides an ability to scale to larger networks than formal verification approaches. Though the framework still provides a formal guarantee of satisfiability whenever it successfully finds one or more violations, these advantages do come at the cost of only providing a statistical estimate of unsatisfiability whenever no violation is found. Key to the practical success of our approach is an adaptation of multi-level splitting, a Monte Carlo approach for estimating the probability of rare events, to our statistical robustness framework. We demonstrate that our approach is able to emulate formal verification procedures on benchmark problems, while scaling to larger networks and providing reliable additional information in the form of accurate estimates of the violation probability.

  @inproceedings{webb2018statistical,
    title = {{A Statistical Approach to Assessing Neural Network Robustness}},
    author = {Webb, Stefan and Rainforth, Tom and Teh, Yee Whye and Kumar, M Pawan},
    booktitle = {International Conference on Learning Representations (ICLR)},
    month = may,
    year = {2019}
  }
  

• H. Kim , A. Mnih , J. Schwarz , M. Garnelo , S. M. A. Eslami , D. Rosenbaum , O. Vinyals , Y. W. Teh , Attentive Neural Processes, in International Conference on Learning Representations (ICLR), 2019.
  
  Neural Processes (NPs) (Garnelo et al., 2018) approach regression by learning to map a context set of observed input-output pairs to a distribution over regression functions. Each function models the distribution of the output given an input, conditioned on the context. NPs have the benefit of fitting observed data efficiently with linear complexity in the number of context input-output pairs, and can learn a wide family of conditional distributions; they learn predictive distributions conditioned on context sets of arbitrary size. Nonetheless, we show that NPs suffer a fundamental drawback of underfitting, giving inaccurate predictions at the inputs of the observed data they condition on. We address this issue by incorporating attention into NPs, allowing each input location to attend to the relevant context points for the prediction. We show that this greatly improves the accuracy of predictions, results in noticeably faster training, and expands the range of functions that can be modelled.

  @inproceedings{KimTeh2019a,
    author = {Kim, H. and Mnih, A. and Schwarz, J. and Garnelo, M. and Eslami, S. M. A. and Rosenbaum, D. and Vinyals, O. and Teh, Y. W.},
    title = {Attentive Neural Processes},
    booktitle = {International Conference on Learning Representations (ICLR)},
    year = {2019},
    month = may
  }
  

• J. Merel , L. Hasenclever , A. Galashov , A. Ahuja , V. Pham , G. Wayne , Y. W. Teh , N. Heess , Neural Probabilistic Motor Primitives for Humanoid Control, in International Conference on Learning Representations (ICLR), 2019.
  
  We focus on the problem of learning a single motor module that can flexibly express a range of behaviors for the control of high-dimensional physically simulated humanoids. To do this, we propose a motor architecture that has the general structure of an inverse model with a latent-variable bottleneck. We show that it is possible to train this model entirely offline to compress thousands of expert policies and learn a motor primitive embedding space. The trained neural probabilistic motor primitive system can perform one-shot imitation of whole-body humanoid behaviors, robustly mimicking unseen trajectories. Additionally, we demonstrate that it is also straightforward to train controllers to reuse the learned motor primitive space to solve tasks, and the resulting movements are relatively naturalistic. To support the training of our model, we compare two approaches for offline policy cloning, including an experience efficient method which we call linear feedback policy cloning. We encourage readers to view a supplementary video (https://youtu.be/CaDEf-QcKwA ) summarizing our results.

  @inproceedings{MerHasGal2019a,
    author = {Merel, Josh and Hasenclever, Leonard and Galashov, Alexandre and Ahuja, Arun and Pham, Vu and Wayne, Greg and Teh, Yee Whye and Heess, Nicolas},
    title = {Neural Probabilistic Motor Primitives for Humanoid Control},
    booktitle = {International Conference on Learning Representations (ICLR)},
    year = {2019},
    month = may
  }
  

• E. Nalisnick , A. Matsukawa , Y. W. Teh , D. Gorur , B. Lakshminarayanan , Do Deep Generative Models Know What They Don’t Know?, in International Conference on Learning Representations (ICLR), 2019.
  
  A neural network deployed in the wild may be asked to make predictions for inputs that were drawn from a different distribution than that of the training data. A plethora of work has demonstrated that it is easy to find or synthesize inputs for which a neural network is highly confident yet wrong. Generative models are generally viewed to be robust to such overconfidence mistakes as modeling the density of the input features can be used to detect novel, out-of-distribution inputs. In this paper we challenge this assumption, focusing our analysis on flow-based generative models in particular since they are trained and evaluated via the exact marginal likelihood. We find that the model density cannot distinguish images of common objects such as dogs, trucks, and horses (i.e. CIFAR-10) from those of house numbers (i.e. SVHN), assigning a higher likelihood to the latter when the model is trained on the former. We find such behavior persists even when we restrict the flow models to constant-volume transformations. These admit some theoretical analysis, and we show that the difference in likelihoods can be explained by the location and variances of the data and the model curvature, which shows that such behavior is more general and not just restricted to the pairs of datasets used in our experiments. Our results suggest caution when using density estimates of deep generative models on out-of-distribution inputs.

  @inproceedings{NalMatTeh2019a,
    author = {Nalisnick, Eric and Matsukawa, Akihiro and Teh, Yee Whye and Gorur, Dilan and Lakshminarayanan, Balaji},
    title = {Do Deep Generative Models Know What They Don't Know?},
    booktitle = {International Conference on Learning Representations (ICLR)},
    year = {2019},
    month = may
  }
  

• A. Galashov , S. M. Jayakumar , L. Hasenclever , D. Tirumala , J. Schwarz , G. Desjardins , W. M. Czarnecki , Y. W. Teh , R. Pascanu , N. Heess , Information asymmetry in KL-regularized RL, in International Conference on Learning Representations (ICLR), 2019.
  
  Many real world tasks exhibit rich structure that is repeated across different parts of the state space or in time. In this work we study the possibility of leveraging such repeated structure to speed up and regularize learning. We start from the KL regularized expected reward objective which introduces an additional component, a default policy. Instead of relying on a fixed default policy, we learn it from data. But crucially, we restrict the amount of information the default policy receives, forcing it to learn reusable behaviors that help the policy learn faster. We formalize this strategy and discuss connections to information bottleneck approaches and to the variational EM algorithm. We present empirical results in both discrete and continuous action domains and demonstrate that, for certain tasks, learning a default policy alongside the policy can significantly speed up and improve learning. Please watch the video demonstrating learned experts and default policies on several continuous control tasks ( https://youtu.be/U2qA3llzus8 ).

  @inproceedings{GalJayHas2019a,
    author = {Galashov, Alexandre and Jayakumar, Siddhant M. and Hasenclever, Leonard and Tirumala, Dhruva and Schwarz, Jonathan and Desjardins, Guillaume and Czarnecki, Wojciech M. and Teh, Yee Whye and Pascanu, Razvan and Heess, Nicolas},
    title = {Information asymmetry in KL-regularized RL},
    booktitle = {International Conference on Learning Representations (ICLR)},
    year = {2019},
    month = may
  }
  

• J. Lee , L. James , S. Choi , F. Caron , A Bayesian model for sparse graphs with flexible degree distributionand overlapping community structure, in Artificial Intelligence and Statistics (AISTATS), 2019.
  
  Project: bigbayes

  @inproceedings{Lee2019,
    author = {Lee, Juho and James, Lancelot and Choi, Seungjin and Caron, Fran\c cois},
    title = {A Bayesian model for sparse graphs with flexible degree distributionand overlapping community structure},
    booktitle = {Artificial Intelligence and Statistics (AISTATS)},
    note = {ArXiv e-prints: 1810.01778},
    year = {2019},
    month = apr
  }
  

• B. Bloem-Reddy , Y. W. Teh , Probabilistic symmetry and invariant neural networks, Jan. 2019.
  
  Project: bigbayes

  @article{Bloem-Reddy:Teh:2019,
    archiveprefix = {arXiv},
    author = {Bloem-Reddy, Benjamin and Teh, Yee Whye},
    eprint = {1901.06082},
    month = jan,
    primaryclass = {stat.ML},
    title = {Probabilistic symmetry and invariant neural networks},
    year = {2019}
  }
  

• F. Ayed , F. Caron , Nonnegative Bayesian nonparametric factor models with completely random measures for community detection, 2019.
  
  @unpublished{Ayed:Caron,
    author = {Ayed, F. and Caron, F.},
    title = {Nonnegative Bayesian nonparametric factor models with completely random measures for community detection},
    year = {2019}
  }
  

• F. Ayed , J. Lee , F. Caron , Beyond the Chinese Restaurant and Pitman-Yor processes: Statistical Models with Double Power-law Behavior, 2019.
  
  @unpublished{Ayed:Lee:Caron,
    author = {Ayed, F. and Lee, J. and Caron, F.},
    title = {Beyond the Chinese Restaurant and Pitman-Yor processes: Statistical Models with Double Power-law Behavior},
    year = {2019}
  }
  

• G. Deligiannidis , A. Bouchard-Côté , A. Doucet , Exponential ergodicity of the bouncy particle sampler, Annals of Statistics, vol. 47, no. 3, 1268–1287, 2019.
  

  @article{deligiannidis2017exponential,
    author = {Deligiannidis, G. and Bouchard-C\^ot\'e, A. and Doucet, A.},
    title = {Exponential ergodicity of the bouncy particle sampler},
    journal = {Annals of Statistics},
    year = {2019},
    volume = {47},
    number = {3},
    pages = {1268-1287}
  }
  

• R. Cornish , P. Vanetti , A. Bouchard-Côté , G. Deligiannidis , A. Doucet , Scalable metropolis-hastings for exact Bayesian inference with large datasets, in 36th International Conference on Machine Learning, ICML 2019, 2019, vol. 2019-June, 2398–2429.
  

  @conference{cornish2019scalable,
    author = {Cornish, R. and Vanetti, P. and Bouchard-Côté, A. and Deligiannidis, G. and Doucet, A.},
    title = {Scalable metropolis-hastings for exact Bayesian inference with large datasets},
    journal = {36th International Conference on Machine Learning, ICML 2019},
    year = {2019},
    volume = {2019-June},
    pages = {2398-2429}
  }
  

• S. Syed , A. Bouchard-Côté , G. Deligiannidis , A. Doucet , Non-reversible parallel tempering: a scalable highly parallel MCMC scheme, Journal of the Royal Statistical Society, Series B (to appear), 2019.
  

  @article{syed2019non,
    title = {Non-reversible parallel tempering: a scalable highly parallel MCMC scheme},
    author = {Syed, Saifuddin and Bouchard-C{\^o}t{\'e}, Alexandre and Deligiannidis, George and Doucet, Arnaud},
    journal = {Journal of the Royal Statistical Society, Series B (to appear)},
    year = {2019}
  }
  

• R. J. Evans , T. Richardson , Smooth, identifiable supermodels of discrete DAG models with latent variables, Bernoulli, vol. 25, no. 2, 848–876, 2019.
  
  @article{evans19,
    author = {Evans, R. J. and Richardson, T.\\~{}S.},
    journal = {Bernoulli},
    volume = {25},
    number = {2},
    pages = {848--876},
    title = {Smooth, identifiable supermodels of discrete {DAG} models with latent variables},
    year = {2019}
  }
  

• E. S. Allman , H. B. Cervantes , R. J. Evans , S. Hoşten , K. Kubjas , D. Lemke , J. A. Rhodes , P. Zwiernik , Maximum likelihood estimation of the latent class model through model boundary decomposition, Algebraic Statistics, vol. 10, no. 1, 51–84, 2019.
  

  @article{allman:19,
    title = {Maximum likelihood estimation of the latent class model through model boundary decomposition},
    author = {Allman, Elizabeth S and Cervantes, Hector Banos and Evans, R. J. and Ho{\c{s}}ten, Serkan and Kubjas, Kaie and Lemke, Daniel and Rhodes, John A and Zwiernik, Piotr},
    journal = {Algebraic Statistics},
    volume = {10},
    number = {1},
    pages = {51--84},
    year = {2019}
  }
  

• S. Flaxman , M. Chirico , P. Pereira , C. Loeffler , Scalable high-resolution forecasting of sparse spatiotemporal events with kernel methods: a winning solution to the NIJ “Real-Time Crime Forecasting Challenge,” Revised and resubmit at Annals of Applied Statistics, 2019.
  
  Project: bigbayes

  We propose a generic spatiotemporal event forecasting method, which we developed for the National Institute of Justice’s (NIJ) Real-Time Crime Forecasting Challenge. Our solution to the challenge is a spatiotemporal forecasting model combining scalable randomized Reproducing Kernel Hilbert Space (RKHS) methods for approximating Gaussian processes with autoregressive smoothing kernels in a regularized supervised learning framework. While the smoothing kernels capture the two main approaches in current use in the field of crime forecasting, kernel density estimation (KDE) and self-exciting point process (SEPP) models, the RKHS component of the model can be understood as an approximation to the popular log-Gaussian Cox Process model. For inference, we discretize the spatiotemporal point pattern and learn a log intensity function using the Poisson likelihood and highly efficient gradient-based optimization methods. Model hyperparameters including quality of RKHS approximation, spatial and temporal kernel lengthscales, number of autoregressive lags, bandwidths for smoothing kernels, as well as cell shape, size, and rotation, were learned using crossvalidation. Resulting predictions significantly exceeded baseline KDE estimates and SEPP models for sparse events.

  @article{flaxman2019scalable,
    title = {Scalable high-resolution forecasting of sparse spatiotemporal events with kernel methods: a winning solution to the NIJ ``Real-Time Crime Forecasting Challenge''},
    author = {Flaxman, Seth and Chirico, Michael and Pereira, Pau and Loeffler, Charles},
    journal = {Revised and resubmit at Annals of Applied Statistics},
    year = {2019}
  }
  

• E. Fong , S. Lyddon , C. Holmes , Scalable Nonparametric Sampling from Multimodal Posteriors with the Posterior Bootstrap, in Proceedings of the 36th International Conference on Machine Learning, 2019, 1952–1962.
  
  @inproceedings{Fong2019,
    title = {Scalable Nonparametric Sampling from Multimodal Posteriors with the Posterior Bootstrap},
    author = {Fong, Edwin and Lyddon, Simon and Holmes, Chris},
    booktitle = {Proceedings of the 36th International Conference on Machine Learning},
    pages = {1952--1962},
    year = {2019},
    publisher = {PMLR}
  }
  

• A. G. Baydin , L. Heinrich , W. Bhimji , B. Gram-Hansen , G. Louppe , L. Shao , K. Cranmer , F. Wood , Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model, Advances in Neural Information Processing Systems, NeurlPS 2019, 2019.
  

  @article{baydin2018efficient,
    title = {Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model},
    author = {Baydin, Atilim Gunes and Heinrich, Lukas and Bhimji, Wahid and Gram-Hansen, Bradley and Louppe, Gilles and Shao, Lei and Cranmer, Kyle and Wood, Frank},
    journal = {Advances in Neural Information Processing Systems, NeurlPS 2019},
    year = {2019}
  }
  

• B. J. Gram-Hansen , P. Helber , I. Varatharajan , F. Azam , A. Coca-Castro , V. Kopackova , P. Bilinski , Mapping Informal Settlements in Developing Countries using Machine Learning and Low Resolution Multi-spectral Data, in Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society, 2019, 361–368.
  

  @inproceedings{gram2019mapping,
    title = {Mapping Informal Settlements in Developing Countries using Machine Learning and Low Resolution Multi-spectral Data},
    author = {Gram-Hansen, Bradley J and Helber, Patrick and Varatharajan, Indhu and Azam, Faiza and Coca-Castro, Alejandro and Kopackova, Veronika and Bilinski, Piotr},
    booktitle = {Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society},
    pages = {361--368},
    year = {2019},
    organization = {ACM}
  }
  

• B. Gram-Hansen , C. S. Witt , T. Rainforth , P. H. Torr , Y. W. Teh , A. G. Baydin , Hijacking Malaria Simulators with Probabilistic Programming, in International Conference on Machine Learning (ICML) AI for Social Good workshop (AI4SG), 2019.
  

  @inproceedings{gram2019hijacking,
    title = {Hijacking Malaria Simulators with Probabilistic Programming},
    author = {Gram-Hansen, Bradley and de Witt, Christian Schr{\"o}der and Rainforth, Tom and Torr, Philip HS and Teh, Yee Whye and Baydin, At{\i}l{\i}m G{\"u}ne{\c{s}}},
    booktitle = {International Conference on Machine Learning (ICML) AI for Social Good workshop (AI4SG)},
    year = {2019}
  }
  

• A. G. Baydin , L. Shao , W. Bhimji , L. Heinrich , L. Meadows , J. Liu , A. Munk , S. Naderiparizi , B. Gram-Hansen , G. Louppe , . others , Etalumis: Bringing Probabilistic Programming to Scientific Simulators at Scale, in Proceedings of the International Conference for High Performance Computing, SC 2019, 2019.
  

  @inproceedings{baydin2019etalumis,
    title = {Etalumis: Bringing Probabilistic Programming to Scientific Simulators at Scale},
    author = {Baydin, At{\i}l{\i}m G{\"u}ne{\c{s}} and Shao, Lei and Bhimji, Wahid and Heinrich, Lukas and Meadows, Lawrence and Liu, Jialin and Munk, Andreas and Naderiparizi, Saeid and Gram-Hansen, Bradley and Louppe, Gilles and others},
    booktitle = {Proceedings of the International Conference for High Performance Computing, SC 2019},
    year = {2019}
  }
  

• A. Blackwell , T. Kohn , M. Erwig , A. G. Baydin , L. Church , J. Geddes , A. Gordon , M. Gorinova , B. Gram-Hansen , N. Lawrence , . others , Usability of Probabilistic Programming Languages, in Psychology of Programming Interest Group 30th Annual Workshop, PPIG 2019, 2019.
  

  @inproceedings{blackwell2019usability,
    title = {Usability of Probabilistic Programming Languages},
    author = {Blackwell, Alan and Kohn, Tobias and Erwig, Martin and Baydin, Atilim Gunes and Church, Luke and Geddes, James and Gordon, Andy and Gorinova, Maria and Gram-Hansen, Bradley and Lawrence, Neil and others},
    booktitle = {Psychology of Programming Interest Group 30th Annual Workshop, PPIG 2019},
    year = {2019}
  }
  

• H. Law , P. Zhao , L. Chan , J. Huang , D. Sejdinovic , Hyperparameter Learning via Distributional Transfer, Advances in Neural Information Processing Systems (NeurIPS), to appear, 2019.
  
  Project: tencent-lsml

  Bayesian optimisation is a popular technique for hyperparameter learning but typically requires initial ’exploration’ even in cases where potentially similar prior tasks have been solved. We propose to transfer information across tasks using kernel embeddings of distributions of training datasets used in those tasks. The resulting method has a faster convergence compared to existing baselines, in some cases requiring only a few evaluations of the target objective.

  @unpublished{LawZhaHuaSej2018,
    author = {Law, H.C.L. and Zhao, P. and Chan, L. and Huang, J. and Sejdinovic, D.},
    title = {{{Hyperparameter Learning via Distributional Transfer}}},
    booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
    pages = {to appear},
    year = {2019}
  }
  

• A. Raj , H. Law , D. Sejdinovic , M. Park , A Differentially Private Kernel Two-Sample Test, in European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD), 2019, to appear.
  
  Project: bigbayes

  Kernel two-sample testing is a useful statistical tool in determining whether data samples arise from different distributions without imposing any parametric assumptions on those distributions. However, raw data samples can expose sensitive information about individuals who participate in scientific studies, which makes the current tests vulnerable to privacy breaches. Hence, we design a new framework for kernel two-sample testing conforming to differential privacy constraints, in order to guarantee the privacy of subjects in the data. Unlike existing differentially private parametric tests that simply add noise to data, kernel-based testing imposes a challenge due to a complex dependence of test statistics on the raw data, as these statistics correspond to estimators of distances between representations of probability measures in Hilbert spaces. Our approach considers finite dimensional approximations to those representations. As a result, a simple chi-squared test is obtained, where a test statistic depends on a mean and covariance of empirical differences between the samples, which we perturb for a privacy guarantee. We investigate the utility of our framework in two realistic settings and conclude that our method requires only a relatively modest increase in sample size to achieve a similar level of power to the non-private tests in both settings.

  @inproceedings{RajLawSejPar2019,
    author = {Raj, A. and Law, H.C.L. and Sejdinovic, D. and Park, M.},
    title = {{{A Differentially Private Kernel Two-Sample Test}}},
    booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD)},
    pages = {to appear},
    year = {2019}
  }
  

• K. Märtens , K. Campbell , C. Yau , Decomposing feature-level variation with Covariate Gaussian Process Latent Variable Models, in Proceedings of the 36th International Conference on Machine Learning (ICML), 2019, vol. 97, 4372–4381.
  
  The interpretation of complex high-dimensional data typically requires the use of dimensionality reduction techniques to extract explanatory low-dimensional representations. However, in many real-world problems these representations may not be sufficient to aid interpretation on their own, and it would be desirable to interpret the model in terms of the original features themselves. Our goal is to characterise how feature-level variation depends on latent low-dimensional representations, external covariates, and non-linear interactions between the two. In this paper, we propose to achieve this through a structured kernel decomposition in a hybrid Gaussian Process model which we call the Covariate Gaussian Process Latent Variable Model (c-GPLVM). We demonstrate the utility of our model on simulated examples and applications in disease progression modelling from high-dimensional gene expression data in the presence of additional phenotypes. In each setting we show how the c-GPLVM can extract low-dimensional structures from high-dimensional data sets whilst allowing a breakdown of feature-level variability that is not present in other commonly used dimensionality reduction approaches.

  @inproceedings{pmlr-v97-martens19a,
    title = {Decomposing feature-level variation with Covariate Gaussian Process Latent Variable Models},
    author = {Märtens, Kaspar and Campbell, Kieran and Yau, Christopher},
    booktitle = {Proceedings of the 36th International Conference on Machine Learning (ICML)},
    pages = {4372--4381},
    year = {2019},
    editor = {Chaudhuri, Kamalika and Salakhutdinov, Ruslan},
    volume = {97},
    series = {Proceedings of Machine Learning Research},
    month = {09--15 Jun},
    publisher = {PMLR}
  }
  

• K. Märtens , M. Titsias , C. Yau , Augmented Ensemble MCMC sampling in Factorial Hidden Markov Models, in International Conference on Artificial Intelligence and Statistics (AISTATS), 2019, vol. 89, 2359–2367.
  
  Bayesian inference for Factorial Hidden Markov Models is challenging due to the exponentially sized latent variable space. Standard Monte Carlo samplers can have difficulties effectively exploring the posterior landscape and are often restricted to exploration around localised regions that depend on initialisation. We introduce a general purpose ensemble Markov Chain Monte Carlo (MCMC) technique to improve on existing poorly mixing samplers. This is achieved by combining parallel tempering and an auxiliary variable scheme to exchange information between the chains in an efficient way. The latter exploits a genetic algorithm within an augmented Gibbs sampler. We compare our technique with various existing samplers in a simulation study as well as in a cancer genomics application, demonstrating the improvements obtained by our augmented ensemble approach.

  @inproceedings{pmlr-v89-martens19a,
    title = {Augmented Ensemble MCMC sampling in Factorial Hidden Markov Models},
    author = {Märtens, Kaspar and Titsias, Michalis and Yau, Christopher},
    booktitle = {International Conference on Artificial Intelligence and Statistics (AISTATS)},
    pages = {2359--2367},
    year = {2019},
    editor = {Chaudhuri, Kamalika and Sugiyama, Masashi},
    volume = {89},
    series = {Proceedings of Machine Learning Research},
    address = {},
    month = {16--18 Apr},
    publisher = {PMLR}
  }
  

• E. Mathieu , C. Le Lan , C. J. Maddison , R. Tomioka , Y. W. Teh , Continuous Hierarchical Representations with Poincaré Variational Auto-Encoders, in Advances in Neural Information Processing Systems 32, 2019, 12565–12576.
  
  The Variational Auto-Encoder (VAE) is a popular method for learning a generative model and embeddings of the data. Many real datasets are hierarchically structured. However, traditional VAEs map data in a Euclidean latent space which cannot efficiently embed tree-like structures. Hyperbolic spaces with negative curvature can. We therefore endow VAEs with a Poincaré ball model of hyperbolic geometry as a latent space and rigorously derive the necessary methods to work with two main Gaussian generalisations on that space. We empirically show better generalisation to unseen data than the Euclidean counterpart, and can qualitatively and quantitatively better recover hierarchical structures.

  @inproceedings{mathieu2019Continuous,
    title = {Continuous Hierarchical Representations with Poincar\'{e} Variational Auto-Encoders},
    author = {Mathieu, Emile and Le Lan, Charline and Maddison, Chris J. and Tomioka, Ryota and Teh, Yee Whye},
    booktitle = {Advances in Neural Information Processing Systems 32},
    pages = {12565--12576},
    year = {2019},
    publisher = {Curran Associates, Inc.}
  }
  

• E. Mathieu , T. Rainforth , N. Siddharth , Y. W. Teh , Disentangling Disentanglement in Variational Autoencoders, in Proceedings of the 36th International Conference on Machine Learning, Long Beach, California, USA, 2019, vol. 97, 4402–4412.
  
  Project: bigbayes

  We develop a generalisation of disentanglement in VAEs—decomposition of the latent representation—characterising it as the fulfilment of two factors: a) the latent encodings of the data having an appropriate level of overlap, and b) the aggregate encoding of the data conforming to a desired structure, represented through the prior. Decomposition permits disentanglement, i.e. explicit independence between latents, as a special case, but also allows for a much richer class of properties to be imposed on the learnt representation, such as sparsity, clustering, independent subspaces, or even intricate hierarchical dependency relationships. We show that the β-VAE varies from the standard VAE predominantly in its control of latent overlap and that for the standard choice of an isotropic Gaussian prior, its objective is invariant to rotations of the latent representation. Viewed from the decomposition perspective, breaking this invariance with simple manipulations of the prior can yield better disentanglement with little or no detriment to reconstructions. We further demonstrate how other choices of prior can assist in producing different decompositions and introduce an alternative training objective that allows the control of both decomposition factors in a principled manner.

  @inproceedings{pmlr-v97-mathieu19a,
    title = {Disentangling Disentanglement in Variational Autoencoders},
    author = {Mathieu, Emile and Rainforth, Tom and Siddharth, N and Teh, Yee Whye},
    booktitle = {Proceedings of the 36th International Conference on Machine Learning},
    pages = {4402--4412},
    year = {2019},
    volume = {97},
    series = {Proceedings of Machine Learning Research},
    address = {Long Beach, California, USA},
    month = {09--15 Jun},
    publisher = {PMLR}
  }
  

• C. Naik , F. Caron , J. Rousseau , Sparse Networks with Core-Periphery Structure, 2019.
  
  @unpublished{naik2019sparse,
    author = {Naik, Cian and Caron, Fran{\c{c}}ois and Rousseau, Judith},
    title = {Sparse Networks with Core-Periphery Structure},
    year = {2019}
  }
  

• D. J. Graham , C. Naik , E. J. McCoy , H. Li , Do speed cameras reduce road traffic collisions?, PLoS one, vol. 14, no. 9, 2019.
  

  @article{graham2019speed,
    title = {Do speed cameras reduce road traffic collisions?},
    author = {Graham, Daniel J and Naik, Cian and McCoy, Emma J and Li, Haojie},
    journal = {PLoS one},
    volume = {14},
    number = {9},
    year = {2019},
    publisher = {Public Library of Science}
  }
  

• T. Cui , C. Fox , G. K. Nicholls , M. O’Sullivan , Using Parallel Markov Chain Monte Carlo to Quantify Uncertainties in Geothermal Reservoir Calibration, International Journal for Uncertainty Quantification, vol. 9, no. 3, 295–310, 2019.
  

  @article{cui2019usingcalibration,
    author = {Cui, T and Fox, C and Nicholls, Geoff K. and O'Sullivan, M.J.},
    journal = {International Journal for Uncertainty Quantification},
    number = {3},
    pages = {295-310},
    title = {Using Parallel Markov Chain Monte Carlo to Quantify Uncertainties in Geothermal Reservoir Calibration},
    volume = {9},
    year = {2019}
  }
  

• J. E. Lee , G. K. Nicholls , R. Ryder , Calibration procedures for approximate Bayesian credible sets, Bayesian Analysis, vol. 14, 1245–1269, 2019.
  

  @article{lee2018calibration,
    title = {Calibration procedures for approximate Bayesian credible sets},
    author = {Lee, J. E. and Nicholls, G. K. and Ryder, R.J.},
    journal = {Bayesian Analysis},
    volume = {14},
    pages = {1245-1269},
    year = {2019},
    publisher = {International Society for Bayesian Analysis}
  }
  

• H. Xing , G. K. Nicholls , J. E. Lee , Calibrated Approximate Bayesian Inference, in Proceedings of the 36th International Conference on Machine Learning, ICML 2019, 9-15 June 2019, Long Beach, California, USA, 2019, 6912–6920.
  

  @inproceedings{XingNL19,
    author = {Xing, H. and Nicholls, G. K. and Lee, J. E.},
    title = {Calibrated Approximate Bayesian Inference},
    booktitle = {Proceedings of the 36th International Conference on Machine Learning,
                   {ICML} 2019, 9-15 June 2019, Long Beach, California, {USA}},
    pages = {6912--6920},
    year = {2019}
  }
  

• E. R. Rodrigues , G. K. Nicholls , M. H. Tarumoto , G. Tzintzun , Using a non-homogeneous Poisson model with spatial anisotropy and change-points to study air pollution data, Environmental and Ecological Statistics, vol. 26, no. 2, 2019.
  
  A non-homogeneous Poisson process is used to study the rate at which a pollutant’s concentration exceeds a given threshold of interest. An anisotropic spatial model is imposed on the parameters of the Poisson intensity function. The main contribution here is to allow the presence of change-points in time since the data may behave differently for different time frames in a given observational period. Additionally, spatial anisotropy is also imposed on the vector of change-points in order to account for the possible correlation between different sites. Estimation of the parameters of the model is performed using Bayesian inference via Markov chain Monte Carlo algorithms, in particular, Gibbs sampling and Metropolis-Hastings. The different versions of the model are applied to ozone data from the monitoring network of Mexico City, Mexico. An analysis of the results obtained is also given.

  @article{Rodrigues2019,
    author = {Rodrigues, E. R. and Nicholls, G. K. and Tarumoto, M. H. and Tzintzun, G.},
    issue = {2},
    journal = {Environmental and Ecological Statistics},
    title = {Using a non-homogeneous Poisson model with spatial anisotropy and change-points to study air pollution data},
    volume = {26},
    year = {2019}
  }
  

• F. Camerlenghi , S. Favaro , Z. Naulet , F. Panero , Optimal disclosure risk assessment, under revision at the Annals of Statistics, 2019.
  
  @unpublished{panero2019disclosure,
    author = {Camerlenghi, Federico and Favaro, Stefano and Naulet, Zacharie and Panero, Francesca},
    pages = {under revision at the Annals of Statistics},
    title = {Optimal disclosure risk assessment},
    year = {2019}
  }
  

• A. Foster , M. Jankowiak , E. Bingham , P. Horsfall , Y. W. Teh , T. Rainforth , N. Goodman , Variational Bayesian Optimal Experimental Design, Advances in Neural Information Processing Systems (NeurIPS, spotlight), 2019.
  
  Project: bigbayes

  Bayesian optimal experimental design (BOED) is a principled framework for making efficient use of limited experimental resources. Unfortunately, its applicability is hampered by the difficulty of obtaining accurate estimates of the expected information gain (EIG) of an experiment. To address this, we introduce several classes of fast EIG estimators by building on ideas from amortized variational inference. We show theoretically and empirically that these estimators can provide significant gains in speed and accuracy over previous approaches. We further demonstrate the practicality of our approach on a number of end-to-end experiments.

  @article{foster2019variational,
    title = {{Variational Bayesian Optimal Experimental Design}},
    author = {Foster, Adam and Jankowiak, Martin and Bingham, Eli and Horsfall, Paul and Teh, Yee Whye and Rainforth, Tom and Goodman, Noah},
    journal = {Advances in Neural Information Processing Systems (NeurIPS, spotlight)},
    year = {2019}
  }
  

• F. Locatello , G. Abbati , T. Rainforth , S. Bauer , B. Schölkopf , O. Bachem , On the Fairness of Disentangled Representations, Advances in Neural Information Processing Systems (NeurIPS, to appear), 2019.
  
  Project: bigbayes

  Recently there has been a significant interest in learning disentangled representations, as they promise increased interpretability, generalization to unseen scenarios and faster learning on downstream tasks. In this paper, we investigate the usefulness of different notions of disentanglement for improving the fairness of downstream prediction tasks based on representations. We consider the setting where the goal is to predict a target variable based on the learned representation of high-dimensional observations (such as images) that depend on both the target variable and an unobserved sensitive variable. We show that in this setting both the optimal and empirical predictions can be unfair, even if the target variable and the sensitive variable are independent. Analyzing more than 12600 trained representations of state-of-the-art disentangled models, we observe that various disentanglement scores are consistently correlated with increased fairness, suggesting that disentanglement may be a useful property to encourage fairness when sensitive variables are not observed.

  @article{locatello2019fairness,
    title = {{On the Fairness of Disentangled Representations}},
    author = {Locatello, Francesco and Abbati, Gabriele and Rainforth, Tom and Bauer, Stefan and Sch{\"o}lkopf, Bernhard and Bachem, Olivier},
    journal = {Advances in Neural Information Processing Systems (NeurIPS, to appear)},
    year = {2019}
  }
  

• A. Golinski , F. Wood , T. Rainforth , Amortized Monte Carlo Integration, International Conference on Machine Learning (ICML, Best Paper honorable mention), 2019.
  
  Project: bigbayes

  Current approaches to amortizing Bayesian inference focus solely on approximating the posterior distribution. Typically, this approximation is, in turn, used to calculate expectations for one or more target functions—a computational pipeline which is inefficient when the target function(s) are known upfront. In this paper, we address this inefficiency by introducing AMCI, a method for amortizing Monte Carlo integration directly. AMCI operates similarly to amortized inference but produces three distinct amortized proposals, each tailored to a different component of the overall expectation calculation. At run-time, samples are produced separately from each amortized proposal, before being combined to an overall estimate of the expectation. We show that while existing approaches are fundamentally limited in the level of accuracy they can achieve, AMCI can theoretically produce arbitrarily small errors for any integrable target function using only a single sample from each proposal at run-time. We further show that it is able to empirically outperform the theoretically optimal self-normalized importance sampler on a number of example problems. Furthermore, AMCI allows not only for amortizing over datasets but also amortizing over target functions.

  @article{golinski2018amci,
    title = {{Amortized Monte Carlo Integration}},
    author = {Golinski, Adam and Wood, Frank and Rainforth, Tom},
    journal = {International Conference on Machine Learning (ICML, Best Paper honorable mention)},
    year = {2019}
  }
  

• Y. Zhou , B. Gram-Hansen , T. Kohn , T. Rainforth , H. Yang , F. Wood , A Low-Level Probabilistic Programming Language for Non-Differentiable Models, International Conference on Artificial Intelligence and Statistics (AISTATS), 2019.
  
  Project: bigbayes

  We develop a new Low-level, First-order Prob- abilistic Programming Language (LF-PPL) suited for models containing a mix of contin- uous, discrete, and/or piecewise-continuous variables. The key success of this language and its compilation scheme, is in its ability to automatically distinguish the discontinuous and continuous parameters in the density func- tion, while further providing runtime checks of when discontinuity boundaries have been crossed. This enables the introduction of new inference engines that are able to exploit gra- dient information, while remaining efficient for models which are not everywhere differen- tiable. We demonstrate this ability by intro- ducing a discontinuous Hamiltonian Monte Carlo (DHMC) inference engine that is able to deliver automated and efficient inference for non-differentiable models. Our system is backed up by a mathematical formalism that ensures that any model expressed in this lan- guage has a density with a sufficiently low measure of discontinuities to maintain the validity of the inference engine.

  @article{zhou2018lfppla,
    title = {{A Low-Level Probabilistic Programming Language
      for Non-Differentiable Models}},
    author = {Zhou, Yuan and Gram-Hansen, Bradley and Kohn, Tobias and Rainforth, Tom and Yang, Hongseok and Wood, Frank},
    year = {2019},
    journal = {International Conference on Artificial Intelligence and Statistics (AISTATS)}
  }
  

• A. Golinski* , M. Lezcano-Casado* , T. Rainforth , Improving Normalizing Flows via Better Orthogonal Parameterizations, ICML Workshop on Invertible Neural Nets and Normalizing Flows, 2019.
  
  Project: bigbayes

  Normalizing flows have to be designed in a manner that permits efficient computation of the determinant of the transformation Jacobian, while ensuring that the transformation remains invertible. Many methods achieve this by using transformations based on matrix decompositions, with decompositions that produce an orthogonal matrix being a popular such approach. However, this introduces the additional difficulty of performing a constrained optimization over the space of orthogonal matrices. Current approaches for achieving this use methods which are prone to numerical instability and which may introduce additional local minima into the optimization landscape. To address this, we introduce two orthogonal matrix parameterizations stemming from Lie group theory—the exponential map and the Cayley map—which alleviate these issues. In particular, they are proven to not introduce new local minima. Focusing on the example architecture of Sylvester normalizing flows (van den Berg et al. (2018)), we show empirically that using our suggested parameterizations lead to significantly improved optimization and, in turn, more effective normalizing flows.

  @article{golinski2019snf,
    title = {{Improving Normalizing Flows via Better Orthogonal Parameterizations}},
    author = {Golinski*, Adam and Lezcano-Casado*, Mario and Rainforth, Tom},
    journal = {ICML Workshop on Invertible Neural Nets and Normalizing Flows},
    year = {2019}
  }
  

• D. Martı́nez-Rubio , V. Kanade , P. Rebeschini , Decentralized Cooperative Stochastic Bandits, in Advances in Neural Information Processing Systems 32, H. Wallach, H. Larochelle, A. Beygelzimer, F. d’ Alché-Buc, E. Fox, and R. Garnett, Eds. Curran Associates, Inc., 2019, 4529–4540.
  
  @incollection{NIPS2019_8702,
    title = {Decentralized Cooperative Stochastic Bandits},
    author = {Mart\'{\i}nez-Rubio, David and Kanade, Varun and Rebeschini, Patrick},
    booktitle = {Advances in Neural Information Processing Systems 32},
    editor = {Wallach, H. and Larochelle, H. and Beygelzimer, A. and d\textquotesingle Alch\'{e}-Buc, F. and Fox, E. and Garnett, R.},
    pages = {4529--4540},
    year = {2019},
    publisher = {Curran Associates, Inc.}
  }
  

• T. Vaskevicius , V. Kanade , P. Rebeschini , Implicit Regularization for Optimal Sparse Recovery, in Advances in Neural Information Processing Systems 32, H. Wallach, H. Larochelle, A. Beygelzimer, F. d’ Alché-Buc, E. Fox, and R. Garnett, Eds. Curran Associates, Inc., 2019, 2972–2983.
  
  @incollection{NIPS2019_8562,
    title = {Implicit Regularization for Optimal Sparse Recovery},
    author = {Vaskevicius, Tomas and Kanade, Varun and Rebeschini, Patrick},
    booktitle = {Advances in Neural Information Processing Systems 32},
    editor = {Wallach, H. and Larochelle, H. and Beygelzimer, A. and d\textquotesingle Alch\'{e}-Buc, F. and Fox, E. and Garnett, R.},
    pages = {2972--2983},
    year = {2019},
    publisher = {Curran Associates, Inc.}
  }
  

• D. Richards , P. Rebeschini , Optimal Statistical Rates for Decentralised Non-Parametric Regression with Linear Speed-Up, in Advances in Neural Information Processing Systems 32, H. Wallach, H. Larochelle, A. Beygelzimer, F. d’ Alché-Buc, E. Fox, and R. Garnett, Eds. Curran Associates, Inc., 2019, 1216–1227.
  
  @incollection{NIPS2019_8405,
    title = {Optimal Statistical Rates for Decentralised Non-Parametric Regression with Linear Speed-Up},
    author = {Richards, Dominic and Rebeschini, Patrick},
    booktitle = {Advances in Neural Information Processing Systems 32},
    editor = {Wallach, H. and Larochelle, H. and Beygelzimer, A. and d\textquotesingle Alch\'{e}-Buc, F. and Fox, E. and Garnett, R.},
    pages = {1216--1227},
    year = {2019},
    publisher = {Curran Associates, Inc.}
  }
  

• P. Rebeschini , S. Tatikonda , A new approach to Laplacian solvers and flow problems, Journal of Machine Learning Research, vol. 20, no. 36, 2019.
  

  @article{rebeschini2019new,
    title = {A new approach to Laplacian solvers and flow problems},
    author = {Rebeschini, Patrick and Tatikonda, Sekhar},
    journal = {Journal of Machine Learning Research},
    volume = {20},
    number = {36},
    year = {2019},
    publisher = {Journal of Machine Learning Research}
  }
  

• M. Fellows , A. Mahajan , T. G. J. Rudner , S. Whiteson , VIREL: A Variational Inference Framework for Reinforcement Learning, in Advances in Neural Information Processing Systems 32, 2019.
  
  @inproceedings{Fellows:etal:2019c,
    author = {Fellows, Matthew and Mahajan, Anuj and Rudner, Tim G. J. and Whiteson, Shimon},
    title = {{VIREL}: {A} {V}ariational {I}nference {F}ramework for {R}einforcement {L}earning},
    journal = {Advances in Neural Information Processing Systems 32},
    year = {2019}
  }
  

• T. G. J. Rudner , M. Rußwurm , J. Fil , R. Pelich , B. Bischke , V. Kopackova , P. Bilinski , Multi³Net: Segmenting Flooded Buildings via Fusion of Multiresolution, Multisensor, and Multitemporal Satellite Imagery, in Proceedings of the Thirty-Three AAAI Conference on Artificial Intelligence, 2019.
  
  @inproceedings{Rudner:etal:2019b,
    author = {Rudner, Tim G. J. and Rußwurm, Marc and Fil, Jakub and Pelich, Ramona and Bischke, Benjamin and Kopackova, Veronika and Bilinski, Piotr},
    title = {{M}ulti³{N}et: {S}egmenting {F}looded {B}uildings via {F}usion of {M}ultiresolution, {M}ultisensor, and {M}ultitemporal {S}atellite {I}magery},
    journal = {Proceedings of the Thirty-Three {AAAI} Conference on Artificial Intelligence},
    year = {2019}
  }
  

• M. Samvelyan , T. Rashid , C. Witt , G. Farquhar , N. Nardelli , T. G. J. Rudner , C. Hung , P. H. S. Torr , J. Foerster , S. Whiteson , The StarCraft Multi-Agent Challenge, in Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems, 2019.
  
  @inproceedings{Samvelyan:etal:2019a,
    author = {Samvelyan, Mikayel and Rashid, Tabish and Schroeder de Witt, Christian and Farquhar, Gregory and Nardelli, Nantas and Rudner, Tim G. J. and Hung, Chia-Man and Torr, Philip H. S. and Foerster, Jakob and Whiteson, Shimon},
    title = {{T}he {S}tar{C}raft {M}ulti-{A}gent {C}hallenge},
    journal = {Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems},
    year = {2019}
  }
  

• S. M. Schmon , G. Deligiannidis , A. Doucet , Bernoulli Race Particle Filters, AISTATS, 2019.
  

  @article{bernrace,
    author = {Schmon, Sebastian M and Deligiannidis, George and Doucet, Arnaud},
    title = {Bernoulli Race Particle Filters},
    journal = {AISTATS},
    year = {2019}
  }
  

• J. K. Fitzsimons , S. M. Schmon , S. J. Roberts , Implicit Priors for Knowledge Sharing in Bayesian Neural Networks, 4th Neurips workshop on Bayesian Deep Learning, 2019.
  

  @article{Fitzsimons2019,
    author = {Fitzsimons, Jack K and Schmon, Sebastian M and Roberts, Stephen J},
    journal = {4th Neurips workshop on Bayesian Deep Learning},
    title = {Implicit Priors for Knowledge Sharing in Bayesian Neural Networks},
    year = {2019}
  }
  

• D. Watson-Parris , S. Sutherland , M. Christensen , A. Caterini , D. Sejdinovic , P. Stier , Detecting Anthropogenic Cloud Perturbations with Deep Learning, in ICML 2019 Workshop on Climate Change: How Can AI Help?, 2019.
  
  One of the most pressing questions in climate science is that of the effect of anthropogenic aerosol on the Earth’s energy balance. Aerosols provide the ‘seeds’ on which cloud droplets form, and changes in the amount of aerosol available to a cloud can change its brightness and other physical properties such as optical thickness and spatial extent. Clouds play a critical role in moderating global temperatures and small perturbations can lead to significant amounts of cooling or warming. Uncertainty in this effect is so large it is not currently known if it is negligible, or provides a large enough cooling to largely negate present-day warming by CO2. This work uses deep convolutional neural networks to look for two particular perturbations in clouds due to anthropogenic aerosol and assess their properties and prevalence, providing valuable insights into their climatic effects.

  @inproceedings{Watetal2019,
    author = {Watson-Parris, Duncan and Sutherland, Sam and Christensen, Matthew and Caterini, Anthony and Sejdinovic, Dino and Stier, Philip},
    title = {{Detecting Anthropogenic Cloud Perturbations with Deep Learning}},
    booktitle = {ICML 2019 Workshop on Climate Change: How Can AI Help?},
    year = {2019}
  }
  

• J. Runge , P. Nowack , M. Kretschmer , S. Flaxman , D. Sejdinovic , Detecting and Quantifying Causal Associations in Large Nonlinear Time Series Datasets, Science Advances, vol. 5, no. 11, 2019.
  
  Identifying causal relationships and quantifying their strength from observational time series data are key problems in disciplines dealing with complex dynamical systems such as the Earth system or the human body. Data-driven causal inference in such systems is challenging since datasets are often high dimensional and nonlinear with limited sample sizes. Here, we introduce a novel method that flexibly combines linear or nonlinear conditional independence tests with a causal discovery algorithm to estimate causal networks from large-scale time series datasets. We validate the method on time series of well-understood physical mechanisms in the climate system and the human heart and using large-scale synthetic datasets mimicking the typical properties of real-world data. The experiments demonstrate that our method outperforms state-of-the-art techniques in detection power, which opens up entirely new possibilities to discover and quantify causal networks from time series across a range of research fields.

  @article{RunNowKreFlaSej2019,
    author = {Runge, Jakob and Nowack, Peer and Kretschmer, Marlene and Flaxman, Seth and Sejdinovic, Dino},
    title = {{{Detecting and Quantifying Causal Associations in Large Nonlinear Time Series Datasets}}},
    journal = {Science Advances},
    volume = {5},
    number = {11},
    doi = {10.1126/sciadv.aau4996},
    year = {2019}
  }
  

• Z. Li , A. Perez-Suay , G. Camps-Valls , D. Sejdinovic , Kernel Dependence Regularizers and Gaussian Processes with Applications to Algorithmic Fairness, ArXiv e-prints:1911.04322, 2019.
  
  Current adoption of machine learning in industrial, societal and economical activities has raised concerns about the fairness, equity and ethics of automated decisions. Predictive models are often developed using biased datasets and thus retain or even exacerbate biases in their decisions and recommendations. Removing the sensitive covariates, such as gender or race, is insufficient to remedy this issue since the biases may be retained due to other related covariates. We present a regularization approach to this problem that trades off predictive accuracy of the learned models (with respect to biased labels) for the fairness in terms of statistical parity, i.e. independence of the decisions from the sensitive covariates. In particular, we consider a general framework of regularized empirical risk minimization over reproducing kernel Hilbert spaces and impose an additional regularizer of dependence between predictors and sensitive covariates using kernel-based measures of dependence, namely the Hilbert-Schmidt Independence Criterion (HSIC) and its normalized version. This approach leads to a closed-form solution in the case of squared loss, i.e. ridge regression. Moreover, we show that the dependence regularizer has an interpretation as modifying the corresponding Gaussian process (GP) prior. As a consequence, a GP model with a prior that encourages fairness to sensitive variables can be derived, allowing principled hyperparameter selection and studying of the relative relevance of covariates under fairness constraints. Experimental results in synthetic examples and in real problems of income and crime prediction illustrate the potential of the approach to improve fairness of automated decisions.

  @unpublished{LiPerCamSej2019,
    author = {Li, Zhu and Perez-Suay, Adrian and Camps-Valls, Gustau and Sejdinovic, Dino},
    title = {{{Kernel Dependence Regularizers and Gaussian Processes with Applications to Algorithmic Fairness}}},
    journal = {ArXiv e-prints:1911.04322},
    year = {2019}
  }
  

• D. Rindt , D. Sejdinovic , D. Steinsaltz , Nonparametric Independence Testing for Right-Censored Data using Optimal Transport, ArXiv e-prints:1906.03866, 2019.
  
  We propose a nonparametric test of independence, termed OPT-HSIC, between a covariate and a right-censored lifetime. Because the presence of censoring creates a challenge in applying the standard permutation-based testing approaches, we use optimal transport to transform the censored dataset into an uncensored one, while preserving the relevant dependencies. We then apply a permutation test using the kernel-based dependence measure as a statistic to the transformed dataset. The type 1 error is proven to be correct in the case where censoring is independent of the covariate. Experiments indicate that OPT-HSIC has power against a much wider class of alternatives than Cox proportional hazards regression and that it has the correct type 1 control even in the challenging cases where censoring strongly depends on the covariate.

  @unpublished{RinSejSte2019,
    author = {Rindt, David and Sejdinovic, Dino and Steinsaltz, David},
    title = {{{Nonparametric Independence Testing for Right-Censored Data using Optimal Transport}}},
    journal = {ArXiv e-prints:1906.03866},
    year = {2019}
  }
  

• J. Ton , L. Chan , Y. W. Teh , D. Sejdinovic , Noise Contrastive Meta-Learning for Conditional Density Estimation using Kernel Mean Embeddings, ArXiv e-prints:1906.02236, 2019.
  
  Project: bigbayes tencent-lsml

  Current meta-learning approaches focus on learning functional representations of relationships between variables, i.e. on estimating conditional expectations in regression. In many applications, however, we are faced with conditional distributions which cannot be meaningfully summarized using expectation only (due to e.g. multimodality). Hence, we consider the problem of conditional density estimation in the meta-learning setting. We introduce a novel technique for meta-learning which combines neural representation and noise-contrastive estimation with the established literature of conditional mean embeddings into reproducing kernel Hilbert spaces. The method is validated on synthetic and real-world problems, demonstrating the utility of sharing learned representations across multiple conditional density estimation tasks.

  @unpublished{TonChaTehSej2019,
    author = {Ton, Jean-Francois and Chan, Lucian and Teh, Yee Whye and Sejdinovic, Dino},
    title = {{{Noise Contrastive Meta-Learning for Conditional Density Estimation using Kernel Mean Embeddings}}},
    journal = {ArXiv e-prints:1906.02236},
    year = {2019}
  }
  

• G. Camps-Valls , D. Sejdinovic , J. Runge , M. Reichstein , A Perspective on Gaussian Processes for Earth Observation, National Science Review, 2019.
  
  @article{CamSejRunRei2019,
    author = {Camps-Valls, G. and Sejdinovic, D. and Runge, J. and Reichstein, M.},
    title = {{{A Perspective on Gaussian Processes for Earth Observation}}},
    journal = {National Science Review},
    doi = {10.1093/nsr/nwz028},
    year = {2019}
  }
  

• Z. Li , J. Ton , D. Oglic , D. Sejdinovic , Towards A Unified Analysis of Random Fourier Features, in International Conference on Machine Learning (ICML), 2019, PMLR 97:3905–3914.
  
  Random Fourier features is a widely used, simple, and effective technique for scaling up kernel methods. The existing theoretical analysis of the approach, however, remains focused on specific learning tasks and typically gives pessimistic bounds which are at odds with the empirical results. We tackle these problems and provide the first unified risk analysis of learning with random Fourier features using the squared error and Lipschitz continuous loss functions. In our bounds, the trade-off between the computational cost and the expected risk convergence rate is problem specific and expressed in terms of the regularization parameter and the \emphnumber of effective degrees of freedom. We study both the standard random Fourier features method for which we improve the existing bounds on the number of features required to guarantee the corresponding minimax risk convergence rate of kernel ridge regression, as well as a data-dependent modification which samples features proportional to \emphridge leverage scores and further reduces the required number of features. As ridge leverage scores are expensive to compute, we devise a simple approximation scheme which provably reduces the computational cost without loss of statistical efficiency.

  @inproceedings{LiTonOglSej2019,
    author = {Li, Z. and Ton, J.-F. and Oglic, D. and Sejdinovic, D.},
    title = {{{Towards A Unified Analysis of Random Fourier Features}}},
    booktitle = {International Conference on Machine Learning (ICML)},
    pages = {PMLR 97:3905-3914},
    year = {2019}
  }
  

• F. Briol , C. Oates , M. Girolami , M. Osborne , D. Sejdinovic , Probabilistic Integration: A Role in Statistical Computation? (with Discussion and Rejoinder), Statistical Science, vol. 34, no. 1, 1–22; rejoinder: 38–42, 2019.
  
  A research frontier has emerged in scientific computation, wherein discretisation error is regarded as a source of epistemic uncertainty that can be modelled. This raises several statistical challenges, including the design of statistical methods that enable the coherent propagation of probabilities through a (possibly deterministic) computational work-flow, in order to assess the impact of discretisation error on the computer output. This paper examines the case for probabilistic numerical methods in routine statistical computation. Our focus is on numerical integration, where a probabilistic integrator is equipped with a full distribution over its output that reflects the fact that the integrand has been discretised. Our main technical contribution is to establish, for the first time, rates of posterior contraction for one such method. Several substantial applications are provided for illustration and critical evaluation, including examples from statistical modelling, computer graphics and a computer model for an oil reservoir.

  @article{BriOatGirOsbSej2019,
    author = {Briol, F.-X. and Oates, C.J. and Girolami, M. and Osborne, M.A. and Sejdinovic, D.},
    title = {{{Probabilistic Integration: A Role in Statistical Computation? (with Discussion and Rejoinder)}}},
    journal = {Statistical Science},
    volume = {34},
    number = {1},
    pages = {1--22; rejoinder: 38--42},
    year = {2019},
    doi = {10.1214/18-STS660}
  }
  

• H. Chai , J. Ton , M. Osborne , R. Garnett , Automated Model Selection with Bayesian Quadrature, in International Conference on Machine Learning (ICML), 2019, PMLR 97:931–940.
  
  We present a novel technique for tailoring Bayesian quadrature (BQ) to model selection. The state-of-the-art for comparing the evidence of multiple models relies on Monte Carlo methods, which converge slowly and are unreliable for computationally expensive models. Previous research has shown that BQ offers sample efficiency superior to Monte Carlo in computing the evidence of an individual model. However, applying BQ directly to model comparison may waste computation producing an overly-accurate estimate for the evidence of a clearly poor model. We propose an automated and efficient algorithm for computing the most-relevant quantity for model selection: the posterior probability of a model. Our technique maximizes the mutual information between this quantity and observations of the models’ likelihoods, yielding efficient acquisition of samples across disparate model spaces when likelihood observations are limited. Our method produces more-accurate model posterior estimates using fewer model likelihood evaluations than standard Bayesian quadrature and Monte Carlo estimators, as we demonstrate on synthetic and real-world examples.

  @inproceedings{chai2019automated,
    author = {Chai, H. and Ton, J.-F. and Osborne, M. and Garnett, R.},
    title = {{{Automated Model Selection with Bayesian Quadrature}}},
    booktitle = {International Conference on Machine Learning (ICML)},
    pages = {PMLR 97:931-940},
    year = {2019}
  }
  

• A. Kirsch , J. Amersfoort , Y. Gal , BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learning, Advances in Neural Information Processing Systems, 2019.
  

  @article{kirsch2019batchbald,
    title = {BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learning},
    author = {Kirsch, Andreas and van Amersfoort, Joost and Gal, Yarin},
    booktitle = {Advances in Neural Information Processing Systems},
    year = {2019}
  }
  

• M. Willetts , S. Roberts , C. Holmes , Disentangling to Cluster: Gaussian Mixture Variational Ladder Autoencoders, in NeurIPS Bayesian Deep Learning Workshop, 2019.
  
  @inproceedings{Willetts2019DisentangledClustering,
    archiveprefix = {arXiv},
    arxivid = {1909.11501},
    author = {Willetts, Matthew and Roberts, Stephen and Holmes, Chris},
    booktitle = {NeurIPS Bayesian Deep Learning Workshop},
    eprint = {1909.11501},
    title = {{Disentangling to Cluster: Gaussian Mixture Variational Ladder Autoencoders}},
    year = {2019}
  }
  

• Y. Zhou , B. Gram-Hansen , T. Kohn , T. Rainforth , H. Yang , F. Wood , LF-PPL: A Low-Level First Order Probabilistic Programming Language for Non-Differentiable Models, in The 22nd International Conference on Artificial Intelligence and Statistics, 2019, 148–157.
  
  Project: bigbayes

  @inproceedings{zhou2019lf,
    title = {LF-PPL: A Low-Level First Order Probabilistic Programming Language for Non-Differentiable Models},
    author = {Zhou, Yuan and Gram-Hansen, Bradley and Kohn, Tobias and Rainforth, Tom and Yang, Hongseok and Wood, Frank},
    booktitle = {The 22nd International Conference on Artificial Intelligence and Statistics},
    pages = {148--157},
    year = {2019}
  }
  

• Y. Zhou , H. Yang , Y. W. Teh , T. Rainforth , Divide, Conquer, and Combine: a New Inference Strategy for Probabilistic Programs with Stochastic Support, International Conference on Machine Learning (ICML, to appear), 2019.
  
  Project: bigbayes

  @article{zhou2019divide,
    title = {Divide, Conquer, and Combine: a New Inference Strategy for Probabilistic Programs with Stochastic Support},
    author = {Zhou, Yuan and Yang, Hongseok and Teh, Yee Whye and Rainforth, Tom},
    journal = {International Conference on Machine Learning (ICML, to appear)},
    year = {2019}
  }
  

2018

• F. Fuchs , O. Groth , A. R. Kosiorek , A. Bewley , M. Wulfmeier , A. Vedaldi , I. Posner , Learning Physics with Neural Stethoscopes, in NeurIPS Workshop on Modeling the Physical World: Learning, Perception, and Control, 2018.
  

  @inproceedings{Fuchs2018learning,
    author = {Fuchs, Fabian and Groth, Olivier and Kosiorek, Adam R. and Bewley, Alex and Wulfmeier, Markus and Vedaldi, Andrea and Posner, Ingmar},
    title = {Learning Physics with Neural Stethoscopes},
    booktitle = {NeurIPS Workshop on Modeling the Physical World: Learning, Perception, and Control},
    year = {2018},
    month = dec
  }
  

• X. Miscouridou , F. Caron , Y. W. Teh , Modelling sparsity, heterogeneity, reciprocity and community structure in temporal interaction data, in Advances in Neural Information Processing Systems (NeurIPS), 2018.
  
  Project: bigbayes

  @inproceedings{HawkesInteractions,
    author = {Miscouridou, Xenia and Caron, Fran\c ois and Teh, Yee Whye},
    title = {Modelling sparsity, heterogeneity, reciprocity and community structure in temporal interaction data},
    booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
    note = {ArXiv e-prints: 1803.06070},
    year = {2018},
    month = dec
  }
  

• A. Golinski , Y. W. Teh , F. Wood , T. Rainforth , Amortized Monte Carlo Integration, in Symposium on Advances in Approximate Bayesian Inference, 2018.
  
  Project: bigbayes

  Current approaches to amortizing Bayesian inference focus solely on approximating the posterior distribution. Typically, this approximation is in turn used to calculate expec- tations for one or more target functions. In this paper, we address the inefficiency of this computational pipeline when the target function(s) are known upfront. To this end, we introduce a method for amortizing Monte Carlo integration. Our approach operates in a similar manner to amortized inference, but tailors the produced amortization arti- facts to maximize the accuracy of the resulting expectation calculation(s). We show that while existing approaches have fundamental limitations in the level of accuracy that can be achieved for a given run time computational budget, our framework can produce arbitrary small errors for a wide range of target functions with O(1) computational cost at run time. Furthermore, our framework allows not only for amortizing over possible datasets, but also over possible target functions.

  @inproceedings{golinski2018amcj,
    title = {{Amortized Monte Carlo Integration}},
    author = {Golinski, Adam and Teh, Yee Whye and Wood, Frank and Rainforth, Tom},
    booktitle = {Symposium on Advances in Approximate Bayesian Inference},
    year = {2018},
    month = dec
  }
  

• J. Mitrovic , D. Sejdinovic , Y. Teh , Causal Inference via Kernel Deviance Measures, in Advances in Neural Information Processing Systems (NeurIPS), 2018.
  
  Project: bigbayes

  Discovering the causal structure among a set of variables is a fundamental problem in many areas of science. In this paper, we propose Kernel Conditional Deviance for Causal Inference (KCDC) a fully nonparametric causal discovery method based on purely observational data. From a novel interpretation of the notion of asymmetry between cause and effect, we derive a corresponding asymmetry measure using the framework of reproducing kernel Hilbert spaces. Based on this, we propose three decision rules for causal discovery. We demonstrate the wide applicability of our method across a range of diverse synthetic datasets. Furthermore, we test our method on real-world time series data and the real-world benchmark dataset Tubingen Cause-Effect Pairs where we outperform existing state-of-the-art methods.

  @inproceedings{MitSejTeh2018,
    author = {Mitrovic, J. and Sejdinovic, D. and Teh, Y.W.},
    title = {{{Causal Inference via Kernel Deviance Measures}}},
    booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
    year = {2018},
    month = dec
  }
  

• J. Chen , J. Zhu , Y. W. Teh , T. Zhang , Stochastic Expectation Maximization with Variance Reduction, in Advances in Neural Information Processing Systems (NeurIPS), 2018, 7978–7988.
  
  Project: bigbayes tencent-lsml

  @inproceedings{NIPS2018_8021,
    author = {Chen, Jianfei and Zhu, Jun and Teh, Yee Whye and Zhang, Tong},
    booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
    editor = {Bengio, S. and Wallach, H. and Larochelle, H. and Grauman, K. and Cesa-Bianchi, N. and Garnett, R.},
    pages = {7978--7988},
    publisher = {Curran Associates, Inc.},
    title = {Stochastic Expectation Maximization with Variance Reduction},
    year = {2018},
    month = dec,
    bdsk-url-1 = {http://papers.nips.cc/paper/8021-stochastic-expectation-maximization-with-variance-reduction.pdf}
  }
  

• B. Bloem-Reddy , A. Foster , E. Mathieu , Y. W. Teh , Sampling and Inference for Beta Neutral-to-the-Left Models of Sparse Networks, in Conference on Uncertainty in Artificial Intelligence, 2018.
  
  Project: bigbayes

  @inproceedings{BloemReddy:etal:2018,
    author = {Bloem-Reddy, Benjamin and Foster, Adam and Mathieu, Emile and Teh, Yee Whye},
    booktitle = {Conference on Uncertainty in Artificial Intelligence},
    title = {Sampling and Inference for Beta Neutral-to-the-Left Models of Sparse Networks},
    month = aug,
    year = {2018}
  }
  

• B. Bloem-Reddy , P. Orbanz , Random-Walk Models of Network Formation and Sequential Monte Carlo Methods for Graphs, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol. 80, no. 5, 871–898, Aug. 2018.
  
  Project: bigbayes

  @article{Bloem-Reddy:Orbanz:2018,
    archiveprefix = {arXiv},
    author = {Bloem-Reddy, Benjamin and Orbanz, Peter},
    eprint = {1612.06404},
    month = aug,
    primaryclass = {stat.ME},
    journal = {Journal of the Royal Statistical Society: Series B (Statistical Methodology)},
    title = {Random-Walk Models of Network Formation and Sequential Monte Carlo Methods for Graphs},
    volume = {80},
    number = {5},
    pages = {871--898},
    year = {2018}
  }
  

• T. Rainforth , A. R. Kosiorek , T. A. Le , C. J. Maddison , M. Igl , F. Wood , Y. W. Teh , Tighter Variational Bounds are Not Necessarily Better, in International Conference on Machine Learning (ICML), 2018.
  
  Project: bigbayes

  We provide theoretical and empirical evidence that using tighter evidence lower bounds (ELBOs) can be detrimental to the process of learning an inference network by reducing the signal-to-noise ratio of the gradient estimator. Our results call into question common implicit assumptions that tighter ELBOs are better variational objectives for simultaneous model learning and inference amortization schemes. Based on our insights, we introduce three new algorithms: the partially importance weighted auto-encoder (PIWAE), the multiply importance weighted auto-encoder (MIWAE), and the combination importance weighted auto-encoder (CIWAE), each of which includes the standard importance weighted auto-encoder (IWAE) as a special case. We show that each can deliver improvements over IWAE, even when performance is measured by the IWAE target itself. Moreover, PIWAE can simultaneously deliver improvements in both the quality of the inference network and generative network, relative to IWAE.

  @inproceedings{rainforth2018tighter,
    title = {Tighter Variational Bounds are Not Necessarily Better},
    author = {Rainforth, Tom and Kosiorek, Adam R. and Le, Tuan Anh and Maddison, Chris J. and Igl, Maximilian and Wood, Frank and Teh, Yee Whye},
    booktitle = {International Conference on Machine Learning (ICML)},
    year = {2018},
    month = jul
  }
  

• X. Miscouridou , A. Perotte , N. Elhadad , R. Ranganath , Deep Survival Analysis: Nonparametrics and Missingness, in pmlr, 2018.
  
  Project: bigbayes

  @inproceedings{DGMforSurvival,
    author = {Miscouridou, Xenia and Perotte, Adler and Elhadad, Noemie and Ranganath, Rajesh},
    title = {Deep Survival Analysis: Nonparametrics and Missingness},
    booktitle = pmlr,
    note = {Proceedings of Machine Learning Research, 85:1-12},
    year = {2018},
    month = jun
  }
  

• M. Battiston , S. Favaro , D. M. Roy , Y. W. Teh , A Characterization of Product-Form Exchangeable Feature Probability Functions, Annals of Applied Probability, vol. 28, Jun. 2018.
  
  Project: bigbayes

  We characterize the class of exchangeable feature allocations assigning probability V_n,k ∏^k_l=1 W_m_lU_n−m_l to a feature allocation of n individuals, displaying k features with counts (m_1,\ldots,m_k) for these features. Each element of this class is parametrized by a countable matrix V and two sequences U and W of non-negative weights. Moreover, a consistency condition is imposed to guarantee that the distribution for feature allocations of n−1 individuals is recovered from that of n individuals, when the last individual is integrated out. In Theorem 1.1, we prove that the only members of this class satisfying the consistency condition are mixtures of the Indian Buffet Process over its mass parameter γ and mixtures of the Beta–Bernoulli model over its dimensionality parameter N. Hence, we provide a characterization of these two models as the only, up to randomization of the parameters, consistent exchangeable feature allocations having the required product form.

  @article{BatFavRoy2016a,
    author = {Battiston, M. and Favaro, S. and Roy, D. M. and Teh, Y. W.},
    journal = {Annals of Applied Probability},
    title = {A Characterization of Product-Form Exchangeable Feature Probability Functions},
    volume = {28},
    year = {2018},
    month = jun,
    bdsk-url-1 = {https://arxiv.org/pdf/1607.02066.pdf}
  }
  

• H. Kim , Y. W. Teh , Scaling up the Automatic Statistician: Scalable Structure Discovery using Gaussian Processes, in Artificial Intelligence and Statistics (AISTATS), 2018.
  
  Project: bigbayes

  Automating statistical modelling is a challenging problem in artificial intelligence. The Automatic Statistician takes a first step in this direction, by employing a kernel search algorithm with Gaussian Processes (GP) to provide interpretable statistical models for regression problems. However this does not scale due to its O(N^3) running time for the model selection. We propose Scalable Kernel Composition (SKC), a scalable kernel search algorithm that extends the Automatic Statistician to bigger data sets. In doing so, we derive a cheap upper bound on the GP marginal likelihood that sandwiches the marginal likelihood with the variational lower bound . We show that the upper bound is significantly tighter than the lower bound and thus useful for model selection.

  @inproceedings{KimTeh18,
    author = {Kim, H. and Teh, Y. W.},
    booktitle = {Artificial Intelligence and Statistics (AISTATS)},
    title = {Scaling up the Automatic Statistician: Scalable Structure Discovery using Gaussian Processes},
    month = apr,
    year = {2018}
  }
  

• R. van den Berg , L. Hasenclever , J. M. Tomczak , M. Welling , Sylvester Normalizing Flows for Variational Inference, Mar-2018.
  
  Variational inference relies on flexible approximate posterior distributions. Normalizing flows provide a general recipe to construct flexible variational posteriors. We introduce Sylvester normalizing flows, which can be seen as a generalization of planar flows. Sylvester normalizing flows remove the well-known single-unit bottleneck from planar flows, making a single transformation much more flexible. We compare the performance of Sylvester normalizing flows against planar flows and inverse autoregressive flows and demonstrate that they compare favorably on several datasets.

  @unpublished{BergHasenclever2018,
    archiveprefix = {arXiv},
    arxivid = {1803.05649},
    author = {{van den Berg}, Rianne and Hasenclever, Leonard and Tomczak, Jakub M. and Welling, Max},
    eprint = {1803.05649},
    month = mar,
    title = {{Sylvester Normalizing Flows for Variational Inference}},
    year = {2018}
  }
  

• Q. Zhang , S. Filippi , A. Gretton , D. Sejdinovic , Large-Scale Kernel Methods for Independence Testing, Statistics and Computing, vol. 28, no. 1, 113–130, Jan. 2018.
  
  Project: bigbayes

  Representations of probability measures in reproducing kernel Hilbert spaces provide a flexible framework for fully nonparametric hypothesis tests of independence, which can capture any type of departure from independence, including nonlinear associations and multivariate interactions. However, these approaches come with an at least quadratic computational cost in the number of observations, which can be prohibitive in many applications. Arguably, it is exactly in such large-scale datasets that capturing any type of dependence is of interest, so striking a favourable tradeoff between computational efficiency and test performance for kernel independence tests would have a direct impact on their applicability in practice. In this contribution, we provide an extensive study of the use of large-scale kernel approximations in the context of independence testing, contrasting block-based, Nystrom and random Fourier feature approaches. Through a variety of synthetic data experiments, it is demonstrated that our novel large scale methods give comparable performance with existing methods whilst using significantly less computation time and memory.

  @article{ZhaFilGreSej2018,
    author = {Zhang, Q. and Filippi, S. and Gretton, A. and Sejdinovic, D.},
    journal = {Statistics and Computing},
    year = {2018},
    month = jan,
    volume = {28},
    number = {1},
    pages = {113--130},
    title = {{Large-Scale Kernel Methods for Independence Testing}}
  }
  

• F. Ayed , M. Battiston , F. Camerlenghi , S. Favaro , Consistent estimation of the missing mass for feature models, 2018.
  
  Project: bigbayes

  @unpublished{Ayed:Battiston:Camerlenghi:Favaro2018b,
    author = {Ayed, F. and Battiston, M. and Camerlenghi, F. and Favaro, S.},
    title = {Consistent estimation of the missing mass for feature models},
    year = {2018}
  }
  

• M. Battiston , S. Favaro , Y. W. Teh , Bayesian nonparametric approaches to sample-size estimation for finding unseen species, 2018.
  
  Project: bigbayes

  @unpublished{Battiston:Favaro:Teh:2018b,
    author = {Battiston, M. and Favaro, S. and Teh, Y. W.},
    title = {Bayesian nonparametric approaches to sample-size estimation for finding unseen species},
    year = {2018}
  }
  

• F. Ayed , M. Battiston , F. Camerlenghi , S. Favaro , On the consistent estimation of the missing mass, 2018.
  
  Project: bigbayes

  @unpublished{Ayed:Battiston:Camerlenghi:Favaro2018a,
    author = {Ayed, F. and Battiston, M. and Camerlenghi, F. and Favaro, S.},
    title = {On the consistent estimation of the missing mass},
    year = {2018}
  }
  

• F. Ayed , M. Battiston , F. Camerlenghi , S. Favaro , On the Good-Turing estimator for feature allocation models, 2018.
  
  Project: bigbayes

  @unpublished{Ayed:Battiston:Camerlenghi:Favaro2018c,
    author = {Ayed, F. and Battiston, M. and Camerlenghi, F. and Favaro, S.},
    title = {On the {Good-Turing} estimator for feature allocation models},
    year = {2018}
  }
  

• B. Bloem-Reddy , Y. W. Teh , Neural network models of exchangeable sequences, NeurIPS Workshop on Bayesian Deep Learning, 2018.
  
  Project: bigbayes

  @article{BloemReddy:Teh:2018aa,
    author = {Bloem-Reddy, Benjamin and Teh, Yee Whye},
    journal = {NeurIPS Workshop on Bayesian Deep Learning},
    title = {Neural network models of exchangeable sequences},
    year = {2018}
  }
  

• A. Caterini , A. Doucet , D. Sejdinovic , Hamiltonian Variational Auto-Encoder, in Advances in Neural Information Processing Systems (NeurIPS), 2018, to appear.
  
  Variational Auto-Encoders (VAEs) have become very popular techniques to perform inference and learning in latent variable models as they allow us to leverage the rich representational power of neural networks to obtain flexible approximations of the posterior of latent variables as well as tight evidence lower bounds (ELBOs). Combined with stochastic variational inference, this provides a methodology scaling to large datasets. However, for this methodology to be practically efficient, it is necessary to obtain low-variance unbiased estimators of the ELBO and its gradients with respect to the parameters of interest. While the use of Markov chain Monte Carlo (MCMC) techniques such as Hamiltonian Monte Carlo (HMC) has been previously suggested to achieve this, the proposed methods require specifying reverse kernels which have a large impact on performance. Additionally, the resulting unbiased estimator of the ELBO for most MCMC kernels is typically not amenable to the reparameterization trick. We show here how to optimally select reverse kernels in this setting and, by building upon Hamiltonian Importance Sampling (HIS), we obtain a scheme that provides low-variance unbiased estimators of the ELBO and its gradients using the reparameterization trick. This allows us to develop a Hamiltonian Variational Auto-Encoder (HVAE). This method can be reinterpreted as a target-informed normalizing flow which, within our context, only requires a few evaluations of the gradient of the sampled likelihood and trivial Jacobian calculations at each iteration.

  @inproceedings{CatDouSej2018,
    author = {Caterini, A.L. and Doucet, A. and Sejdinovic, D.},
    title = {{{Hamiltonian Variational Auto-Encoder}}},
    booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
    pages = {to appear},
    year = {2018}
  }
  

• A. Caterini , D. E. Chang , Deep Neural Networks in a Mathematical Framework. Springer, 2018.
  

  @book{caterini2018deep,
    title = {Deep Neural Networks in a Mathematical Framework},
    author = {Caterini, A. and Chang, D. E.},
    year = {2018},
    publisher = {Springer}
  }
  

• G. Deligiannidis , A. Doucet , M. Pitt , The correlated pseudomarginal method, JRSSB, vol. 80, no. 5, 839–870, 2018.
  

  @article{deligiannidis2015correlated,
    author = {Deligiannidis, G. and Doucet, A. and Pitt, M.K.},
    title = {The correlated pseudomarginal method},
    journal = {JRSSB},
    year = {2018},
    volume = {80},
    number = {5},
    pages = {839-870}
  }
  

• G. Deligiannidis , A. Lee , Which ergodic averages have finite asymptotic variance?, Annals of Applied Probability, vol. 28, no. 4, 2309–2334, 2018.
  

  @article{deligiannidis2016which,
    author = {Deligiannidis, G. and Lee, A.},
    title = {Which ergodic averages have finite asymptotic variance?},
    journal = {Annals of Applied Probability},
    year = {2018},
    volume = {28},
    number = {4},
    pages = {2309-2334}
  }
  

• E. Dupont , S. Suresha , Probabilistic Semantic Inpainting with Pixel Constrained CNNs, arXiv preprint arXiv:1810.03728, 2018.
  

  @article{dupont2018probabilistic,
    title = {Probabilistic Semantic Inpainting with Pixel Constrained CNNs},
    author = {Dupont, Emilien and Suresha, Suhas},
    journal = {arXiv preprint arXiv:1810.03728},
    year = {2018}
  }
  

• E. Dupont , Learning Disentangled Joint Continuous and Discrete Representations, in Advances in Neural Information Processing Systems, 2018.
  
  @inproceedings{dupont2018learning,
    title = {Learning Disentangled Joint Continuous and Discrete Representations},
    author = {Dupont, Emilien},
    booktitle = {Advances in Neural Information Processing Systems},
    year = {2018}
  }
  

• E. Dupont , T. Zhang , P. Tilke , L. Liang , W. Bailey , Generating Realistic Geology Conditioned on Physical Measurements with Generative Adversarial Networks, ICML TADGM Workshop, 2018.
  
  @article{dupont2018generating,
    title = {Generating Realistic Geology Conditioned on Physical Measurements with Generative Adversarial Networks},
    author = {Dupont, Emilien and Zhang, Tuanfeng and Tilke, Peter and Liang, Lin and Bailey, William},
    journal = {ICML TADGM Workshop},
    year = {2018}
  }
  

• R. J. Evans , Margins of discrete Bayesian networks, Annals of Statistics, vol. 46, no. 6A, 2623–2656, 2018.
  

  @article{evans:18,
    author = {Evans, R. J.},
    title = {Margins of discrete {B}ayesian networks},
    journal = {Annals of Statistics},
    volume = {46},
    number = {6A},
    pages = {2623--2656},
    archiveprefix = {arXiv},
    eprint = {1501.02103},
    year = {2018}
  }
  

• I. Shpitser , R. J. Evans , T. S. Richardson , Acyclic Linear SEMs Obey the Nested Markov Property, in Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence (UAI-18), 2018, vol. 2018.
  

  @inproceedings{shpitser:18,
    title = {Acyclic Linear {SEMs} Obey the Nested {M}arkov Property},
    author = {Shpitser, Ilya and Evans, R. J. and Richardson, Thomas S},
    booktitle = {Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence (UAI-18)},
    volume = {2018},
    year = {2018}
  }
  

• C. Loeffler , S. Flaxman , Is gun violence contagious? A spatiotemporal test, Journal of Quantitative Criminology, vol. 34, no. 4, 999–1017, 2018.
  
  Project: bigbayes

  Existing theories of gun violence predict stable spatial concentrations and contagious diffusion of gun violence into surrounding areas. Recent empirical studies have reported confirmatory evidence of such spatiotemporal diffusion of gun violence. However, existing tests cannot readily distinguish spatiotemporal clustering from spatiotemporal diffusion. This leaves as an open question whether gun violence actually is contagious or merely clusters in space and time. Compounding this problem, gun violence is subject to considerable measurement error with many nonfatal shootings going unreported to police. Using point process data from an acoustical gunshot locator system and a combination of Bayesian spatiotemporal point process modeling and space/time interaction tests, this paper demonstrates that contemporary urban gun violence does diffuse, but only slightly, suggesting that a disease model for infectious spread of gun violence is a poor fit for the geographically stable and temporally stochastic process observed.

  @article{loeffler2018gun,
    title = {Is gun violence contagious? A spatiotemporal test},
    author = {Loeffler, Charles and Flaxman, Seth},
    journal = {Journal of Quantitative Criminology},
    volume = {34},
    number = {4},
    pages = {999--1017},
    year = {2018},
    publisher = {Springer}
  }
  

• A. Foster , M. Jankowiak , E. Bingham , Y. W. Teh , T. Rainforth , N. Goodman , Variational Optimal Experiment Design: Efficient Automation of Adaptive Experiments, NeurIPS Workshop on Bayesian Deep Learning, 2018.
  
  Project: bigbayes

  Bayesian optimal experimental design (OED) is a principled framework for making efficient use of limited experimental resources. Unfortunately, the applicability of OED is hampered by the difficulty of obtaining accurate estimates of the expected information gain (EIG) for different experimental designs. We introduce a class of fast EIG estimators that leverage amortised variational inference and show that they provide substantial empirical gains over previous approaches. We integrate our approach into a deep probabilistic programming framework, thus making OED accessible to practitioners at large.

  @article{foster2018voed,
    title = {{Variational Optimal Experiment Design: Efficient Automation of Adaptive Experiments}},
    author = {Foster, Adam and Jankowiak, Martin and Bingham, Eli and Teh, Yee Whye and Rainforth, Tom and Goodman, Noah},
    journal = {NeurIPS Workshop on Bayesian Deep Learning},
    year = {2018}
  }
  

• S. Webb , A. Golinski , R. Zinkov , N. Siddharth , T. Rainforth , Y. W. Teh , F. Wood , Faithful Inversion of Generative Models for Effective Amortized Inference, in Advances in Neural Information Processing Systems (NeurIPS), 2018.
  
  Project: bigbayes

  Inference amortization methods share information across multiple posterior-inference problems, allowing each to be carried out more efficiently. Generally, they require the inversion of the dependency structure in the generative model, as the modeller must learn a mapping from observations to distributions approximating the posterior. Previous approaches have involved inverting the dependency structure in a heuristic way that fails to capture these dependencies correctly, thereby limiting the achievable accuracy of the resulting approximations. We introduce an algorithm for faithfully, and minimally, inverting the graphical model structure of any generative model. Such inverses have two crucial properties: (a) they do not encode any independence assertions that are absent from the model and; (b) they are local maxima for the number of true independencies encoded. We prove the correctness of our approach and empirically show that the resulting minimally faithful inverses lead to better inference amortization than existing heuristic approaches.

  @inproceedings{webb2018minimal,
    title = {Faithful Inversion of Generative Models for Effective Amortized Inference},
    author = {Webb, Stefan and Golinski, Adam and Zinkov, Robert and Siddharth, N. and Rainforth, Tom and Teh, Yee Whye and Wood, Frank},
    booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
    year = {2018}
  }
  

• P. Helber , B. Gram-Hansen , I. Varatharajan , F. Azam , A. Coca-Castro , V. Kopackova , P. Bilinski , Generating Material Maps to Map Informal Settlements, in NeurlPS workshop on Machine Learning for the Developing World (ML4DW), 2018.
  

  @inproceedings{helber2018generating,
    title = {Generating Material Maps to Map Informal Settlements},
    author = {Helber, Patrick and Gram-Hansen, Bradley and Varatharajan, Indhu and Azam, Faiza and Coca-Castro, Alejandro and Kopackova, Veronika and Bilinski, Piotr},
    booktitle = {NeurlPS workshop on Machine Learning for the Developing World (ML4DW)},
    year = {2018}
  }
  

• B. Gram-Hansen , Y. Zhou , T. Kohn , T. Rainforth , H. Yang , F. Wood , Hamiltonian Monte Carlo for Probabilistic Programs with Discontinuities, in International Conference on Probabilistic Programming, 2018.
  

  @inproceedings{gram2018hamiltonian,
    title = {Hamiltonian Monte Carlo for Probabilistic Programs with Discontinuities},
    author = {Gram-Hansen, Bradley and Zhou, Yuan and Kohn, Tobias and Rainforth, Tom and Yang, Hongseok and Wood, Frank},
    booktitle = {International Conference on Probabilistic Programming},
    year = {2018}
  }
  

• A. R. Kosiorek , H. Kim , Y. W. Teh , I. Posner , Sequential Attend, Infer, Repeat: Generative Modelling of Moving Objects, in Advances in Neural Information Processing Systems (NeurIPS), 2018.
  
  Project: bigbayes

  We present Sequential Attend, Infer, Repeat (SQAIR), an interpretable deep generative model for videos of moving objects. It can reliably discover and track objects throughout the sequence of frames, and can also generate future frames conditioning on the current frame, thereby simulating expected motion of objects. This is achieved by explicitly encoding object presence, locations and appearances in the latent variables of the model. SQAIR retains all strengths of its predecessor, Attend, Infer, Repeat (AIR, Eslami et. al., 2016), including learning in an unsupervised manner, and addresses its shortcomings. We use a moving multi-MNIST dataset to show limitations of AIR in detecting overlapping or partially occluded objects, and show how SQAIR overcomes them by leveraging temporal consistency of objects. Finally, we also apply SQAIR to real-world pedestrian CCTV data, where it learns to reliably detect, track and generate walking pedestrians with no supervision.

  @inproceedings{koskimposteh18,
    title = {Sequential Attend, Infer, Repeat: Generative Modelling of Moving Objects},
    author = {Kosiorek, Adam R. and Kim, Hyunjik and Teh, Yee Whye and Posner, Ingmar},
    booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
    year = {2018}
  }
  

• H. Kim , A. Mnih , Disentangling by Factorising, in International Conference on Machine Learning (ICML), 2018.
  
  Project: bigbayes

  We define and address the problem of unsupervised learning of disentangled representations on data generated from independent factors of variation. We propose FactorVAE, a method that disentangles by encouraging the distribution of representations to be factorial and hence independent across the dimensions. We show that it improves upon beta-VAE by providing a better trade-off between disentanglement and reconstruction quality. Moreover, we highlight the problems of a commonly used disentanglement metric and introduce a new metric that does not suffer from them.

  @inproceedings{KimMnih18,
    author = {Kim, H. and Mnih, A.},
    booktitle = {International Conference on Machine Learning (ICML)},
    title = {Disentangling by Factorising},
    year = {2018}
  }
  

• T. A. Le , A. R. Kosiorek , N. Siddharth , Y. W. Teh , F. Wood , Revisiting Reweighted Wake-Sleep, CoRR, vol. abs/1805.10469, 2018.
  

  @article{Le2018RevisitingRW,
    title = {Revisiting Reweighted Wake-Sleep},
    author = {Le, Tuan Anh and Kosiorek, Adam R. and Siddharth, N. and Teh, Yee Whye and Wood, Frank},
    journal = {CoRR},
    year = {2018},
    volume = {abs/1805.10469}
  }
  

• F. B. Fuchs , O. Groth , A. R. Kosiorek , A. Bewley , M. Wulfmeier , A. Vedaldi , I. Posner , Neural Stethoscopes: Unifying Analytic, Auxiliary and Adversarial Network Probing, CoRR, vol. abs/1806.05502, 2018.
  

  @article{Fuchs2018NeuralSU,
    title = {Neural Stethoscopes: Unifying Analytic, Auxiliary and Adversarial Network Probing},
    author = {Fuchs, Fabian B. and Groth, Oliver and Kosiorek, Adam R. and Bewley, Alex and Wulfmeier, Markus and Vedaldi, Andrea and Posner, Ingmar},
    journal = {CoRR},
    year = {2018},
    volume = {abs/1806.05502}
  }
  

• H. Law , D. Sejdinovic , E. Cameron , T. Lucas , S. Flaxman , K. Battle , K. Fukumizu , Variational Learning on Aggregate Outputs with Gaussian Processes, in Advances in Neural Information Processing Systems (NeurIPS), 2018, to appear.
  
  Project: bigbayes

  While a typical supervised learning framework assumes that the inputs and the outputs are measured at the same levels of granularity, many applications, including global mapping of disease, only have access to outputs at a much coarser level than that of the inputs. Aggregation of outputs makes generalization to new inputs much more difficult. We consider an approach to this problem based on variational learning with a model of output aggregation and Gaussian processes, where aggregation leads to intractability of the standard evidence lower bounds. We propose new bounds and tractable approximations, leading to improved prediction accuracy and scalability to large datasets, while explicitly taking uncertainty into account. We develop a framework which extends to several types of likelihoods, including the Poisson model for aggregated count data. We apply our framework to a challenging and important problem, the fine-scale spatial modelling of malaria incidence, with over 1 million observations.

  @inproceedings{LawSejCamLucFlaBatFuk2018,
    author = {Law, H.C.L. and Sejdinovic, D. and Cameron, E. and Lucas, T.C.D. and Flaxman, S. and Battle, K. and Fukumizu, K.},
    title = {{{Variational Learning on Aggregate Outputs with Gaussian Processes}}},
    booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
    pages = {to appear},
    year = {2018}
  }
  

• J. Heo , H. Lee , S. Kim , J. Lee , K. Kim , E. Yang , S. Hwang , Uncertainty-aware attention for reliable interpretation and prediction, in Advances in Neural Information Processing Systems (NeurIPS), 2018.
  
  Project: bigbayes

  @inproceedings{Heo2018,
    title = {Uncertainty-aware attention for reliable interpretation and prediction},
    author = {Heo, J. and Lee, H. and Kim, S. and Lee, J. and Kim, K. and Yang, E. and Hwang, S.},
    booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
    year = {2018}
  }
  

• H. Lee , J. Lee , S. Kim , E. Yang , S. Hwang , DropMax: adaptive variational softmax, in Advances in Neural Information Processing Systems (NeurIPS), 2018.
  
  Project: bigbayes

  @inproceedings{Lee2018,
    title = {DropMax: adaptive variational softmax},
    author = {Lee, H. and Lee, J. and Kim, S. and Yang, E. and Hwang, S.},
    booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
    year = {2018}
  }
  

• E. Pompe , C. Holmes , K. Łatuszyński , A Framework for Adaptive MCMC Targeting Multimodal Distributions, arXiv preprint arXiv:1812.02609, 2018.
  
  @article{pompe2018framework,
    title = {{A Framework for Adaptive MCMC Targeting Multimodal Distributions}},
    author = {Pompe, Emilia and Holmes, Chris and {\L}atuszy{\'n}ski, Krzysztof},
    journal = {arXiv preprint arXiv:1812.02609},
    year = {2018}
  }
  

• T. Rainforth , Y. Zhou , X. Lu , Y. W. Teh , F. Wood , H. Yang , J. Meent , Inference Trees: Adaptive Inference with Exploration, arXiv preprint arXiv:1806.09550, 2018.
  
  Project: bigbayes

  We introduce inference trees (ITs), a new class of inference methods that build on ideas from Monte Carlo tree search to perform adaptive sampling in a manner that balances exploration with exploitation, ensures consistency, and alleviates pathologies in existing adaptive methods. ITs adaptively sample from hierarchical partitions of the parameter space, while simultaneously learning these partitions in an online manner. This enables ITs to not only identify regions of high posterior mass, but also maintain uncertainty estimates to track regions where significant posterior mass may have been missed. ITs can be based on any inference method that provides a consistent estimate of the marginal likelihood. They are particularly effective when combined with sequential Monte Carlo, where they capture long-range dependencies and yield improvements beyond proposal adaptation alone.

  @article{rainforth2018it,
    title = {Inference Trees: Adaptive Inference with Exploration},
    author = {Rainforth, Tom and Zhou, Yuan and Lu, Xiaoyu and Teh, Yee Whye and Wood, Frank and Yang, Hongseok and van de Meent, Jan-Willem},
    journal = {arXiv preprint arXiv:1806.09550},
    year = {2018}
  }
  

• X. Lu , T. Rainforth , Y. Zhou , J. Meent , Y. W. Teh , On Exploration, Exploitation and Learning in Adaptive Importance Sampling, arXiv preprint arXiv:1810.13296, 2018.
  
  Project: bigbayes

  We study adaptive importance sampling (AIS) as an online learning problem and argue for the importance of the trade-off between exploration and exploitation in this adaptation. Borrowing ideas from the bandits literature, we propose Daisee, a partition-based AIS algorithm. We further introduce a notion of regret for AIS and show that Daisee has O((log T)^(3/4) √T) cumulative pseudo-regret, where T is the number of iterations. We then extend Daisee to adaptively learn a hierarchical partitioning of the sample space for more efficient sampling and confirm the performance of both algorithms empirically.

  @article{lu2018exploration,
    title = {{On Exploration, Exploitation and Learning in Adaptive Importance Sampling}},
    author = {Lu, Xiaoyu and Rainforth, Tom and Zhou, Yuan and van de Meent, Jan-Willem and Teh, Yee Whye},
    journal = {arXiv preprint arXiv:1810.13296},
    year = {2018}
  }
  

• T. Rainforth , Nesting Probabilistic Programs, Conference on Uncertainty in Artificial Intelligence (UAI), 2018.
  
  Project: bigbayes

  We formalize the notion of nesting probabilistic programming queries and investigate the resulting statistical implications. We demonstrate that query nesting allows the definition of models which could not otherwise be expressed, such as those involving agents reasoning about other agents, but that existing systems take approaches that lead to inconsistent estimates. We show how to correct this by delineating possible ways one might want to nest queries and asserting the respective conditions required for convergence. We further introduce, and prove the correctness of, a new online nested Monte Carlo estimation method that makes it substantially easier to ensure these conditions are met, thereby providing a simple framework for designing statistically correct inference engines.

  @article{rainforth2018nesting,
    title = {Nesting Probabilistic Programs},
    author = {Rainforth, Tom},
    journal = {Conference on Uncertainty in Artificial Intelligence (UAI)},
    year = {2018}
  }
  

• T. Rainforth , R. Cornish , H. Yang , A. Warrington , F. Wood , On Nesting Monte Carlo Estimators, International Conference on Machine Learning (ICML), 2018.
  
  Project: bigbayes

  Many problems in machine learning and statistics involve nested expectations and thus do not permit conventional Monte Carlo (MC) estimation. For such problems, one must nest estimators, such that terms in an outer estimator themselves involve calculation of a separate, nested, estimation. We investigate the statistical implications of nesting MC estimators, including cases of multiple levels of nesting, and establish the conditions un- der which they converge. We derive corresponding rates of convergence and provide empirical evidence that these rates are observed in practice. We further establish a number of pitfalls that can arise from naive nesting of MC estimators, provide guidelines about how these can be avoided, and lay out novel methods for reformulating certain classes of nested expectation problems into single expectations, leading to improved convergence rates. We demonstrate the applicability of our work by using our results to develop a new estimator for discrete Bayesian experimental design problems and derive error bounds for a class of variational objectives.

  @article{rainforth2017opportunities,
    title = {{On Nesting Monte Carlo Estimators}},
    author = {Rainforth, Tom and Cornish, Robert and Yang, Hongseok and Warrington, Andrew and Wood, Frank},
    journal = {International Conference on Machine Learning (ICML)},
    year = {2018}
  }
  

• T. A. Le , M. Igl , T. Rainforth , T. Jin , F. Wood , Auto-Encoding Sequential Monte Carlo, in International Conference on Learning Representations, 2018.
  
  We build on auto-encoding sequential Monte Carlo (AESMC): a method for model and proposal learning based on maximizing the lower bound to the log marginal likelihood in a broad family of structured probabilistic models. Our approach relies on the efficiency of sequential Monte Carlo (SMC) for performing inference in structured probabilistic models and the flexibility of deep neural networks to model complex conditional probability distributions. We develop additional theoretical insights and experiment with a new training procedure which can improve both model and proposal learning. We demonstrate that our approach provides a fast, easy-to-implement and scalable means for simultaneous model learning and proposal adaptation in deep generative models.

  @inproceedings{le2018autoencoding,
    title = {Auto-Encoding Sequential Monte Carlo},
    author = {Le, Tuan Anh and Igl, Maximilian and Rainforth, Tom and Jin, Tom and Wood, Frank},
    booktitle = {International Conference on Learning Representations},
    year = {2018}
  }
  

• D. T. Frazier , G. M. Martin , C. P. Robert , J. Rousseau , Asymptotic properties of approximate Bayesian computation, arXiv preprint arXiv:1607.06903, 2018.
  

  @article{frazier2018asymptotic,
    title = {Asymptotic properties of approximate Bayesian computation},
    author = {Frazier, David T and Martin, Gael M and Robert, Christian P and Rousseau, Judith},
    journal = {arXiv preprint arXiv:1607.06903},
    year = {2018}
  }
  

• D. J. Benjamin , J. O. Berger , M. Johannesson , B. A. Nosek , E. Wagenmakers , R. Berk , K. A. Bollen , B. Brembs , L. Brown , C. Camerer , . others , Redefine statistical significance, Nature Human Behaviour, vol. 2, no. 1, 6, 2018.
  

  @article{benjamin2018redefine,
    title = {Redefine statistical significance},
    author = {Benjamin, Daniel J and Berger, James O and Johannesson, Magnus and Nosek, Brian A and Wagenmakers, E-J and Berk, Richard and Bollen, Kenneth A and Brembs, Bj{\"o}rn and Brown, Lawrence and Camerer, Colin and others},
    journal = {Nature Human Behaviour},
    volume = {2},
    number = {1},
    pages = {6},
    year = {2018},
    publisher = {Nature Publishing Group}
  }
  

• S. Donnet , V. Rivoirard , J. Rousseau , C. Scricciolo , . others , Posterior concentration rates for empirical Bayes procedures with applications to Dirichlet process mixtures, Bernoulli, vol. 24, no. 1, 231–256, 2018.
  

  @article{donnet2018posterior,
    title = {Posterior concentration rates for empirical Bayes procedures with applications to Dirichlet process mixtures},
    author = {Donnet, Sophie and Rivoirard, Vincent and Rousseau, Judith and Scricciolo, Catia and others},
    journal = {Bernoulli},
    volume = {24},
    number = {1},
    pages = {231--256},
    year = {2018},
    publisher = {Bernoulli Society for Mathematical Statistics and Probability}
  }
  

• T. G. J. Rudner , V. Fortuin , Y. W. Teh , Y. Gal , On the Connection between Neural Processes and Approximate Gaussian Processes, NeurIPS 2018 Workshop on Bayesian Deep Learning, 2018.
  
  @article{Rudner:etal:2018,
    author = {Rudner, Tim G. J. and Fortuin, Vincent and Teh, Yee Whye and Gal, Yarin},
    title = {{O}n the {C}onnection between {N}eural {P}rocesses and {A}pproximate {G}aussian {P}rocesses},
    journal = {NeurIPS 2018 Workshop on Bayesian Deep Learning},
    year = {2018}
  }
  

• H. C. L. Law , P. Zhao , J. Huang , D. Sejdinovic , Hyperparameter Learning via Distributional Transfer, ArXiv e-prints:1810.06305, 2018.
  
  Project: tencent-lsml

  Bayesian optimisation is a popular technique for hyperparameter learning but typically requires initial ’exploration’ even in cases where potentially similar prior tasks have been solved. We propose to transfer information across tasks using kernel embeddings of distributions of training datasets used in those tasks. The resulting method has a faster convergence compared to existing baselines, in some cases requiring only a few evaluations of the target objective.

  @unpublished{LawZhaHuaSej2019,
    author = {Law, Ho Chung Leon and Zhao, Peilin and Huang, Junzhou and Sejdinovic, Dino},
    title = {{{Hyperparameter Learning via Distributional Transfer}}},
    journal = {ArXiv e-prints:1810.06305},
    year = {2018}
  }
  

• M. Kanagawa , P. Hennig , D. Sejdinovic , B. Sriperumbudur , Gaussian Processes and Kernel Methods: A Review on Connections and Equivalences, ArXiv e-prints:1807.02582, 2018.
  
  This paper is an attempt to bridge the conceptual gaps between researchers working on the two widely used approaches based on positive definite kernels: Bayesian learning or inference using Gaussian processes on the one side, and frequentist kernel methods based on reproducing kernel Hilbert spaces on the other. It is widely known in machine learning that these two formalisms are closely related; for instance, the estimator of kernel ridge regression is identical to the posterior mean of Gaussian process regression. However, they have been studied and developed almost independently by two essentially separate communities, and this makes it difficult to seamlessly transfer results between them. Our aim is to overcome this potential difficulty. To this end, we review several old and new results and concepts from either side, and juxtapose algorithmic quantities from each framework to highlight close similarities. We also provide discussions on subtle philosophical and theoretical differences between the two approaches.

  @unpublished{KanHenSejSri2018,
    author = {Kanagawa, M. and Hennig, P. and Sejdinovic, D. and Sriperumbudur, B.K.},
    title = {{{ Gaussian Processes and Kernel Methods: A Review on Connections and Equivalences}}},
    journal = {ArXiv e-prints:1807.02582},
    year = {2018}
  }
  

• J. Ton , S. Flaxman , D. Sejdinovic , S. Bhatt , Spatial Mapping with Gaussian Processes and Nonstationary Fourier Features, Spatial Statistics, vol. 28, 59–78, 2018.
  
  Project: bigbayes

  The use of covariance kernels is ubiquitous in the field of spatial statistics. Kernels allow data to be mapped into high-dimensional feature spaces and can thus extend simple linear additive methods to nonlinear methods with higher order interactions. However, until recently, there has been a strong reliance on a limited class of stationary kernels such as the Matern or squared exponential, limiting the expressiveness of these modelling approaches. Recent machine learning research has focused on spectral representations to model arbitrary stationary kernels and introduced more general representations that include classes of nonstationary kernels. In this paper, we exploit the connections between Fourier feature representations, Gaussian processes and neural networks to generalise previous approaches and develop a simple and efficient framework to learn arbitrarily complex nonstationary kernel functions directly from the data, while taking care to avoid overfitting using state-of-the-art methods from deep learning. We highlight the very broad array of kernel classes that could be created within this framework. We apply this to a time series dataset and a remote sensing problem involving land surface temperature in Eastern Africa. We show that without increasing the computational or storage complexity, nonstationary kernels can be used to improve generalisation performance and provide more interpretable results.

  @article{TonFlaSejBha2018,
    author = {Ton, J.-F. and Flaxman, S. and Sejdinovic, D. and Bhatt, S.},
    title = {{Spatial Mapping with Gaussian Processes and Nonstationary Fourier Features}},
    journal = {Spatial Statistics},
    year = {2018},
    volume = {28},
    pages = {59--78}
  }
  

• H. Law , D. Sutherland , D. Sejdinovic , S. Flaxman , Bayesian Approaches to Distribution Regression, in Artificial Intelligence and Statistics (AISTATS), 2018.
  
  Project: bigbayes

  Distribution regression has recently attracted much interest as a generic solution to the problem of supervised learning where labels are available at the group level, rather than at the individual level. Current approaches, however, do not propagate the uncertainty in observations due to sampling variability in the groups. This effectively assumes that small and large groups are estimated equally well, and should have equal weight in the final regression. We account for this uncertainty with a Bayesian distribution regression formalism, improving the robustness and performance of the model when group sizes vary. We frame our models in a neural network style, allowing for simple MAP inference using backpropagation to learn the parameters, as well as MCMC-based inference which can fully propagate uncertainty. We demonstrate our approach on illustrative toy datasets, as well as on a challenging problem of predicting age from images.

  @inproceedings{LawSutSejFla2018,
    author = {Law, H.C.L. and Sutherland, D.J. and Sejdinovic, D. and Flaxman, S.},
    title = {{Bayesian Approaches to Distribution Regression}},
    booktitle = {Artificial Intelligence and Statistics (AISTATS)},
    year = {2018}
  }
  

• M. Willetts , S. Hollowell , L. Aslett , C. Holmes , A. Doherty , Statistical machine learning of sleep and physical activity phenotypes from sensor data in 96, 220 UK Biobank participants, Scientific Reports, 2018.
  
  @article{Willetts2018ActivityHMM,
    author = {Willetts, Matthew and Hollowell, Sven and Aslett, Louis and Holmes, Chris and Doherty, Aiden},
    doi = {10.1038/s41598-018-26174-1},
    journal = {Scientific Reports},
    title = {{Statistical machine learning of sleep and physical activity phenotypes from sensor data in 96, 220 UK Biobank participants}},
    year = {2018}
  }
  

• M. Willetts , S. Roberts , C. Holmes , Semi-Unsupervised Learning using Deep Generative Models, in NeurIPS Bayesian Deep Learning Workshop, 2018.
  

  @inproceedings{Willetts2018SemiUnsupervised,
    author = {Willetts, Matthew and Roberts, Stephen and Holmes, Chris},
    booktitle = {NeurIPS Bayesian Deep Learning Workshop},
    title = {{Semi-Unsupervised Learning using Deep Generative Models}},
    year = {2018}
  }
  

• M. Willetts , A. Doherty , S. Roberts , C. Holmes , Semi-Unsupervised Learning of Human Activity using Deep Generative Models, in NeurIPS ML4Health Workshop, 2018.
  

  @inproceedings{Willetts2018ActivityDGM,
    archiveprefix = {arXiv},
    arxivid = {1810.12176v2},
    author = {Willetts, Matthew and Doherty, Aiden and Roberts, Stephen and Holmes, Chris},
    booktitle = {NeurIPS ML4Health Workshop},
    eprint = {1810.12176v2},
    title = {{Semi-Unsupervised Learning of Human Activity using Deep Generative Models}},
    year = {2018}
  }
  

2017

• N. Dhir , A. R. Kosiorek , I. Posner , Bayesian Delay Embeddings for Dynamical Systems, in NIPS Timeseries Workshop, 2017.
  
  @inproceedings{DhirNIPS2017,
    author = {Dhir, Neil and Kosiorek, Adam Roman and Posner, Ingmar},
    title = {Bayesian Delay Embeddings for Dynamical Systems},
    booktitle = {NIPS Timeseries Workshop},
    year = {2017},
    month = dec
  }
  

• A. R. Kosiorek , A. Bewley , I. Posner , Hierarchical Attentive Recurrent Tracking, in Neural Information Processing Systems, 2017.
  
  @inproceedings{Kosiorek2017hierarchical,
    title = {Hierarchical Attentive Recurrent Tracking},
    author = {Kosiorek, Adam R and Bewley, Alex and Posner, Ingmar},
    booktitle = {Neural Information Processing Systems},
    year = {2017},
    month = dec
  }
  

• C. J. Maddison , D. Lawson , G. Tucker , N. Heess , M. Norouzi , A. Mnih , A. Doucet , Y. W. Teh , Filtering Variational Objectives, in Advances in Neural Information Processing Systems (NeurIPS), 2017.
  
  Project: deepmind

  The evidence lower bound (ELBO) appears in many algorithms for maximum likelihood estimation (MLE) with latent variables because it is a sharp lower bound of the marginal log-likelihood. For neural latent variable models, optimizing the ELBO jointly in the variational posterior and model parameters produces state-of-the-art results. Inspired by the success of the ELBO as a surrogate MLE objective, we consider the extension of the ELBO to a family of lower bounds defined by a Monte Carlo estimator of the marginal likelihood. We show that the tightness of such bounds is asymptotically related to the variance of the underlying estimator. We introduce a special case, the filtering variational objectives (FIVOs), which takes the same arguments as the ELBO and passes them through a particle filter to form a tighter bound. FIVOs can be optimized tractably with stochastic gradients, and are particularly suited to MLE in sequential latent variable models. In standard sequential generative modeling tasks we present uniform improvements over models trained with ELBO, including some whole nat-per-timestep improvements.

  @inproceedings{MadLawTuc2017b,
    author = {Maddison, C. J. and Lawson, D. and Tucker, G. and Heess, N. and Norouzi, M. and Mnih, A. and Doucet, A. and Teh, Y. W.},
    booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
    title = {Filtering Variational Objectives},
    year = {2017},
    month = dec,
    bdsk-url-1 = {https://arxiv.org/pdf/1705.09279v1.pdf}
  }
  

• V. Perrone , P. A. Jenkins , D. Spano , Y. W. Teh , Poisson Random Fields for Dynamic Feature Models, Journal of Machine Learning Research (JMLR), Dec. 2017.
  
  Project: bigbayes

  @article{PerJenSpa2017a,
    author = {Perrone, V. and Jenkins, P. A. and Spano, D. and Teh, Y. W.},
    journal = {Journal of Machine Learning Research (JMLR)},
    month = dec,
    year = {2017},
    title = {{P}oisson Random Fields for Dynamic Feature Models},
    bdsk-url-1 = {https://arxiv.org/abs/1611.07460},
    bdsk-url-2 = {https://arxiv.org/pdf/1611.07460v1.pdf}
  }
  

• G. Di Benedetto , F. Caron , Y. W. Teh , Non-exchangeable random partition models for microclustering, Nov-2017.
  
  Project: bigbayes

  @unpublished{DiBenedetto2017,
    author = {Di Benedetto, Giuseppe and Caron, Fran{\c{c}}ois and Teh, Yee Whye},
    title = {Non-exchangeable random partition models for microclustering},
    note = {ArXiv e-prints:1711.07287},
    archiveprefix = {arXiv},
    year = {2017},
    month = nov
  }
  

• B. Bloem-Reddy , P. Orbanz , Preferential Attachment and Vertex Arrival Times, Oct. 2017.
  
  Project: bigbayes

  @article{Bloem-Reddy:Orbanz:2017,
    archiveprefix = {arXiv},
    author = {Bloem-Reddy, Benjamin and Orbanz, Peter},
    eprint = {1710.02159},
    month = oct,
    primaryclass = {math.PR},
    title = {Preferential Attachment and Vertex Arrival Times},
    year = {2017}
  }
  

• L. Hasenclever , S. Webb , T. Lienart , S. Vollmer , B. Lakshminarayanan , C. Blundell , Y. W. Teh , Distributed Bayesian Learning with Stochastic Natural-gradient Expectation Propagation and the Posterior Server, Journal of Machine Learning Research (JMLR), Oct. 2017.
  
  Project: sgmcmc

  This paper makes two contributions to Bayesian machine learning algorithms. Firstly, we propose stochastic natural gradient expectation propagation (SNEP), a novel alternative to expectation propagation (EP), a popular variational inference algorithm. SNEP is a black box variational algorithm, in that it does not require any simplifying assumptions on the distribution of interest, beyond the existence of some Monte Carlo sampler for estimating the moments of the EP tilted distributions. Further, as opposed to EP which has no guarantee of convergence, SNEP can be shown to be convergent, even when using Monte Carlo moment estimates. Secondly, we propose a novel architecture for distributed Bayesian learning which we call the posterior server. The posterior server allows scalable and robust Bayesian learning in cases where a dataset is stored in a distributed manner across a cluster, with each compute node containing a disjoint subset of data. An independent Monte Carlo sampler is run on each compute node, with direct access only to the local data subset, but which targets an approximation to the global posterior distribution given all data across the whole cluster. This is achieved by using a distributed asynchronous implementation of SNEP to pass messages across the cluster. We demonstrate SNEP and the posterior server on distributed Bayesian learning of logistic regression and neural networks.

  @article{HasWebLie2017a,
    author = {Hasenclever, L. and Webb, S. and Lienart, T. and Vollmer, S. and Lakshminarayanan, B. and Blundell, C. and Teh, Y. W.},
    note = {ArXiv e-prints: 1512.09327},
    title = {Distributed {B}ayesian Learning with Stochastic Natural-gradient Expectation Propagation and the Posterior Server},
    journal = {Journal of Machine Learning Research (JMLR)},
    month = oct,
    year = {2017},
    bdsk-url-1 = {https://arxiv.org/pdf/1512.09327.pdf}
  }
  

• A. Todeschini , X. Miscouridou , F. Caron , Exchangeable Random Measures for Sparse and Modular Graphs with Overlapping Communities, Aug-2017.
  
  Project: bigbayes

  @unpublished{OverlappingCommunityGraph,
    author = {Todeschini, Adrien and Miscouridou, Xenia and Caron, Fran\c ois},
    title = {Exchangeable Random Measures for Sparse and Modular Graphs with Overlapping Communities},
    note = {ArXiv e-prints: 1602.02114},
    archiveprefix = {arXiv},
    year = {2017},
    month = aug
  }
  

• T. Nagapetyan , A. B. Duncan , L. Hasenclever , S. J. Vollmer , L. Szpruch , K. Zygalakis , The True Cost of Stochastic Gradient Langevin Dynamics, Jun-2017.
  
  The problem of posterior inference is central to Bayesian statistics and a wealth of Markov Chain Monte Carlo (MCMC) methods have been proposed to obtain asymptotically correct samples from the posterior. As datasets in applications grow larger and larger, scalability has emerged as a central problem for MCMC methods. Stochastic Gradient Langevin Dynamics (SGLD) and related stochastic gradient Markov Chain Monte Carlo methods offer scalability by using stochastic gradients in each step of the simulated dynamics. While these methods are asymptotically unbiased if the stepsizes are reduced in an appropriate fashion, in practice constant stepsizes are used. This introduces a bias that is often ignored. In this paper we study the mean squared error of Lipschitz functionals in strongly log- concave models with i.i.d. data of growing data set size and show that, given a batchsize, to control the bias of SGLD the stepsize has to be chosen so small that the computational cost of reaching a target accuracy is roughly the same for all batchsizes. Using a control variate approach, the cost can be reduced dramatically. The analysis is performed by considering the algorithms as noisy discretisations of the Langevin SDE which correspond to the Euler method if the full data set is used. An important observation is that the 1scale of the step size is determined by the stability criterion if the accuracy is required for consistent credible intervals. Experimental results confirm our theoretical findings.

  @unpublished{Nagapetyan2017,
    month = jun,
    author = {Nagapetyan, T. and Duncan, A. B. and Hasenclever, L. and Vollmer, S. J. and Szpruch, L. and Zygalakis, K.},
    eprint = {1706.02692},
    title = {{The True Cost of Stochastic Gradient Langevin Dynamics}},
    year = {2017}
  }
  

• Z. Hu , C. Yau , A. A. Ahmed , A pan-cancer genome-wide analysis reveals tumour dependencies by induction of nonsense-mediated decay, Nature communications, vol. 8, 15943, Jun. 2017.
  
  Nonsense-mediated decay (NMD) eliminates transcripts with premature termination codons. Although NMD-induced loss-of-function has been shown to contribute to the genesis of particular cancers, its global functional consequence in tumours has not been characterized. Here we develop an algorithm to predict NMD and apply it on somatic mutations reported in The Cancer Genome Atlas. We identify more than 73 K mutations that are predicted to elicit NMD (NMD-elicit). NMD-elicit mutations in tumour suppressor genes (TSGs) are associated with significant reduction in gene expression. We discover cancer-specific NMD-elicit signatures in TSGs and cancer-associated genes. Our analysis reveals a previously unrecognized dependence of hypermutated tumours on hypofunction of genes that are involved in chromatin remodelling and translation. Half of hypermutated stomach adenocarcinomas are associated with NMD-elicit mutations of the translation initiators LARP4B and EIF5B. Our results unravel strong therapeutic opportunities by targeting tumour dependencies on NMD-elicit mutations.

  @article{Hu:2017bra,
    author = {Hu, Zhiyuan and Yau, Christopher and Ahmed, Ahmed Ashour},
    title = {{A pan-cancer genome-wide analysis reveals tumour dependencies by induction of nonsense-mediated decay}},
    journal = {Nature communications},
    year = {2017},
    volume = {8},
    pages = {15943},
    month = jun
  }
  

• M. A. Smith , C. B. Nielsen , F. C. Chan , A. McPherson , A. Roth , H. Farahani , D. Machev , A. Steif , S. P. Shah , E-scape: interactive visualization of single-cell phylogenetics and cancer evolution, Nature Methods, vol. 14, no. 6, 549–550, May 2017.
  

  @article{smith2017scape,
    author = {Smith, Maia A and Nielsen, Cydney B and Chan, Fong Chun and McPherson, Andrew and Roth, Andrew and Farahani, Hossein and Machev, Daniel and Steif, Adi and Shah, Sohrab P},
    journal = {Nature Methods},
    month = may,
    number = {6},
    pages = {549--550},
    publisher = {Nature Research},
    title = {{E-scape: interactive visualization of single-cell phylogenetics and cancer evolution}},
    volume = {14},
    year = {2017}
  }
  

• A. Bouchard-Côté , A. Doucet , A. Roth , Particle Gibbs Split-Merge Sampling for Bayesian Inference in Mixture Models, Journal of Machine Learning Research, vol. 18, no. 28, 1–39, Apr. 2017.
  

  @article{pg-sm,
    author = {Bouchard-C{\^o}t{\'e}, Alexandre and Doucet, Arnaud and Roth, Andrew},
    journal = {Journal of Machine Learning Research},
    month = apr,
    number = {28},
    pages = {1--39},
    title = {{Particle Gibbs Split-Merge Sampling for Bayesian Inference in Mixture Models}},
    volume = {18},
    year = {2017}
  }
  

• J. Rousseau , B. Szabo , Asymptotic behaviour of the empirical Bayes posteriors associated to maximum marginal likelihood estimator, Ann. Statist., vol. 45, no. 2, 833–865, Apr. 2017.
  
  @article{rousseau2017,
    author = {Rousseau, Judith and Szabo, Botond},
    doi = {10.1214/16-AOS1469},
    fjournal = {The Annals of Statistics},
    journal = {Ann. Statist.},
    month = apr,
    number = {2},
    pages = {833--865},
    publisher = {The Institute of Mathematical Statistics},
    title = {Asymptotic behaviour of the empirical Bayes posteriors associated to maximum marginal likelihood estimator},
    volume = {45},
    year = {2017}
  }
  

• J. Arbel , S. Favaro , B. Nipoti , Y. W. Teh , Bayesian nonparametric inference for discovery probabilities: credible intervals and large sample asymptotics, Statistica Sinica, Apr. 2017.
  
  Project: bigbayes

  Given a sample of size n from a population of individuals belonging to different species with unknown proportions, a popular problem of practical interest consists in making inference on the probability Dn(l) that the (n+1)-th draw coincides with a species with frequency l in the sample, for any l = 0,1,...,n. This paper contributes to the methodology of Bayesian nonparametric inference for Dn(l). Specifically, under the general framework of Gibbs-type priors we show how to derive credible intervals for a Bayesian nonparametric estimation of Dn(l), and we investigate the large n asymptotic behaviour of such an estimator. Of particular interest are special cases of our results obtained under the specification of the two parameter Poisson–Dirichlet prior and the normalized generalized Gamma prior, which are two of the most commonly used Gibbs-type priors. With respect to these two prior specifications, the proposed results are illustrated through a simulation study and a benchmark Expressed Sequence Tags dataset. To the best our knowledge, this illustration provides the first comparative study between the two parameter Poisson–Dirichlet prior and the normalized generalized Gamma prior in the context of Bayesian nonparemetric inference for Dn(l).

  @article{ArbFavNip2017a,
    author = {Arbel, J. and Favaro, S. and Nipoti, B. and Teh, Y. W.},
    journal = {Statistica Sinica},
    title = {{B}ayesian nonparametric inference for discovery probabilities: credible intervals and large sample asymptotics},
    year = {2017},
    month = apr
  }
  

• X. Lu , V. Perrone , L. Hasenclever , Y. W. Teh , S. J. Vollmer , Relativistic Monte Carlo, in Artificial Intelligence and Statistics (AISTATS), 2017.
  
  Project: bigbayes

  Hamiltonian Monte Carlo (HMC) is a popular Markov chain Monte Carlo (MCMC) algorithm that generates proposals for a Metropolis-Hastings algorithm by simulating the dynamics of a Hamiltonian system. However, HMC is sensitive to large time discretizations and performs poorly if there is a mismatch between the spatial geometry of the target distribution and the scales of the momentum distribution. In particular the mass matrix of HMC is hard to tune well. In order to alleviate these problems we propose relativistic Hamiltonian Monte Carlo, a version of HMC based on relativistic dynamics that introduce a maximum velocity on particles. We also derive stochastic gradient versions of the algorithm and show that the resulting algorithms bear interesting relationships to gradient clipping, RMSprop, Adagrad and Adam, popular optimisation methods in deep learning. Based on this, we develop relativistic stochastic gradient descent by taking the zero-temperature limit of relativistic stochastic gradient Hamiltonian Monte Carlo. In experiments we show that the relativistic algorithms perform better than classical Newtonian variants and Adam.

  @inproceedings{LuPerHas2016a,
    author = {Lu, X. and Perrone, V. and Hasenclever, L. and Teh, Y. W. and Vollmer, S. J.},
    booktitle = {Artificial Intelligence and Statistics (AISTATS)},
    title = {Relativistic {M}onte {C}arlo},
    month = apr,
    year = {2017},
    bdsk-url-1 = {https://arxiv.org/pdf/1609.04388v1.pdf}
  }
  

• S. Salehi , A. Steif , A. Roth , S. Aparicio , A. Bouchard-Côté , S. P. Shah , ddClone: joint statistical inference of clonal populations from single cell and bulk tumour sequencing data, Genome biology, vol. 18, no. 1, 44, Mar. 2017.
  

  @article{salehi2017ddclone,
    author = {Salehi, Sohrab and Steif, Adi and Roth, Andrew and Aparicio, Samuel and Bouchard-C{\^o}t{\'e}, Alexandre and Shah, Sohrab P},
    journal = {Genome biology},
    month = mar,
    number = {1},
    pages = {44},
    publisher = {BioMed Central},
    title = {{ddClone: joint statistical inference of clonal populations from single cell and bulk tumour sequencing data}},
    volume = {18},
    year = {2017}
  }
  

• M. Battiston , S. Favaro , Discussion of F. Caron and E. B. Fox, "Sparse graphs using exchangeable random measures.", Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol. 79, no. 5, 2017.
  
  Project: bigbayes

  @article{Battiston:Favaro:2017a,
    author = {Battiston, M. and Favaro, S.},
    journal = {Journal of the Royal Statistical Society: Series B (Statistical Methodology)},
    number = {5},
    title = {Discussion of F. Caron and E. B. Fox, "Sparse graphs using exchangeable random measures."},
    volume = {79},
    year = {2017}
  }
  

• S. Bacallado , M. Battiston , S. Favaro , L. Trippa , Sufficientness postulates for Gibbs-type priors and hierarchial generalizations, Statistical Sciences, vol. 32, 487–500, 2017.
  
  Project: bigbayes

  @article{Battiston:Bacallado:Favaro:Trippa:2017,
    author = {Bacallado, S. and Battiston, M. and Favaro, S. and Trippa, L.},
    journal = {Statistical Sciences},
    pages = {487–-500},
    title = {Sufficientness postulates for Gibbs-type priors and hierarchial generalizations},
    volume = {32},
    year = {2017}
  }
  

• B. Bloem-Reddy , Discussion of F. Caron and E. B. Fox, "Sparse graphs using exchangeable random measures.", Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol. 79, no. 5, 2017.
  

  @article{BloemReddy:2017aa,
    author = {Bloem-Reddy, Benjamin},
    journal = {Journal of the Royal Statistical Society: Series B (Statistical Methodology)},
    number = {5},
    title = {Discussion of F. Caron and E. B. Fox, "Sparse graphs using exchangeable random measures."},
    volume = {79},
    year = {2017}
  }
  

• A. Barbos , F. Caron , J. F. Giovannelli , A. Doucet , Clone MCMC: Parallel High-Dimensional Gaussian Gibbs Sampling, in Advances in Neural Information Processing Systems (NeurIPS), 2017.
  

  @incollection{Barbos2017,
    title = {Clone {MCMC}: Parallel High-Dimensional {G}aussian {G}ibbs Sampling},
    author = {Barbos, A. and Caron, F. and Giovannelli, J. F. and Doucet, A.},
    booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
    year = {2017}
  }
  

• F. Caron , E. B. Fox , Sparse Graphs using exchangeable random measures, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol. 79, no. 5, 1295–1366, 2017.
  
  @article{Caron2017,
    title = {Sparse Graphs using exchangeable random measures},
    author = {Caron, F. and Fox, E. B.},
    journal = {Journal of the Royal Statistical Society: Series B (Statistical Methodology)},
    year = {2017},
    number = {5},
    pages = {1295-1366},
    volume = {79}
  }
  

• F. Caron , W. Neiswanger , F. Wood , A. Doucet , M. Davy , Generalized Pólya Urn for Time-Varying Pitman-Yor Processes, Journal of Machine Learning Research (JMLR), vol. 18, no. 27, 1–32, 2017.
  
  @article{Caron2017a,
    title = {Generalized {P}{\'o}lya Urn for Time-Varying Pitman-Yor Processes},
    author = {Caron, F. and Neiswanger, W. and Wood, F. and Doucet, A. and Davy, M.},
    journal = {Journal of Machine Learning Research (JMLR)},
    year = {2017},
    number = {27},
    pages = {1-32},
    volume = {18}
  }
  

• E. Matechou , F. Caron , Modelling individual migration patterns using a Bayesian nonparametric approach for capture-recapture data, Annals of Applied Statistics, vol. 11, no. 1, 21–40, 2017.
  
  @article{Matechou2017,
    title = {Modelling individual migration patterns using a Bayesian nonparametric approach for capture-recapture data},
    author = {Matechou, E. and Caron, F.},
    journal = {Annals of Applied Statistics},
    year = {2017},
    number = {1},
    pages = {21-40},
    volume = {11}
  }
  

• A. Caterini , A Novel Mathematical Framework for the Analysis of Neural Networks, Master's thesis, University of Waterloo, 2017.
  

  @mastersthesis{caterini2017novel,
    title = {{A Novel Mathematical Framework for the Analysis of Neural Networks}},
    author = {Caterini, A.},
    year = {2017},
    school = {University of Waterloo}
  }
  

• G. Deligiannidis , Z. Kosloff , Relative complexity of Random walks in Random scenery in the absence of a weak invariance principle for the local times, Annals of Probability, vol. 45, no. 4, 2505–2532, 2017.
  

  @article{deligiannidis2017relative,
    author = {Deligiannidis, G. and Kosloff, Z.},
    title = {Relative complexity of Random walks in Random scenery in the absence of a weak invariance principle for the local times},
    journal = {Annals of Probability},
    year = {2017},
    volume = {45},
    number = {4},
    pages = {2505-2532}
  }
  

• P. Vanetti , A. Bouchard-Côté , G. Deligiannidis , A. Doucet , Piecewise-Deterministic Markov Chain Monte Carlo, arXiv preprint arXiv:1707.05296, 2017.
  

  @article{vanetti2017piecewise,
    title = {Piecewise-Deterministic Markov Chain Monte Carlo},
    author = {Vanetti, P. and Bouchard-C{\^o}t{\'e}, A. and Deligiannidis, G. and Doucet, A.},
    journal = {arXiv preprint arXiv:1707.05296},
    year = {2017}
  }
  

• C. Nowzohour , M. Maathuis , R. J. Evans , P. Bühlmann , Structure learning with bow-free acyclic path diagrams, Electronic Journal of Statistics, vol. 11, no. 2, 5342–5374, 2017.
  

  @article{nowzohour:17,
    title = {Structure learning with bow-free acyclic path diagrams},
    author = {Nowzohour, Christopher and Maathuis, Marloes and Evans, R. J. and B{\"u}hlmann, Peter},
    journal = {Electronic Journal of Statistics},
    volume = {11},
    number = {2},
    pages = {5342-5374},
    year = {2017}
  }
  

• Q. F. Wills , E. Mellado-Gomez , R. Nolan , D. Warner , E. Sharma , J. Broxholme , B. Wright , H. Lockstone , W. James , M. Lynch , M. Gonzales , J. West , A. Leyrat , S. Padilla-Parra , S. Filippi , C. Holmes , M. D. Moore , R. Bowden , The nature and nurture of cell heterogeneity: accounting for macrophage gene-environment interactions with single-cell RNA-Seq, BMC Genomics, 2017.
  
  @article{Wills2017,
    author = {Wills, Quin F. and Mellado-Gomez, Esther and Nolan, Rory and Warner, Damien and Sharma, Eshita and Broxholme, John and Wright, Benjamin and Lockstone, Helen and James, William and Lynch, Mark and Gonzales, Michael and West, Jay and Leyrat, Anne and Padilla-Parra, Sergi and Filippi, Sarah and Holmes, Chris and Moore, Michael D. and Bowden, Rory},
    title = {The nature and nurture of cell heterogeneity: accounting for macrophage gene-environment interactions with single-cell RNA-Seq},
    journal = {BMC Genomics},
    year = {2017}
  }
  

• B. Goodman , S. Flaxman , European Union Regulations on Algorithmic Decision Making and a “Right to Explanation,” AI Magazine, vol. 38, no. 3, 50–58, 2017.
  
  Project: bigbayes

  @article{goodman2017european,
    author = {Goodman, Bryce and Flaxman, Seth},
    journal = {AI Magazine},
    number = {3},
    pages = {50--58},
    publisher = {American Association for Artificial Intelligence},
    title = {{European Union} Regulations on Algorithmic Decision Making and a ``Right to Explanation''},
    volume = {38},
    year = {2017}
  }
  

• L. Hasenclever , S. Webb , T. Lienart , S. Vollmer , B. Lakshminarayanan , C. Blundell , Y. W. Teh , Distributed Bayesian Learning with Stochastic Natural Gradient Expectation Propagation and the Posterior Server, Journal of Machine Learning Research, vol. 18, no. 106, 1–37, 2017.
  
  Project: bigbayes sgmcmc

  This paper makes two contributions to Bayesian machine learning algorithms. Firstly, we propose stochastic natural gradient expectation propagation (SNEP), a novel alternative to expectation propagation (EP), a popular variational inference algorithm. SNEP is a black box variational algorithm, in that it does not require any simplifying assumptions on the distribution of interest, beyond the existence of some Monte Carlo sampler for estimating the moments of the EP tilted distributions. Further, as opposed to EP which has no guarantee of convergence, SNEP can be shown to be convergent, even when using Monte Carlo moment estimates. Secondly, we propose a novel architecture for distributed Bayesian learning which we call the posterior server. The posterior server allows scalable and robust Bayesian learning in cases where a dataset is stored in a distributed manner across a cluster, with each compute node containing a disjoint subset of data. An independent Monte Carlo sampler is run on each compute node, with direct access only to the local data subset, but which targets an approximation to the global posterior distribution given all data across the whole cluster. This is achieved by using a distributed asynchronous implementation of SNEP to pass messages across the cluster. We demonstrate SNEP and the posterior server on distributed Bayesian learning of logistic regression and neural networks.

  @article{HasWebLie2015a,
    author = {Hasenclever, Leonard and Webb, Stefan and Lienart, Thibaut and Vollmer, Sebastian and Lakshminarayanan, Balaji and Blundell, Charles and Teh, Yee Whye},
    title = {Distributed Bayesian Learning with Stochastic Natural Gradient Expectation Propagation and the Posterior Server},
    journal = {Journal of Machine Learning Research},
    year = {2017},
    volume = {18},
    number = {106},
    pages = {1-37}
  }
  

• J. Watson , L. Nieto-Barajas , C. C. Holmes , Characterizing variation of nonparametric random probability measures using the Kullback–Leibler divergence, Statistics, vol. 51, no. 3, 558–571, 2017.
  

  @article{watson2017characterizing,
    title = {Characterizing variation of nonparametric random probability measures using the Kullback--Leibler divergence},
    author = {Watson, James and Nieto-Barajas, Luis and Holmes, Chris C.},
    journal = {Statistics},
    volume = {51},
    number = {3},
    pages = {558--571},
    year = {2017},
    publisher = {Taylor \& Francis}
  }
  

• S. Filippi , C. C. Holmes , . others , A Bayesian nonparametric approach to testing for dependence between random variables, Bayesian Analysis, 2017.
  

  @article{filippi2017bayesian,
    title = {A Bayesian nonparametric approach to testing for dependence between random variables},
    author = {Filippi, Sarah and Holmes, Chris C and others},
    journal = {Bayesian Analysis},
    year = {2017},
    publisher = {International Society for Bayesian Analysis}
  }
  

• A. R. Taylor , J. A. Flegg , C. C. Holmes , P. J. Guérin , C. H. Sibley , M. D. Conrad , G. Dorsey , P. J. Rosenthal , Artemether-Lumefantrine and Dihydroartemisinin-Piperaquine Exert Inverse Selective Pressure on Plasmodium Falciparum Drug Sensitivity-Associated Haplotypes in Uganda, in Open forum infectious diseases, 2017, vol. 4, no. 1.
  

  @inproceedings{taylor2017artemether,
    title = {Artemether-Lumefantrine and Dihydroartemisinin-Piperaquine Exert Inverse Selective Pressure on Plasmodium Falciparum Drug Sensitivity-Associated Haplotypes in Uganda},
    author = {Taylor, Aimee R and Flegg, Jennifer A and Holmes, Chris C and Gu{\'e}rin, Philippe J and Sibley, Carol H and Conrad, Melissa D and Dorsey, Grant and Rosenthal, Philip J},
    booktitle = {Open forum infectious diseases},
    volume = {4},
    number = {1},
    year = {2017},
    organization = {Oxford University Press}
  }
  

• G. Nicholson , C. C. Holmes , A note on statistical repeatability and study design for high-throughput assays, Statistics in medicine, vol. 36, no. 5, 790–798, 2017.
  

  @article{nicholson2017note,
    title = {A note on statistical repeatability and study design for high-throughput assays},
    author = {Nicholson, George and Holmes, Chris C.},
    journal = {Statistics in medicine},
    volume = {36},
    number = {5},
    pages = {790--798},
    year = {2017},
    publisher = {John Wiley \& Sons, Ltd}
  }
  

• Q. F. Wills , E. Mellado-Gomez , R. Nolan , D. Warner , E. Sharma , J. Broxholme , B. Wright , H. Lockstone , W. James , M. Lynch , . others , The nature and nurture of cell heterogeneity: accounting for macrophage gene-environment interactions with single-cell RNA-Seq, BMC genomics, vol. 18, no. 1, 53, 2017.
  

  @article{wills2017nature,
    title = {The nature and nurture of cell heterogeneity: accounting for macrophage gene-environment interactions with single-cell RNA-Seq},
    author = {Wills, Quin F and Mellado-Gomez, Esther and Nolan, Rory and Warner, Damien and Sharma, Eshita and Broxholme, John and Wright, Benjamin and Lockstone, Helen and James, William and Lynch, Mark and others},
    journal = {BMC genomics},
    volume = {18},
    number = {1},
    pages = {53},
    year = {2017},
    publisher = {BioMed Central}
  }
  

• C. Holmes , S. Walker , Assigning a value to a power likelihood in a general Bayesian model, Biometrika, vol. 104, no. 2, 497–503, 2017.
  

  @article{holmes2017assigning,
    title = {Assigning a value to a power likelihood in a general Bayesian model},
    author = {Holmes, CC and Walker, SG},
    journal = {Biometrika},
    volume = {104},
    number = {2},
    pages = {497--503},
    year = {2017},
    publisher = {Oxford University Press}
  }
  

• T. Rukat , C. C. Holmes , M. K. Titsias , C. Yau , Bayesian Boolean Matrix Factorisation, arXiv preprint arXiv:1702.06166, 2017.
  

  @article{rukat2017bayesian,
    title = {Bayesian Boolean Matrix Factorisation},
    author = {Rukat, Tammo and Holmes, Chris C and Titsias, Michalis K and Yau, Christopher},
    journal = {arXiv preprint arXiv:1702.06166},
    year = {2017}
  }
  

• P. M. Esperança , L. J. Aslett , C. C. Holmes , Encrypted accelerated least squares regression, arXiv preprint arXiv:1703.00839, 2017.
  

  @article{esperancca2017encrypted,
    title = {Encrypted accelerated least squares regression},
    author = {Esperan{\c{c}}a, Pedro M and Aslett, Louis JM and Holmes, Chris C},
    journal = {arXiv preprint arXiv:1703.00839},
    year = {2017}
  }
  

• C. C. Drovandi , C. C. Holmes , J. McGree , K. Mengersen , S. Richardson , E. Ryan , Principles of experimental design for Big Data analysis, Statistical Science, 2017.
  

  @article{drovandi2017principles,
    title = {Principles of experimental design for Big Data analysis},
    author = {Drovandi, Christopher C and Holmes, Chris C and McGree, James and Mengersen, Kerrie and Richardson, Sylvia and Ryan, Elizabeth},
    journal = {Statistical Science},
    year = {2017},
    publisher = {Institute of Mathematical Statistics}
  }
  

• I. Roxanis , R. Colling , E. A. Rakha , A. Green , J. Rittscher , R. C. Conceicao , A. Ross , G. Nicholson , C. C. Holmes , Digital Analysis of Tumour Microarchitecture as an Independent Prognostic Tool in Breast Cancer, in LABORATORY INVESTIGATION, 2017, vol. 97, 68A–68A.
  

  @inproceedings{roxanis2017digital,
    title = {Digital Analysis of Tumour Microarchitecture as an Independent Prognostic Tool in Breast Cancer},
    author = {Roxanis, Ioannis and Colling, Richard and Rakha, Emad A and Green, Andrew and Rittscher, Jens and Conceicao, Raquel C and Ross, Aidan and Nicholson, George and Holmes, Chris C.},
    booktitle = {LABORATORY INVESTIGATION},
    volume = {97},
    pages = {68A--68A},
    year = {2017},
    organization = {NATURE PUBLISHING GROUP 75 VARICK ST, 9TH FLR, NEW YORK, NY 10013-1917 USA}
  }
  

• A. Doucet , C. Holmes , R. Bardenet , On Markov chain Monte Carlo Methods for Tall Data, 2017.
  

  @article{doucet2017markov,
    title = {On Markov chain Monte Carlo Methods for Tall Data},
    author = {Doucet, A and Holmes, CC and Bardenet, R},
    year = {2017}
  }
  

• Z. Wang , J. S. Morris , S. Cao , J. Ahn , R. Liu , S. Tyekucheva , B. Li , W. Lu , X. Tang , I. I. Wistuba , . others , Transcriptome Deconvolution of Heterogeneous Tumor Samples with Immune Infiltration, bioRxiv, 146795, 2017.
  

  @article{wang2017transcriptome,
    title = {Transcriptome Deconvolution of Heterogeneous Tumor Samples with Immune Infiltration},
    author = {Wang, Zeya and Morris, Jeffrey S and Cao, Shaolong and Ahn, Jaeil and Liu, Rongjie and Tyekucheva, Svitlana and Li, Bo and Lu, Wei and Tang, Ximing and Wistuba, Ignacio I and others},
    journal = {bioRxiv},
    pages = {146795},
    year = {2017},
    publisher = {Cold Spring Harbor Labs Journals}
  }
  

• J. Lee , C. Heakulani , Z. Ghahramani , L. F. James , S. Choi , Bayesian inference on random simple graphs with power law degree distributions, in International Conference on Machine Learning (ICML), 2017.
  

  @inproceedings{Lee2017,
    title = {Bayesian inference on random simple graphs with power law degree distributions},
    author = {Lee, J. and Heakulani, C. and Ghahramani, Z. and James, L. F. and Choi, S.},
    booktitle = {International Conference on Machine Learning (ICML)},
    year = {2017}
  }
  

• C. J. Maddison , A. Mnih , Y. W. Teh , The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables, in International Conference on Learning Representations (ICLR), 2017.
  
  Project: deepmind

  The reparameterization trick enables optimizing large scale stochastic computation graphs via gradient descent. The essence of the trick is to refactor each stochastic node into a differentiable function of its parameters and a random variable with fixed distribution. After refactoring, the gradients of the loss propagated by the chain rule through the graph are low variance unbiased estimators of the gradients of the expected loss. While many continuous random variables have such reparameterizations, discrete random variables lack continuous reparameterizations due to the discontinuous nature of discrete states. In this work we introduce concrete random variables – continuous relaxations of discrete random variables. The concrete distribution is a new family of distributions with closed form densities and a simple reparameterization. Whenever a discrete stochastic node of a computation graph can be refactored into a one-hot bit representation that is treated continuously, concrete stochastic nodes can be used with automatic differentiation to produce low-variance biased gradients of objectives (including objectives that depend on the log-probability of latent stochastic nodes) on the corresponding discrete graph. We demonstrate effectiveness of concrete relaxations on density estimation and structured prediction tasks using neural networks.

  @inproceedings{maddison2016concrete,
    author = {Maddison, Chris J. and Mnih, Andriy and Teh, Yee Whye},
    booktitle = {International Conference on Learning Representations (ICLR)},
    note = {ArXiv e-prints:1611.00712},
    title = {{The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables}},
    year = {2017},
    bdsk-url-1 = {https://arxiv.org/pdf/1611.00712.pdf}
  }
  

• A. K. Styring , M. Charles , F. Fantone , M. M. Hald , A. McMahon , R. H. Meadow , G. K. Nicholls , A. K. Patel , M. C. Pitre , A. Smith , A. Sołtysiak , G. Stein11 , J. A. Weber , H. Weiss , A. Bogaard , Isotope evidence for agricultural extensification reveals how the world’s first cities were fed, Nature Plants, vol. 3, 2017.
  

  @article{Styring17,
    author = {Styring, Amy K. and Charles, Michael and Fantone, Federica and Hald, Mette Marie and McMahon, Augusta and Meadow, Richard H. and Nicholls, Geoff K. and Patel, Ajita K. and Pitre, Mindy C. and Smith, Alexia and Sołtysiak, Arkadiusz and Stein11, Gil and Weber, Jill A. and Weiss, Harvey and Bogaard, Amy},
    journal = {Nature Plants},
    volume = {3},
    title = {{Isotope evidence for agricultural extensification reveals how the world’s first cities were fed}},
    year = {2017}
  }
  

• L. J. Kelly , G. K. Nicholls , Lateral transfer in Stochastic Dollo Models, The Annals of Applied Statistics, vol. 11, 1146–1168, 2017.
  

  @article{kelly17,
    author = {Kelly, L. J. and Nicholls, G. K.},
    title = {{Lateral transfer in {S}tochastic {D}ollo {M}odels}},
    volume = {11},
    journal = {The Annals of Applied Statistics},
    pages = {1146 -- 1168},
    year = {2017}
  }
  

• K. Palla , D. Belgrave , A Birth-Death Modelling Framework for Inferring Disease Causality within the Context of Allergy Development., in 16th IEEE International Conference on Machine Learning and Applications (ICMLA), 2017.
  
  Project: bigbayes

  @inproceedings{PallaBel17,
    author = {Palla, Konstantina and Belgrave, Danielle},
    title = {A Birth-Death Modelling Framework for Inferring Disease Causality within the Context of Allergy Development.},
    year = {2017},
    booktitle = {16th IEEE International Conference on Machine Learning and Applications (ICMLA)}
  }
  

• K. Palla , D. A. Knowles , Z. Ghahramani , A birth-death process for feature allocation., in Proceedings of the 34th International Conference on Machine Learning, 2017.
  
  Project: bigbayes

  We propose a Bayesian nonparametric prior over feature allocations for sequential data, the birth-death feature allocation process (BDFP). The BDFP models the evolution of the feature allocation of a set of objects N across a covariate (e.g. time) by creating and deleting features. A BDFP is exchangeable, projective, stationary and reversible, and its equilibrium distribution is given by the Indian buffet process (IBP). We also present the Beta Event Process (BEP) and we show that it is the de Finetti mixing distribution underlying the BDFP. This results shows that the BEP plays the role for the BDFP that the Beta process plays for the Indian buffet process. Moreover, we show that the BEP permits simplified inference. The utility of this prior is demonstrated on synthetic and real world data.

  @inproceedings{PalKnoGha2016,
    author = {Palla, Konstantina and Knowles, David A. and Ghahramani, Zoubin},
    title = {A birth-death process for feature allocation.},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    year = {2017}
  }
  

• T. Rainforth , Automating Inference, Learning, and Design using Probabilistic Programming, PhD thesis, University of Oxford, 2017.
  
  Imagine a world where computational simulations can be inverted as easily as running them forwards, where data can be used to refine models automatically, and where the only expertise one needs to carry out powerful statistical analysis is a basic proficiency in scientific coding. Creating such a world is the ambitious long-term aim of probabilistic programming. <br><br>The bottleneck for improving the probabilistic models, or simulators, used throughout the quantitative sciences, is often not an ability to devise better models conceptually, but a lack of expertise, time, or resources to realize such innovations. Probabilistic programming systems (PPSs) help alleviate this bottleneck by providing an expressive and accessible modeling framework, then automating the required computation to draw inferences from the model, for example finding the model parameters likely to give rise to a certain output. By decoupling model specification and inference, PPSs streamline the process of developing and drawing inferences from new models, while opening up powerful statistical methods to non-experts. Many systems further provide the flexibility to write new and exciting models which would be hard, or even impossible, to convey using conventional statistical frameworks. <br><br>The central goal of this thesis is to improve and extend PPSs. In particular, we will make advancements to the underlying inference engines and increase the range of problems which can be tackled. For example, we will extend PPSs to a mixed inference-optimization framework, thereby providing automation of tasks such as model learning and engineering design. Meanwhile, we make inroads into constructing systems for automating adaptive sequential design problems, providing potential applications across the sciences. Furthermore, the contributions of the work reach far beyond probabilistic programming, as achieving our goal will require us to make advancements in a number of related fields such as particle Markov chain Monte Carlo methods, Bayesian optimization, and Monte Carlo fundamentals.

  @phdthesis{rainforth2017thesis,
    title = {{Automating Inference, Learning, and Design using
    	Probabilistic Programming}},
    author = {Rainforth, Tom},
    institution = {University of Oxford},
    year = {2017}
  }
  

• B. T. Vincent , T. Rainforth , The DARC Toolbox: automated, flexible, and efficient delayed and risky choice experiments using Bayesian adaptive design, 2017.
  
  Delayed and risky choice (DARC) experiments are a cornerstone of research in psychology, behavioural economics and neuroeconomics. By collecting an agent’s preferences between pairs of prospects we can characterise their preferences, investigate what affects them, and probe the underlying decision making mechanisms. We present a state-of-the-art approach and software toolbox allowing such DARC experiments to be run in a highly efficient way. Data collection is costly, so our toolbox automatically and adaptively generates pairs of prospects in real time to maximise the information gathered about the participant’s behaviours. We demonstrate that this leads to improvements over alternative experimental paradigms. The key to releasing our real time and automatic performance is a number of advances over current Bayesian adaptive design methodology. In particular, we derive an improved estimator for discrete output problems and design a novel algorithm for automating sequential adaptive design. We provide a number of pre-prepared DARC tools for researchers to use, but a key contribution is an adaptive experiment toolbox that can be extended to virtually any 2-alternative-choice tasks. In particular, to carry out custom adaptive experiments using our toolbox, the user need only encode their behavioural model and design space – both the subsequent inference and sequential design optimisation are automated for arbitrary models the user might write.

  @article{vincent2017darc,
    title = {The DARC Toolbox: automated, flexible, and efficient delayed and risky choice experiments using Bayesian adaptive design},
    author = {Vincent, Benjamin T and Rainforth, Tom},
    year = {2017},
    publisher = {PsyArXiv}
  }
  

• B. Bloem-Reddy , E. Mathieu , A. Foster , T. Rainforth , H. Ge , M. Lomelí , Z. Ghahramani , Y. W. Teh , Sampling and inference for discrete random probability measures in probabilistic programs, NIPS Workshop on Advances in Approximate Bayesian Inference, 2017.
  
  Project: bigbayes

  We consider the problem of sampling a sequence from a discrete random probability measure (RPM) with countable support, under (probabilistic) constraints of finite memory and computation. A canonical example is sampling from the Dirichlet Process, which can be accomplished using its stick-breaking representation and lazy initialization of its atoms. We show that efficiently lazy initialization is possible if and only if a size-biased representation of the discrete RPM is used. For models constructed from such discrete RPMs, we consider the implications for generic particle-based inference methods in probabilistic programming systems. To demonstrate, we implement SMC for Normalized Inverse Gaussian Process mixture models in Turing.

  @article{bloemreddy2017rpm,
    title = {Sampling and inference for discrete random probability measures in probabilistic programs},
    author = {Bloem-Reddy, Benjamin and Mathieu, Emile and Foster, Adam and Rainforth, Tom and Ge, Hong and Lomelí, María and Ghahramani, Zoubin and Teh, Yee Whye},
    journal = {NIPS Workshop on Advances in Approximate Bayesian Inference},
    year = {2017}
  }
  

• P. Rebeschini , S. C. Tatikonda , Accelerated consensus via Min-Sum Splitting, in Advances in Neural Information Processing Systems 30, I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett, Eds. Curran Associates, Inc., 2017, 1374–1384.
  
  @incollection{NIPS2017_6736,
    author = {Rebeschini, Patrick and Tatikonda, Sekhar C},
    booktitle = {Advances in Neural Information Processing Systems 30},
    editor = {Guyon, I. and Luxburg, U. V. and Bengio, S. and Wallach, H. and Fergus, R. and Vishwanathan, S. and Garnett, R.},
    pages = {1374--1384},
    publisher = {Curran Associates, Inc.},
    title = {Accelerated consensus via Min-Sum Splitting},
    year = {2017},
    bdsk-url-1 = {http://papers.nips.cc/paper/6736-accelerated-consensus-via-min-sum-splitting.pdf}
  }
  

• F. Caron , J. Rousseau , On sparsity and power-law properties of graphs based on exchangeable point processes, arXiv preprint arXiv:1708.03120, 2017.
  

  @article{caron2017sparsity,
    title = {On sparsity and power-law properties of graphs based on exchangeable point processes},
    author = {Caron, Fran{\c{c}}ois and Rousseau, Judith},
    journal = {arXiv preprint arXiv:1708.03120},
    year = {2017}
  }
  

• S. Donnet , V. Rivoirard , J. Rousseau , C. Scricciolo , . others , Posterior concentration rates for counting processes with Aalen multiplicative intensities, Bayesian Analysis, vol. 12, no. 1, 53–87, 2017.
  

  @article{donnet2017posterior,
    title = {Posterior concentration rates for counting processes with Aalen multiplicative intensities},
    author = {Donnet, Sophie and Rivoirard, Vincent and Rousseau, Judith and Scricciolo, Catia and others},
    journal = {Bayesian Analysis},
    volume = {12},
    number = {1},
    pages = {53--87},
    year = {2017},
    publisher = {International Society for Bayesian Analysis}
  }
  

• N. Bochkina , J. Rousseau , . others , Adaptive density estimation based on a mixture of Gammas, Electronic Journal of Statistics, vol. 11, no. 1, 916–962, 2017.
  

  @article{bochkina2017adaptive,
    title = {Adaptive density estimation based on a mixture of Gammas},
    author = {Bochkina, Natalia and Rousseau, Judith and others},
    journal = {Electronic Journal of Statistics},
    volume = {11},
    number = {1},
    pages = {916--962},
    year = {2017},
    publisher = {The Institute of Mathematical Statistics and the Bernoulli Society}
  }
  

• T. G. J. Rudner , D. Sejdinovic , Inter-domain Deep Gaussian Processes, NeurIPS 2017 Workshop on Bayesian Deep Learning, 2017.
  
  @article{Rudner:Sejdinovic:2017,
    author = {Rudner, Tim G. J. and Sejdinovic, Dino},
    title = {{I}nter-domain {D}eep {G}aussian {P}rocesses},
    journal = {NeurIPS 2017 Workshop on Bayesian Deep Learning},
    year = {2017}
  }
  

• T. Rukat , C. C. Holmes , M. K. Titsias , C. Yau , Bayesian Boolean Matrix Factorisation, 2017.
  
  Boolean matrix factorisation aims to decompose a binary data matrix into an approximate Boolean product of two low rank, binary matrices: one containing meaningful patterns, the other quantifying how the observations can be expressed as a combination of these patterns. We introduce the OrMachine, a probabilistic generative model for Boolean matrix factorisation and derive a Metropolised Gibbs sampler that facilitates efficient parallel posterior inference. On real world and simulated data, our method outperforms all currently existing approaches for Boolean matrix factorisation and completion. This is the first method to provide full posterior inference for Boolean Matrix factorisation which is relevant in applications, e.g. for controlling false positive rates in collaborative filtering and, crucially, improves the interpretability of the inferred patterns. The proposed algorithm scales to large datasets as we demonstrate by analysing single cell gene expression data in 1.3 million mouse brain cells across 11 thousand genes on commodity hardware.

  @article{rukat2017_bayes-boolean,
    title = {Bayesian Boolean Matrix Factorisation},
    author = {Rukat, Tammo and Holmes, Chris C. and Titsias, Michalis K. and Yau, Christopher},
    archiveprefix = {arXiv},
    year = {2017},
    eprint = {1702.06166},
    primaryclass = {stat.ML}
  }
  

• M. Groß , U. Rendtel , T. Schmid , S. Schmon , N. Tzavidis , Estimating the density of ethnic minorities and aged people in Berlin: multivariate kernel density estimation applied to sensitive georeferenced administrative data protected via measurement error, Journal of the Royal Statistical Society: Series A (Statistics in Society), 2017.
  

  @article{GrosRendtelSchmidEtAl2017,
    author = {Gro{\ss}, Marcus and Rendtel, Ulrich and Schmid, Timo and Schmon, Sebastian and Tzavidis, Nikos},
    title = {Estimating the density of ethnic minorities and aged people in Berlin: multivariate kernel density estimation applied to sensitive georeferenced administrative data protected via measurement error},
    journal = {Journal of the Royal Statistical Society: Series A (Statistics in Society)},
    year = {2017}
  }
  

• S. Flaxman , Y. Teh , D. Sejdinovic , Poisson Intensity Estimation with Reproducing Kernels, Electronic Journal of Statistics, vol. 11, no. 2, 5081–5104, 2017.
  
  Project: bigbayes

  Despite the fundamental nature of the inhomogeneous Pois- son process in the theory and application of stochastic processes, and its attractive generalizations (e.g. Cox process), few tractable nonparametric modeling approaches of intensity functions exist, especially when observed points lie in a high-dimensional space. In this paper we develop a new, computationally tractable Reproducing Kernel Hilbert Space (RKHS) for- mulation for the inhomogeneous Poisson process. We model the square root of the intensity as an RKHS function. Whereas RKHS models used in su- pervised learning rely on the so-called representer theorem, the form of the inhomogeneous Poisson process likelihood means that the representer theorem does not apply. However, we prove that the representer theorem does hold in an appropriately transformed RKHS, guaranteeing that the optimization of the penalized likelihood can be cast as a tractable finite- dimensional problem. The resulting approach is simple to implement, and readily scales to high dimensions and large-scale datasets.

  @article{FlaTehSej2017ejs,
    author = {Flaxman, S. and Teh, Y.W. and Sejdinovic, D.},
    title = {{{Poisson Intensity Estimation with Reproducing Kernels}}},
    journal = {Electronic Journal of Statistics},
    year = {2017},
    volume = {11},
    number = {2},
    pages = {5081--5104}
  }
  

• Q. Zhang , S. Filippi , S. Flaxman , D. Sejdinovic , Feature-to-Feature Regression for a Two-Step Conditional Independence Test, in Uncertainty in Artificial Intelligence (UAI), 2017.
  
  Project: bigbayes

  The algorithms for causal discovery and more broadly for learning the structure of graphical models require well calibrated and consistent conditional independence (CI) tests. We revisit the CI tests which are based on two-step procedures and involve regression with subsequent (unconditional) independence test (RESIT) on regression residuals and investigate the assumptions under which these tests operate. In particular, we demonstrate that when going beyond simple functional relationships with additive noise, such tests can lead to an inflated number of false discoveries. We study the relationship of these tests with those based on dependence measures using reproducing kernel Hilbert spaces (RKHS) and propose an extension of RESIT which uses RKHS-valued regression. The resulting test inherits the simple two-step testing procedure of RESIT, while giving correct Type I control and competitive power. When used as a component of the PC algorithm, the proposed test is more robust to the case where hidden variables induce a switching behaviour in the associations present in the data.

  @inproceedings{ZhaFilFlaSej2017,
    author = {Zhang, Q. and Filippi, S. and Flaxman, S. and Sejdinovic, D.},
    title = {{Feature-to-Feature Regression for a Two-Step Conditional Independence Test}},
    booktitle = {Uncertainty in Artificial Intelligence (UAI)},
    year = {2017}
  }
  

• J. Mitrovic , D. Sejdinovic , Y. W. Teh , Deep Kernel Machines via the Kernel Reparametrization Trick, in International Conference on Learning Representations (ICLR) Workshop Track, 2017.
  
  Project: bigbayes

  While deep neural networks have achieved state-of-the-art performance on many tasks across varied domains, they still remain black boxes whose inner workings are hard to interpret and understand. In this paper, we develop a novel method for efficiently capturing the behaviour of deep neural networks using kernels. In particular, we construct a hierarchy of increasingly complex kernels that encode individual hidden layers of the network. Furthermore, we discuss how our framework motivates a novel supervised weight initialization method that discovers highly discriminative features already at initialization.

  @inproceedings{MitSejTeh2017,
    author = {Mitrovic, J. and Sejdinovic, D. and Teh, Y. W.},
    booktitle = {International Conference on Learning Representations (ICLR) Workshop Track},
    title = {{Deep Kernel Machines via the Kernel Reparametrization Trick}},
    year = {2017},
    bdsk-url-1 = {https://openreview.net/forum?id=Bkiqt3Ntg&noteId=Bkiqt3Ntg}
  }
  

• H. Law , C. Yau , D. Sejdinovic , Testing and Learning on Distributions with Symmetric Noise Invariance, in Advances in Neural Information Processing Systems (NeurIPS), 2017, 1343–1353.
  
  Kernel embeddings of distributions and the Maximum Mean Discrepancy (MMD), the resulting distance between distributions, are useful tools for fully nonparametric two-sample testing and learning on distributions. However, it is rarely that all possible differences between samples are of interest – discovered differences can be due to different types of measurement noise, data collection artefacts or other irrelevant sources of variability. We propose distances between distributions which encode invariance to additive symmetric noise, aimed at testing whether the assumed true underlying processes differ. Moreover, we construct invariant features of distributions, leading to learning algorithms robust to the impairment of the input distributions with symmetric additive noise. Such features lend themselves to a straightforward neural network implementation and can thus also be learned given a supervised signal.

  @inproceedings{LawYauSej2017,
    author = {Law, H.C.L. and Yau, C. and Sejdinovic, D.},
    title = {{{Testing and Learning on Distributions with Symmetric Noise Invariance}}},
    booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
    year = {2017},
    pages = {1343--1353}
  }
  

• J. Runge , P. Nowack , M. Kretschmer , S. Flaxman , D. Sejdinovic , Detecting Causal Associations in Large Nonlinear Time Series Datasets, ArXiv e-prints:1702.07007, 2017.
  
  Detecting causal associations in time series datasets is a key challenge for novel insights into complex dynamical systems such as the Earth system or the human brain. Interactions in high-dimensional dynamical systems often involve time-delays, nonlinearity, and strong autocorrelations. These present major challenges for causal discovery techniques such as Granger causality leading to low detection power, biases, and unreliable hypothesis tests. Here we introduce a reliable and fast method that outperforms current approaches in detection power and scales up to high-dimensional datasets. It overcomes detection biases, especially when strong autocorrelations are present, and allows ranking associations in large-scale analyses by their causal strength. We provide mathematical proofs, evaluate our method in extensive numerical experiments, and illustrate its capabilities in a large-scale analysis of the global surface-pressure system where we unravel spurious associations and find several potentially causal links that are difficult to detect with standard methods. The broadly applicable method promises to discover novel causal insights also in many other fields of science.

  @unpublished{RunSejFla2017,
    author = {Runge, J. and Nowack, P. and Kretschmer, M. and Flaxman, S. and Sejdinovic, D.},
    title = {{{Detecting Causal Associations in Large Nonlinear Time Series Datasets}}},
    journal = {ArXiv e-prints:1702.07007},
    year = {2017}
  }
  

• I. Schuster , H. Strathmann , B. Paige , D. Sejdinovic , Kernel Sequential Monte Carlo, in European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD), 2017.
  
  Bayesian posterior inference with Monte Carlo methods has a fundamental role in statistics and probabilistic machine learning. Target posterior distributions arising in increasingly complex models often exhibit high degrees of nonlinearity and multimodality and pose substantial challenges to traditional samplers. We propose the Kernel Sequential Monte Carlo (KSMC) framework for building emulator models of the current particle system in a Reproducing Kernel Hilbert Space and use the emulator’s geometry to inform local proposals. KSMC is applicable when gradients are unknown or prohibitively expensive and inherits the superior performance of SMC on multi-modal targets and its ability to estimate model evidence. Strengths of the proposed methodology are demonstrated on a series of challenging synthetic and real-world examples.

  @inproceedings{SchStrPaiSej2017,
    author = {Schuster, I. and Strathmann, H. and Paige, B. and Sejdinovic, D.},
    booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD)},
    title = {{Kernel Sequential Monte Carlo}},
    year = {2017}
  }
  

• S. Flaxman , Y. W. Teh , D. Sejdinovic , Poisson Intensity Estimation with Reproducing Kernels, in Artificial Intelligence and Statistics (AISTATS), 2017.
  
  Project: bigbayes

  Despite the fundamental nature of the inhomogeneous Poisson process in the theory and application of stochastic processes, and its attractive generalizations (e.g. Cox process), few tractable nonparametric modeling approaches of intensity functions exist, especially in high dimensional settings. In this paper we develop a new, computationally tractable Reproducing Kernel Hilbert Space (RKHS) formulation for the inhomogeneous Poisson process. We model the square root of the intensity as an RKHS function. The modeling challenge is that the usual representer theorem arguments no longer apply due to the form of the inhomogeneous Poisson process likelihood. However, we prove that the representer theorem does hold in an appropriately transformed RKHS, guaranteeing that the optimization of the penalized likelihood can be cast as a tractable finite-dimensional problem. The resulting approach is simple to implement, and readily scales to high dimensions and large-scale datasets.

  @inproceedings{FlaTehSej2017,
    author = {Flaxman, S. and Teh, Y. W. and Sejdinovic, D.},
    booktitle = {Artificial Intelligence and Statistics (AISTATS)},
    title = {{Poisson Intensity Estimation with Reproducing Kernels}},
    year = {2017}
  }
  

• Y. W. Teh , V. Bapst , W. M. Czarnecki , J. Quan , J. Kirkpatrick , R. Hadsell , N. Heess , R. Pascanu , Distral: Robust multitask reinforcement learning, in Advances in Neural Information Processing Systems (NeurIPS), 2017.
  
  Most deep reinforcement learning algorithms are data inefficient in complex and rich environments, limiting their applicability to many scenarios. One direction for improving data efficiency is multitask learning with shared neural network parameters, where efficiency may be improved through transfer across related tasks. In practice, however, this is not usually observed, because gradients from different tasks can interfere negatively, making learning unstable and sometimes even less data efficient. Another issue is the different reward schemes between tasks, which can easily lead to one task dominating the learning of a shared model. We propose a new approach for joint training of multiple tasks, which we refer to as Distral (Distill & transfer learning). Instead of sharing parameters between the different workers, we propose to share a "distilled" policy that captures common behaviour across tasks. Each worker is trained to solve its own task while constrained to stay close to the shared policy, while the shared policy is trained by distillation to be the centroid of all task policies. Both aspects of the learning process are derived by optimizing a joint objective function. We show that our approach supports efficient transfer on complex 3D environments, outperforming several related methods. Moreover, the proposed learning process is more robust and more stable—attributes that are critical in deep reinforcement learning.

  @inproceedings{TehBapCza2017a,
    author = {Teh, Y. W. and Bapst, V. and Czarnecki, W. M. and Quan, J. and Kirkpatrick, J. and Hadsell, R. and Heess, N. and Pascanu, R.},
    booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
    title = {Distral: Robust multitask reinforcement learning},
    year = {2017}
  }
  

• C. J. Maddison , D. Lawson , G. Tucker , N. Heess , M. Norouzi , A. Mnih , A. Doucet , Y. W. Teh , Particle Value Functions, in ICLR 2017 Workshop Proceedings, 2017.
  
  Project: deepmind

  The policy gradients of the expected return objective can react slowly to rare rewards. Yet, in some cases agents may wish to emphasize the low or high returns regardless of their probability. Borrowing from the economics and control literature, we review the risk-sensitive value function that arises from an exponential utility and illustrate its effects on an example. This risk-sensitive value function is not always applicable to reinforcement learning problems, so we introduce the particle value function defined by a particle filter over the distributions of an agent’s experience, which bounds the risk-sensitive one. We illustrate the benefit of the policy gradients of this objective in Cliffworld.

  @inproceedings{MadLawTuc2017a,
    author = {Maddison, C. J. and Lawson, D. and Tucker, G. and Heess, N. and Norouzi, M. and Mnih, A. and Doucet, A. and Teh, Y. W.},
    booktitle = {ICLR 2017 Workshop Proceedings},
    note = {ArXiv e-prints: 1703.05820},
    title = {Particle Value Functions},
    year = {2017},
    bdsk-url-1 = {https://arxiv.org/pdf/1705.09279v1.pdf}
  }
  

• M. Lomeli , S. Favaro , Y. W. Teh , A Marginal Sampler for σ-Stable Poisson-Kingman Mixture Models, Journal of Computational and Graphical Statistics, 2017.
  
  Project: bigbayes

  We investigate the class of σ-stable Poisson-Kingman random probability measures (RPMs) in the context of Bayesian nonparametric mixture modeling. This is a large class of discrete RPMs which encompasses most of the popular discrete RPMs used in Bayesian nonparametrics, such as the Dirichlet process, Pitman-Yor process, the normalized inverse Gaussian process and the normalized generalized Gamma process. We show how certain sampling properties and marginal characterisations of σ-stable Poisson-Kingman RPMs can be usefully exploited for devising a Markov chain Monte Carlo (MCMC) algorithm for performing posterior inference with a Bayesian nonparametric mixture model. Specifically, we introduce a novel and efficient MCMC sampling scheme in an augmented space that has a fixed number of auxiliary variables per iteration. We apply our sampling scheme to a density estimation and clustering tasks with unidimensional and multidimensional datasets, and compare it against competing MCMC sampling schemes.

  @article{LomFavTeh2015b,
    author = {Lomeli, M. and Favaro, S. and Teh, Y. W.},
    doi = {10.1080/10618600.2015.1110526},
    journal = {Journal of Computational and Graphical Statistics},
    title = {A Marginal Sampler for {$\sigma$}-Stable {P}oisson-{K}ingman Mixture Models},
    year = {2017},
    bdsk-url-1 = {http://www.tandfonline.com/doi/abs/10.1080/10618600.2015.1110526},
    bdsk-url-2 = {http://dx.doi.org/10.1080/10618600.2015.1110526},
    bdsk-url-3 = {https://arxiv.org/pdf/1407.4211v3.pdf}
  }
  

• S. J. Greenhill , C. Wu , X. Hua , M. Dunn , S. C. Levinson , R. D. Gray , Evolutionary dynamics of language systems, Proceedings of the National Academy of Sciences, 201700388, 2017.
  

  @article{greenhill2017evolutionary,
    title = {Evolutionary dynamics of language systems},
    author = {Greenhill, S. J. and Wu, C.-H. and Hua, X. and Dunn, M. and Levinson, S. C. and Gray, R. D.},
    journal = {Proceedings of the National Academy of Sciences},
    pages = {201700388},
    year = {2017},
    publisher = {National Acad Sciences}
  }
  

2016

• S. Bhatt , E. Cameron , S. Flaxman , D. J. Weiss , D. L. Smith , P. W. Gething , Improved prediction accuracy for disease risk mapping using Gaussian Process stacked generalisation, Dec-2016.
  
  Maps of infectious disease—charting spatial variations in the force of infection, degree of endemicity, and the burden on human health—provide an essential evidence base to support planning towards global health targets. Contemporary disease mapping efforts have embraced statistical modelling approaches to properly acknowledge uncertainties in both the available measurements and their spatial interpolation. The most common such approach is that of Gaussian process regression, a mathematical framework comprised of two components: a mean function harnessing the predictive power of multiple independent variables, and a covariance function yielding spatio-temporal shrinkage against residual variation from the mean. Though many techniques have been developed to improve the flexibility and fitting of the covariance function, models for the mean function have typically been restricted to simple linear terms. For infectious diseases, known to be driven by complex interactions between environmental and socio-economic factors, improved modelling of the mean function can greatly boost predictive power. Here we present an ensemble approach based on stacked generalisation that allows for multiple, non-linear algorithmic mean functions to be jointly embedded within the Gaussian process framework. We apply this method to mapping Plasmodium falciparum prevalence data in Sub-Saharan Africa and show that the generalised ensemble approach markedly out-performs any individual method.

  @unpublished{bhattetal2016,
    author = {Bhatt, S. and Cameron, E. and Flaxman, Seth and Weiss, D. J. and Smith, D. L. and Gething, P. W.},
    title = {Improved prediction accuracy for disease risk mapping using Gaussian Process stacked generalisation},
    note = {ArXiv e-prints: 1612.03278},
    year = {2016},
    month = dec
  }
  

• F. C. Chan , R. Kridel , A. Mottok , M. Boyle , P. Farinha , K. Tan , B. Meissner , A. Bashashati , A. McPherson , A. Roth , . others , Divergent Modes of Tumor Evolution Underlie Histological Transformation and Early Progression of Follicular Lymphoma, Dec. 2016.
  

  @article{chan2016divergent,
    author = {Chan, Fong Chun and Kridel, Robert and Mottok, Anja and Boyle, Merrill and Farinha, Pedro and Tan, King and Meissner, Barbara and Bashashati, Ali and McPherson, Andrew and Roth, Andrew and others},
    month = dec,
    publisher = {Am Soc Hematology},
    title = {{Divergent Modes of Tumor Evolution Underlie Histological Transformation and Early Progression of Follicular Lymphoma}},
    year = {2016}
  }
  

• R. Kridel , F. C. Chan , A. Mottok , M. Boyle , P. Farinha , K. Tan , B. Meissner , A. Bashashati , A. McPherson , A. Roth , . others , Histological Transformation and Progression in Follicular Lymphoma: A Clonal Evolution Study, PLoS Medicine, vol. 13, no. 12, e1002197, Dec. 2016.
  

  @article{kridel2016histological,
    author = {Kridel, Robert and Chan, Fong Chun and Mottok, Anja and Boyle, Merrill and Farinha, Pedro and Tan, King and Meissner, Barbara and Bashashati, Ali and McPherson, Andrew and Roth, Andrew and others},
    journal = {PLoS Medicine},
    month = dec,
    number = {12},
    pages = {e1002197},
    publisher = {Public Library of Science},
    title = {{Histological Transformation and Progression in Follicular Lymphoma: A Clonal Evolution Study}},
    volume = {13},
    year = {2016}
  }
  

• S. Flaxman , D. Sutherland , Y. Wang , Y. W. Teh , Understanding the 2016 US Presidential Election using ecological inference and distribution regression with census microdata, Arxiv e-prints, Nov-2016.
  
  Project: bigbayes

  We combine fine-grained spatially referenced census data with the vote outcomes from the 2016 US presidential election. Using this dataset, we perform ecological inference using distribution regression (Flaxman et al, KDD 2015) with a multinomial-logit regression so as to model the vote outcome Trump, Clinton, Other / Didn’t vote as a function of demographic and socioeconomic features. Ecological inference allows us to estimate "exit poll" style results like what was Trump’s support among white women, but for entirely novel categories. We also perform exploratory data analysis to understand which census variables are predictive of voting for Trump, voting for Clinton, or not voting for either. All of our methods are implemented in python and R and are available online for replication.

  @unpublished{flaxsuthetal2016,
    author = {Flaxman, Seth and Sutherland, Dougal and Wang, Yu-Xiang and Teh, Yee Whye},
    title = {Understanding the 2016 US Presidential Election using ecological inference and distribution regression with census microdata},
    journal = {Arxiv e-prints},
    note = {ArXiv e-prints: 1611.03787},
    year = {2016},
    month = nov
  }
  

• K. Palla , F. Caron , Y. W. Teh , A Bayesian nonparametric model for sparse dynamic networks, Jun-2016.
  
  Project: bigbayes

  We propose a Bayesian nonparametric prior for time-varying networks. To each node of the network is associated a positive parameter, modeling the sociability of that node. Sociabilities are assumed to evolve over time, and are modeled via a dynamic point process model. The model is able to (a) capture smooth evolution of the interaction between nodes, allowing edges to appear/disappear over time (b) capture long term evolution of the sociabilities of the nodes (c) and yield sparse graphs, where the number of edges grows subquadratically with the number of nodes. The evolution of the sociabilities is described by a tractable time-varying gamma process. We provide some theoretical insights into the model and apply it to three real world datasets.

  @unpublished{PallaCaronTeh2016,
    author = {Palla, Konstantina and Caron, Francois and Teh, Yee Whye},
    title = {A Bayesian nonparametric model for sparse dynamic networks},
    note = {ArXiv e-prints: 1607.01624},
    archiveprefix = {arXiv},
    year = {2016},
    month = jun
  }
  

• P. Rebeschini , S. Tatikonda , Decay of correlation in network flow problems, in 2016 Annual Conference on Information Science and Systems (CISS), 2016, 169–174.
  

  @inproceedings{7460496,
    author = {Rebeschini, P. and Tatikonda, S.},
    booktitle = {2016 Annual Conference on Information Science and Systems (CISS)},
    doi = {10.1109/CISS.2016.7460496},
    keywords = {graph theory;matrix algebra;optimisation;constrained network optimization problems;exponential rate;graph-theoretical distance;killed random walk;local sensitivity;network flow problems;substochastic transition matrix;Convex functions;Correlation;Information science;Linear programming;Optimization;Radio frequency;Sensitivity analysis;decay of correlation;killed random walk;network flow;sensitivity of optimal point},
    month = mar,
    pages = {169-174},
    title = {Decay of correlation in network flow problems},
    year = {2016},
    bdsk-url-1 = {http://dx.doi.org/10.1109/CISS.2016.7460496}
  }
  

• A. Roth , A. McPherson , E. Laks , T. Masud , A. Bashashati , A. W. Zhang , G. Ha , J. Biele , D. Yap , A. Wan , L. M. Prentice , J. Khattra , M. Smith , C. Nielsen , S. C. Mullaly , S. Kalloger , A. Karnezis , K. Shumansky , C. Siu , J. Rosner , H. L. Chan , J. Ho , N. Melnyk , J. Senz , W. Yang , R. Moore , A. Mungall , M. A. Marra , A. Bouchard-Côté , C. B. Gilks , D. G. Huntsman , J. N. McAlpine , S. Aparicio , S. P. Shah , Divergent Modes of Clonal Spread and Intraperitoneal Mixing in High-Grade Serous Ovarian Cancer, Nature Genetics, Mar. 2016.
  

  @article{ith,
    author = {Roth, Andrew and McPherson, Andrew and Laks, Emma and Masud, Tehmina and Bashashati, Ali and Zhang, Allen W. and Ha, Gavin and Biele, Justina and Yap, Damian and Wan, Adrian and Prentice, Leah M. and Khattra, Jaswinder and Smith, Maia and Nielsen, Cydney and Mullaly, Sarah C. and Kalloger, Steve and Karnezis, Anthony and Shumansky, Karey and Siu, Celia and Rosner, Jamie and Chan, Hector Li and Ho, Julie and Melnyk, Nataliya and Senz, Janine and Yang, Winnie and Moore, Richard and Mungall, Andrew and Marra, Marco A. and Bouchard-C{\^o}t{\'e}, Alexandre and Gilks, C. Blake and Huntsman, David G. and McAlpine, Jessica N. and Aparicio, Samuel and Shah, Sohrab P},
    journal = {Nature Genetics},
    month = mar,
    title = {{Divergent Modes of Clonal Spread and Intraperitoneal Mixing in High-Grade Serous Ovarian Cancer}},
    year = {2016}
  }
  

• A. Roth , A. McPherson , E. Laks , J. Biele , D. Yap , A. Wan , J. N. McAlpine , S. Aparicio , A. Bouchard-Côté , S. P. Shah , Simultaneous inference of clonal genotypes and population structure from single cell tumour sequencing, Nature Methods, Mar. 2016.
  

  @article{scg,
    author = {Roth, Andrew and McPherson, Andrew and Laks, Emma and Biele, Justina and Yap, Damian and Wan, Adrian and McAlpine, Jessica N. and Aparicio, Samuel and Bouchard-C{\^o}t{\'e}, Alexandre and Shah, Sohrab P},
    journal = {Nature Methods},
    month = mar,
    title = {{Simultaneous inference of clonal genotypes and population structure from single cell tumour sequencing}},
    year = {2016}
  }
  

• A. S. Morrissy , L. Garzia , D. J. H. Shih , S. Zuyderduyn , X. Huang , P. Skowron , M. Remke , F. M. G. Cavalli , V. Ramaswamy , P. E. Lindsay , S. Jelveh , L. K. Donovan , X. Wang , B. Luu , K. Zayne , Y. Li , C. Mayoh , N. Thiessen , E. Mercier , K. L. Mungall , Y. Ma , K. Tse , T. Zeng , K. Shumansky , A. J. L. Roth , S. Shah , H. Farooq , N. Kijima , B. L. Holgado , J. J. Y. Lee , S. Matan-Lithwick , J. Liu , S. C. Mack , A. Manno , K. A. Michealraj , C. Nor , J. Peacock , L. Qin , J. Reimand , A. Rolider , Y. Y. Thompson , X. Wu , T. Pugh , A. Ally , M. Bilenky , Y. S. N. Butterfield , R. Carlsen , Y. Cheng , E. Chuah , R. D. Corbett , N. Dhalla , A. He , D. Lee , H. I. Li , W. Long , M. Mayo , P. Plettner , J. Q. Qian , J. E. Schein , A. Tam , T. Wong , I. Birol , Y. Zhao , C. C. Faria , J. Pimentel , S. Nunes , T. Shalaby , M. Grotzer , I. F. Pollack , R. L. Hamilton , X. Li , A. E. Bendel , D. W. Fults , A. W. Walter , T. Kumabe , T. Tominaga , V. P. Collins , Y. Cho , C. Hoffman , D. Lyden , J. H. Wisoff , J. J. H. Garvin , D. S. Stearns , L. Massimi , U. Schüller , J. Sterba , K. Zitterbart , S. Puget , O. Ayrault , S. E. Dunn , D. P. C. Tirapelli , C. G. Carlotti , H. Wheeler , A. R. Hallahan , W. Ingram , T. J. MacDonald , J. J. Olson , E. G. Van Meir , J. Lee , K. Wang , S. Kim , B. Cho , T. Pietsch , G. Fleischhack , S. Tippelt , Y. S. Ra , S. Bailey , J. C. Lindsey , S. C. Clifford , C. G. Eberhart , M. K. Cooper , R. J. Packer , M. Massimino , M. L. Garre , U. Bartels , U. Tabori , C. E. Hawkins , P. Dirks , E. Bouffet , J. T. Rutka , R. J. Wechsler-Reya , W. A. Weiss , L. S. Collier , A. J. Dupuy , A. Korshunov , D. T. W. Jones , M. Kool , P. A. Northcott , S. M. Pfister , D. A. Largaespada , A. J. Mungall , R. A. Moore , N. Jabado , G. D. Bader , S. J. M. Jones , D. Malkin , M. A. Marra , M. D. Taylor , Divergent clonal selection dominates medulloblastoma at recurrence., Nature, vol. 529, no. 7586, 351–357, Jan. 2016.
  

  @article{MorrissyGarziaShihEtAl2016,
    author = {Morrissy, A Sorana and Garzia, Livia and Shih, David J H. and Zuyderduyn, Scott and Huang, Xi and Skowron, Patryk and Remke, Marc and Cavalli, Florence M G. and Ramaswamy, Vijay and Lindsay, Patricia E. and Jelveh, Salomeh and Donovan, Laura K. and Wang, Xin and Luu, Betty and Zayne, Kory and Li, Yisu and Mayoh, Chelsea and Thiessen, Nina and Mercier, Eloi and Mungall, Karen L. and Ma, Yusanne and Tse, Kane and Zeng, Thomas and Shumansky, Karey and Roth, Andrew J L. and Shah, Sohrab and Farooq, Hamza and Kijima, Noriyuki and Holgado, Borja L. and Lee, John J Y. and Matan-Lithwick, Stuart and Liu, Jessica and Mack, Stephen C. and Manno, Alex and Michealraj, K. A. and Nor, Carolina and Peacock, John and Qin, Lei and Reimand, Juri and Rolider, Adi and Thompson, Yuan Y. and Wu, Xiaochong and Pugh, Trevor and Ally, Adrian and Bilenky, Mikhail and Butterfield, Yaron S N. and Carlsen, Rebecca and Cheng, Young and Chuah, Eric and Corbett, Richard D. and Dhalla, Noreen and He, An and Lee, Darlene and Li, Haiyan I. and Long, William and Mayo, Michael and Plettner, Patrick and Qian, Jenny Q. and Schein, Jacqueline E. and Tam, Angela and Wong, Tina and Birol, Inanc and Zhao, Yongjun and Faria, Claudia C. and Pimentel, Jos{\'e} and Nunes, Sofia and Shalaby, Tarek and Grotzer, Michael and Pollack, Ian F. and Hamilton, Ronald L. and Li, Xiao-Nan and Bendel, Anne E. and Fults, Daniel W. and Walter, Andrew W. and Kumabe, Toshihiro and Tominaga, Teiji and Collins, V Peter and Cho, Yoon-Jae and Hoffman, Caitlin and Lyden, David and Wisoff, Jeffrey H. and Garvin, Jr James H and Stearns, Duncan S. and Massimi, Luca and Sch{\"u}ller, Ulrich and Sterba, Jaroslav and Zitterbart, Karel and Puget, Stephanie and Ayrault, Olivier and Dunn, Sandra E. and Tirapelli, Daniela P C. and Carlotti, Carlos G. and Wheeler, Helen and Hallahan, Andrew R. and Ingram, Wendy and MacDonald, Tobey J. and Olson, Jeffrey J. and {Van Meir}, Erwin G. and Lee, Ji-Yeoun and Wang, Kyu-Chang and Kim, Seung-Ki and Cho, Byung-Kyu and Pietsch, Torsten and Fleischhack, Gudrun and Tippelt, Stephan and Ra, Young Shin and Bailey, Simon and Lindsey, Janet C. and Clifford, Steven C. and Eberhart, Charles G. and Cooper, Michael K. and Packer, Roger J. and Massimino, Maura and Garre, Maria Luisa and Bartels, Ute and Tabori, Uri and Hawkins, Cynthia E. and Dirks, Peter and Bouffet, Eric and Rutka, James T. and Wechsler-Reya, Robert J. and Weiss, William A. and Collier, Lara S. and Dupuy, Adam J. and Korshunov, Andrey and Jones, David T W. and Kool, Marcel and Northcott, Paul A. and Pfister, Stefan M. and Largaespada, David A. and Mungall, Andrew J. and Moore, Richard A. and Jabado, Nada and Bader, Gary D. and Jones, Steven J M. and Malkin, David and Marra, Marco A. and Taylor, Michael D.},
    doi = {10.1038/nature16478},
    institution = {Division of Neurosurgery, The Hospital for Sick Children, Toronto, Ontario M5S 3E1, Canada.},
    journal = {Nature},
    month = jan,
    number = {7586},
    pages = {351--357},
    title = {{Divergent clonal selection dominates medulloblastoma at recurrence.}},
    volume = {529},
    exclude_group_member = {Lee, Wu},
    year = {2016}
  }
  

• B. Bloem-Reddy , J. P. Cunningham , Slice Sampling on Hamiltonian Trajectories, in International Conference on Machine Learning (ICML), 2016, vol. 33, 3050–3058.
  
  @inproceedings{BloemReddy:Cunningham:2016,
    author = {Bloem-Reddy, Benjamin and Cunningham, John P.},
    booktitle = {International Conference on Machine Learning (ICML)},
    pages = {3050--3058},
    title = {Slice Sampling on Hamiltonian Trajectories},
    volume = {33},
    year = {2016}
  }
  

• T. Gray-Davies , C. Holmes , F. Caron , Scalable Bayesian nonparametric regression via a Plackett-Luce model for conditional ranks, Electronic Journal of Statistics, vol. 10, 1807–1828, 2016.
  
  @article{Gray-Davies2016,
    title = {Scalable Bayesian nonparametric regression via a {Plackett-Luce} model for conditional ranks},
    author = {Gray-Davies, T. and Holmes, C. and Caron, F.},
    journal = {Electronic Journal of Statistics},
    year = {2016},
    pages = {1807-1828},
    volume = {10}
  }
  

• A. Caterini , D. E. Chang , A Geometric Framework for Convolutional Neural Networks, ArXiv e-prints:1608.04374, 2016.
  
  @unpublished{caterini2016geometric,
    title = {A Geometric Framework for Convolutional Neural Networks},
    author = {Caterini, A. and Chang, D. E.},
    journal = {ArXiv e-prints:1608.04374},
    year = {2016}
  }
  

• G. Deligiannidis , S. Utev , Optimal Bounds for the Variance of Self-Intersection Local Times, International Journal of Stochastic Analysis, vol. 2016, 2016.
  

  @article{deligiannidis2016optimal,
    author = {Deligiannidis, G. and Utev, S.},
    title = {Optimal Bounds for the Variance of Self-Intersection Local Times},
    journal = {International Journal of Stochastic Analysis},
    year = {2016},
    volume = {2016}
  }
  

• R. J. Evans , Graphs for margins of Bayesian networks, Scandinavian Journal of Statistics, vol. 43, no. 3, 625–648, 2016.
  

  @article{evans:mdags,
    author = {Evans, R. J.},
    journal = {Scandinavian Journal of Statistics},
    title = {Graphs for margins of {B}ayesian networks},
    archiveprefix = {arXiv},
    eprint = {1408.1809},
    volume = {43},
    issue = {3},
    pages = {625--648},
    year = {2016}
  }
  

• R. B. A. Silva , R. J. Evans , Causal Inference through a Witness Protection Program, Journal of Machine Learning Research, vol. 17, no. 56, 1–53, 2016.
  

  @article{silva:evans:16,
    title = {Causal Inference through a Witness Protection Program},
    author = {Silva, R. B. A. and Evans, R. J.},
    journal = {Journal of Machine Learning Research},
    volume = {17},
    issue = {56},
    pages = {1--53},
    year = {2016},
    archiveprefix = {arXiv},
    eprint = {1406.0531}
  }
  

• A. Hitz , R. J. Evans , One-Component Regular Variation and Graphical Modeling of Extremes, Journal of Applied Probability, vol. 53, no. 3, 733–746, 2016.
  

  @article{hitz:evans:16,
    title = {One-Component Regular Variation and Graphical Modeling of Extremes},
    author = {Hitz, A. and Evans, R. J.},
    journal = {Journal of Applied Probability},
    year = {2016},
    volume = {53},
    issue = {3},
    pages = {733-746}
  }
  

• S. Filippi , C. P. Barnes , P. D. W. Kirk , T. Kudo , K. Kunida , S. S. McMahon , T. Tsuchiya , T. Wada , S. Kuroda , M. P. H. Stumpf , Robustness of MEK-ERK Dynamics and Origins of Cell-to-Cell Variability in MAPK Signaling, CellReports, 2016.
  

  @article{Filippi:2016gs,
    author = {Filippi, Sarah and Barnes, Chris P and Kirk, Paul D W and Kudo, Takamasa and Kunida, Katsuyuki and McMahon, Siobhan S and Tsuchiya, Takaho and Wada, Takumi and Kuroda, Shinya and Stumpf, Michael P H},
    title = {{Robustness of MEK-ERK Dynamics and Origins of Cell-to-Cell Variability in MAPK Signaling}},
    journal = {CellReports},
    year = {2016}
  }
  

• S. Filippi , C. C. Holmes , L. E. Nieto-Barajas , Scalable Bayesian nonparametric measures for exploring pairwise dependence via Dirichlet Process Mixtures, Electronic Journal of Statistics, 2016.
  

  @article{Filippi:2016kg,
    author = {Filippi, Sarah and Holmes, Chris C and Nieto-Barajas, Luis E.},
    title = {{Scalable Bayesian nonparametric measures for exploring pairwise dependence via Dirichlet Process Mixtures}},
    journal = {Electronic Journal of Statistics},
    year = {2016}
  }
  

• S. Filippi , C. C. Holmes , A Bayesian Nonparametric Approach to Testing for Dependence Between Random Variables, Bayesian Analysis, 2016.
  

  @article{Filippi:2016di,
    author = {Filippi, Sarah and Holmes, Chris C},
    title = {{A Bayesian Nonparametric Approach to Testing for Dependence Between Random Variables}},
    journal = {Bayesian Analysis},
    year = {2016}
  }
  

• W. Herlands , A. Wilson , H. Nickisch , S. Flaxman , D. Neill , W. Van Panhuis , E. Xing , Scalable Gaussian Processes for Characterizing Multidimensional Change Surfaces, in Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, 2016, 1013–1021.
  
  Project: sgmcmc

  We present a scalable Gaussian process model for identifying and characterizing smooth multidimensional changepoints, and automatically learning changes in expressive covariance structure. We use Random Kitchen Sink features to flexibly define a change surface in combination with expressive spectral mixture kernels to capture the complex statistical structure. Finally, through the use of novel methods for additive non-separable kernels, we can scale the model to large datasets. We demonstrate the model on numerical and real world data, including a large spatio-temporal disease dataset where we identify previously unknown heterogeneous changes in space and time.

  @inproceedings{HerWilNicketal2016,
    author = {Herlands, William and Wilson, Andrew and Nickisch, Hannes and Flaxman, Seth and Neill, Daniel and Van Panhuis, Wilbert and Xing, Eric},
    title = {{Scalable Gaussian Processes} for Characterizing Multidimensional Change Surfaces},
    booktitle = {Proceedings of the 19th International Conference on Artificial Intelligence and Statistics},
    pages = {1013--1021},
    year = {2016}
  }
  

• P. G. Bissiri , C. Holmes , S. G. Walker , A general framework for updating belief distributions, Journal of the Royal Statistical Society: Series B (Statistical Methodology), 2016.
  

  @article{bissiri2016general,
    title = {A general framework for updating belief distributions},
    author = {Bissiri, Pier Giovanni and Holmes, CC and Walker, Stephen G},
    journal = {Journal of the Royal Statistical Society: Series B (Statistical Methodology)},
    year = {2016}
  }
  

• M. K. Titsias , C. C. Holmes , C. Yau , Statistical inference in hidden Markov models using k-segment constraints, Journal of the American Statistical Association, vol. 111, no. 513, 200–215, 2016.
  

  @article{titsias2016statistical,
    title = {Statistical inference in hidden Markov models using k-segment constraints},
    author = {Titsias, Michalis K and Holmes, Chris C and Yau, Christopher},
    journal = {Journal of the American Statistical Association},
    volume = {111},
    number = {513},
    pages = {200--215},
    year = {2016},
    publisher = {Taylor \& Francis}
  }
  

• J. Watson , C. C. Holmes , . others , Approximate models and robust decisions, Statistical Science, vol. 31, no. 4, 465–489, 2016.
  

  @article{watson2016approximate,
    title = {Approximate models and robust decisions},
    author = {Watson, James and Holmes, Chris C. and others},
    journal = {Statistical Science},
    volume = {31},
    number = {4},
    pages = {465--489},
    year = {2016},
    publisher = {Institute of Mathematical Statistics}
  }
  

• T. Gray-Davies , C. C. Holmes , F. Caron , . others , Scalable Bayesian nonparametric regression via a Plackett-Luce model for conditional ranks, Electronic Journal of Statistics, vol. 10, no. 2, 1807–1828, 2016.
  

  @article{gray2016scalable,
    title = {Scalable Bayesian nonparametric regression via a Plackett-Luce model for conditional ranks},
    author = {Gray-Davies, Tristan and Holmes, Chris C and Caron, Fran{\c{c}}ois and others},
    journal = {Electronic Journal of Statistics},
    volume = {10},
    number = {2},
    pages = {1807--1828},
    year = {2016},
    publisher = {The Institute of Mathematical Statistics and the Bernoulli Society}
  }
  

• S. Filippi , C. C. Holmes , L. E. Nieto-Barajas , . others , Scalable Bayesian nonparametric measures for exploring pairwise dependence via Dirichlet Process Mixtures, Electronic Journal of Statistics, vol. 10, no. 2, 3338–3354, 2016.
  

  @article{filippi2016scalable,
    title = {Scalable Bayesian nonparametric measures for exploring pairwise dependence via Dirichlet Process Mixtures},
    author = {Filippi, Sarah and Holmes, Chris C and Nieto-Barajas, Luis E and others},
    journal = {Electronic Journal of Statistics},
    volume = {10},
    number = {2},
    pages = {3338--3354},
    year = {2016},
    publisher = {The Institute of Mathematical Statistics and the Bernoulli Society}
  }
  

• Z. Wang , J. S. Morris , C. C. Holmes , J. Ahn , B. Li , W. Lu , X. Tang , I. I. Wistuba , W. Wang , Cell type-specific deconvolution of heterogeneous tumor samples with immune infiltration using expression data. American Association for Cancer Research, 2016.
  

  @misc{wang2016cell,
    title = {Cell type-specific deconvolution of heterogeneous tumor samples with immune infiltration using expression data},
    author = {Wang, Zeya and Morris, Jeffrey S and Holmes, Chris C and Ahn, Jaeil and Li, Bo and Lu, Wei and Tang, Ximing and Wistuba, Ignacio I and Wang, Wenyi},
    year = {2016},
    publisher = {American Association for Cancer Research}
  }
  

• M. Behr , C. C. Holmes , A. Munk , Multiscale Blind Source Separation, arXiv preprint arXiv:1608.07173, 2016.
  

  @article{behr2016multiscale,
    title = {Multiscale Blind Source Separation},
    author = {Behr, Merle and Holmes, Chris C. and Munk, Axel},
    journal = {arXiv preprint arXiv:1608.07173},
    year = {2016}
  }
  

• J. Watson , C. C. Holmes , . others , Rejoinder: Approximate Models and Robust Decisions, Statistical Science, vol. 31, no. 4, 516–520, 2016.
  

  @article{watson2016rejoinder,
    title = {Rejoinder: Approximate Models and Robust Decisions},
    author = {Watson, James and Holmes, Chris C. and others},
    journal = {Statistical Science},
    volume = {31},
    number = {4},
    pages = {516--520},
    year = {2016},
    publisher = {Institute of Mathematical Statistics}
  }
  

• H. Kim , X. Lu , S. Flaxman , Y. W. Teh , Collaborative Filtering with Side Information: a Gaussian Process Perspective, 2016.
  
  We tackle the problem of collaborative filtering (CF) with side information, through the lens of Gaussian Process (GP) regression. Driven by the idea of using the kernel to explicitly model user-item similarities, we formulate the GP in a way that allows the incorporation of low-rank matrix factorisation, arriving at our model, the Tucker Gaussian Process (TGP). Consequently, TGP generalises classical Bayesian matrix factorisation models, and goes beyond them to give a natural and elegant method for incorporating side information, giving enhanced predictive performance for CF problems. Moreover we show that it is a novel model for regression, especially well-suited to grid-structured data and problems where the dependence on covariates is close to being separable.

  @unpublished{kimluflateh16,
    title = {Collaborative Filtering with Side Information: a Gaussian Process Perspective},
    author = {Kim, H. and Lu, X. and Flaxman, S. and Teh, Y. W.},
    note = {ArXiv e-prints: 1605.07025},
    year = {2016}
  }
  

• J. Lee , L. F. James , S. Choi , Finite-dimensional BFRY priors and variational Bayesian inference for power law models, in Advances in Neural Information Processing Systems (NeurIPS), 2016.
  

  @inproceedings{Lee2016,
    title = {Finite-dimensional {BFRY} priors and variational {B}ayesian inference for power law models},
    author = {Lee, J. and James, L. F. and Choi, S.},
    booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
    year = {2016}
  }
  

• C. J. Maddison , A Poisson process model for Monte Carlo, in Perturbation, Optimization, and Statistics, T. Hazan, G. Papandreou, and D. Tarlow, Eds. MIT Press, 2016.
  
  Simulating samples from arbitrary probability distributions is a major research program of statistical computing. Recent work has shown promise in an old idea, that sampling from a discrete distribution can be accomplished by perturbing and maximizing its mass function. Yet, it has not been clearly explained how this research project relates to more traditional ideas in the Monte Carlo literature. This chapter addresses that need by identifying a Poisson process model that unifies the perturbation and accept-reject views of Monte Carlo simulation. Many existing methods can be analyzed in this framework. The chapter reviews Poisson processes and defines a Poisson process model for Monte Carlo methods. This model is used to generalize the perturbation trick to infinite spaces by constructing Gumbel processes, random functions whose maxima are located at samples over infinite spaces. The model is also used to analyze A* sampling and OS*, two methods from distinct Monte Carlo families.

  @incollection{maddison2016ppmontecarlo,
    author = {Maddison, Chris J.},
    booktitle = {Perturbation, Optimization, and Statistics},
    editor = {Hazan, Tamir and Papandreou, George and Tarlow, Daniel},
    publisher = {MIT Press},
    title = {{A Poisson process model for Monte Carlo}},
    year = {2016},
    bdsk-url-1 = {https://arxiv.org/pdf/1602.05986.pdf}
  }
  

• D. Silver , A. Huang , C. J. Maddison , A. Guez , L. Sifre , G. Driessche , J. Schrittwieser , I. Antonoglou , V. Panneershelvam , M. Lanctot , S. Dieleman , D. Grewe , J. Nham , N. Kalchbrenner , I. Sutskever , T. Lillicrap , M. Leach , K. Kavukcuoglu , T. Graepel , D. Hassabis , Mastering the game of Go with deep neural networks and tree search, Nature, vol. 529, no. 7587, 484–489, 2016.
  
  @article{silver2016alphago,
    author = {Silver, David and Huang, Aja and Maddison, Chris J. and Guez, Arthur and Sifre, Laurent and van den Driessche, George and Schrittwieser, Julian and Antonoglou, Ioannis and Panneershelvam, Veda and Lanctot, Marc and Dieleman, Sander and Grewe, Dominik and Nham, John and Kalchbrenner, Nal and Sutskever, Ilya and Lillicrap, Timothy and Leach, Madeleine and Kavukcuoglu, Koray and Graepel, Thore and Hassabis, Demis},
    journal = {Nature},
    number = {7587},
    pages = {484 - 489},
    title = {{Mastering the game of Go with deep neural networks and tree search}},
    volume = {529},
    year = {2016},
    bdsk-url-1 = {http://www.nature.com/nature/journal/v529/n7587/full/nature16961.html}
  }
  

• K. Märtens , J. Hallin , J. Warringer , G. Liti , L. Parts , Predicting quantitative traits from genome and phenome with near perfect accuracy, Nature Communications, vol. 7, 11512, 2016.
  

  @article{martens2016predicting,
    title = {Predicting quantitative traits from genome and phenome with near perfect accuracy},
    author = {Märtens, Kaspar and Hallin, Johan and Warringer, Jonas and Liti, Gianni and Parts, Leopold},
    journal = {Nature Communications},
    volume = {7},
    pages = {11512},
    year = {2016},
    publisher = {Nature Publishing Group}
  }
  

• J. Hallin , K. Märtens , A. I. Young , M. Zackrisson , F. Salinas , L. Parts , J. Warringer , G. Liti , Powerful decomposition of complex traits in a diploid model, Nature Communications, vol. 7, 13311, 2016.
  

  @article{hallin2016powerful,
    title = {Powerful decomposition of complex traits in a diploid model},
    author = {Hallin, Johan and Märtens, Kaspar and Young, Alexander I and Zackrisson, Martin and Salinas, Francisco and Parts, Leopold and Warringer, Jonas and Liti, Gianni},
    journal = {Nature Communications},
    volume = {7},
    pages = {13311},
    year = {2016},
    publisher = {Nature Publishing Group}
  }
  

• R. Kolde , K. Märtens , K. Lokk , S. Laur , J. Vilo , seqlm: an MDL based method for identifying differentially methylated regions in high density methylation array data, Bioinformatics, vol. 32, no. 17, 2604–2610, 2016.
  

  @article{kolde2016seqlm,
    title = {seqlm: an MDL based method for identifying differentially methylated regions in high density methylation array data},
    author = {Kolde, Raivo and Märtens, Kaspar and Lokk, Kaie and Laur, Sven and Vilo, Jaak},
    journal = {Bioinformatics},
    volume = {32},
    number = {17},
    pages = {2604--2610},
    year = {2016},
    publisher = {Oxford University Press}
  }
  

• E. Matechou , G. K. Nicholls , B. Morgan , J. Collazo , J. Lyons , Bayesian analysis of Jolly-Seber type models: Incorporating heterogeneity in arrival and departure, Environmental and Ecological Statistics, vol. 23, no. 4, 531–547, 2016.
  

  @article{Matechou2016531,
    author = {Matechou, E. and Nicholls, G. K. and Morgan, B.J.T. and Collazo, J.A. and Lyons, J.E.},
    title = {Bayesian analysis of Jolly-Seber type models: Incorporating heterogeneity in arrival and departure},
    journal = {Environmental and Ecological Statistics},
    year = {2016},
    volume = {23},
    number = {4},
    pages = {531-547},
    doi = {10.1007/s10651-016-0352-0},
    note = {cited By 0},
    document_type = {Article},
    source = {Scopus}
  }
  

• N. Heard , K. Palla , M. Skoularidou , Topic modelling of authentication events in an enterprise computer network, 2016.
  
  Project: bigbayes

  The possibility for theft or misuse of legitimate user credentials is a potential cyber-security weakness in any enterprise computer network which is almost impossible to eradicate. However, by monitoring the network traffic patterns, it can be possible to detect misuse of credentials. This article presents an initial investigation into deconvolving the mixture behaviour of several individuals within a network, to see if individual users can be identified. Towards that, a technique used for document classification is deployed, the Latent Dirichlet allocation model. A pilot study is conducted on authentication events taken from real data from the enterprise network of Los Alamos National Laboratory.

  @inproceedings{Heard:2016:10.1109/ISI.2016.7745466,
    author = {Heard, NA and Palla, K and Skoularidou, M},
    doi = {10.1109/ISI.2016.7745466},
    publisher = {IEEE},
    title = {Topic modelling of authentication events in an enterprise computer network},
    year = {2016}
  }
  

• T. Rainforth , T. A. Le , J. Meent , M. A. Osborne , F. Wood , Bayesian Optimization for Probabilistic Programs, in Advances in Neural Information Processing Systems, 2016, 280–288.
  
  We present the first general purpose framework for marginal maximum a posteriori estimation of probabilistic program variables. By using a series of code transformations, the evidence of any probabilistic program, and therefore of any graphical model, can be optimized with respect to an arbitrary subset of its sampled variables. To carry out this optimization, we develop the first Bayesian optimization package to directly exploit the source code of its target, leading to innovations in problem-independent hyperpriors, unbounded optimization, and implicit constraint satisfaction; delivering significant performance improvements over prominent existing packages. We present applications of our method to a number of tasks including engineering design and parameter optimization.

  @inproceedings{rainforth2016BOPP,
    title = {Bayesian {O}ptimization for {P}robabilistic {P}rograms},
    author = {Rainforth, Tom and Le, Tuan Anh and van de Meent, Jan-Willem and Osborne, Michael A and Wood, Frank},
    booktitle = {Advances in Neural Information Processing Systems},
    pages = {280--288},
    year = {2016}
  }
  

• T. Rainforth , R. Cornish , H. Yang , F. Wood , On the Pitfalls of Nested Monte Carlo, NIPS Workshop on Advances in Approximate Bayesian Inference, 2016.
  
  There is an increasing interest in estimating expectations outside of the classical inference framework, such as for models expressed as probabilistic programs. Many of these contexts call for some form of nested inference to be applied. In this paper, we analyse the behaviour of nested Monte Carlo (NMC) schemes, for which classical convergence proofs are insufficient. We give conditions under which NMC will converge, establish a rate of convergence, and provide empirical data that suggests that this rate is observable in practice. Finally, we prove that general-purpose nested inference schemes are inherently biased. Our results serve to warn of the dangers associated with na ̈ıve composition of inference and models.

  @article{rainforth2016nestedMC,
    title = {On the {P}itfalls of {N}ested {M}onte {C}arlo},
    author = {Rainforth, Tom and Cornish, Robert and Yang, Hongseok and Wood, Frank},
    year = {2016},
    journal = {NIPS Workshop on Advances in Approximate Bayesian Inference}
  }
  

• D. Janz , B. Paige , T. Rainforth , J. Meent , F. Wood , Probabilistic Structure Discovery in Time Series Data, NIPS Workshop on Artificial Intelligence for Data Science, 2016.
  
  Existing methods for structure discovery in time series data construct interpretable, compositional kernels for Gaussian process regression models. While the learned Gaussian process model provides posterior mean and variance estimates, typically the structure is learned via a greedy optimization procedure. This restricts the space of possible solutions and leads to over-confident uncertainty estimates. We introduce a fully Bayesian approach, inferring a full posterior over structures, which more reliably captures the uncertainty of the model.

  @article{janz2016probstruct,
    title = {Probabilistic {S}tructure {D}iscovery in {T}ime {S}eries {D}ata},
    author = {Janz, David and Paige, Brooks and Rainforth, Tom and van de Meent, Jan-Willem and Wood, Frank},
    year = {2016},
    journal = {NIPS Workshop on Artificial Intelligence for Data Science}
  }
  

• T. Rainforth , C. A. Naesseth , F. Lindsten , B. Paige , J. Meent , A. Doucet , F. Wood , Interacting Particle Markov Chain Monte Carlo, in Proceedings of the 33rd International Conference on Machine Learning, 2016, vol. 48.
  
  We introduce interacting particle Markov chain Monte Carlo (iPMCMC), a PMCMC method based on an interacting pool of standard and conditional sequential Monte Carlo samplers. Like related methods, iPMCMC is a Markov chain Monte Carlo sampler on an extended space. We present empirical results that show significant improvements in mixing rates relative to both noninteracting PMCMC samplers, and a single PMCMC sampler with an equivalent memory and computational budget. An additional advantage of the iPMCMC method is that it is suitable for distributed and multi-core architectures.

  @inproceedings{rainforth2016ipmcmc,
    title = {Interacting Particle {M}arkov Chain {M}onte {C}arlo},
    author = {Rainforth, Tom and Naesseth, Christian A and Lindsten, Fredrik and Paige, Brooks and van de Meent, Jan-Willem and Doucet, Arnaud and Wood, Frank},
    booktitle = {Proceedings of the 33rd International Conference on Machine Learning},
    series = {JMLR: W\&CP},
    volume = {48},
    year = {2016}
  }
  

• J. Rousseau , On the frequentist properties of bayesian nonparametric methods, Annual Review of Statistics and Its Application, vol. 3, 211–231, 2016.
  

  @article{rousseau2016frequentist,
    title = {On the frequentist properties of bayesian nonparametric methods},
    author = {Rousseau, Judith},
    journal = {Annual Review of Statistics and Its Application},
    volume = {3},
    pages = {211--231},
    year = {2016},
    publisher = {Annual Reviews}
  }
  

• J. Arbel , K. Mengersen , J. Rousseau , . others , Bayesian nonparametric dependent model for partially replicated data: the influence of fuel spills on species diversity, The Annals of Applied Statistics, vol. 10, no. 3, 1496–1516, 2016.
  

  @article{arbel2016bayesian,
    title = {Bayesian nonparametric dependent model for partially replicated data: the influence of fuel spills on species diversity},
    author = {Arbel, Julyan and Mengersen, Kerrie and Rousseau, Judith and others},
    journal = {The Annals of Applied Statistics},
    volume = {10},
    number = {3},
    pages = {1496--1516},
    year = {2016},
    publisher = {Institute of Mathematical Statistics}
  }
  

• E. Gassiat , J. Rousseau , . others , Nonparametric finite translation hidden Markov models and extensions, Bernoulli, vol. 22, no. 1, 193–212, 2016.
  

  @article{gassiat2016nonparametric,
    title = {Nonparametric finite translation hidden Markov models and extensions},
    author = {Gassiat, Elisabeth and Rousseau, Judith and others},
    journal = {Bernoulli},
    volume = {22},
    number = {1},
    pages = {193--212},
    year = {2016},
    publisher = {Bernoulli Society for Mathematical Statistics and Probability}
  }
  

• P. Rubin-Delanchy , D. J. Lawson , N. A. Heard , Anomaly detection for cyber-security applications, in Dynamic Networks and Cybersecurity, London: World Scientific, 2016.
  

  @incollection{rubin-delanchy16,
    author = {Rubin-Delanchy, Patrick and Lawson, Daniel J and Heard, Nicholas A},
    title = {Anomaly detection for cyber-security applications},
    publisher = {World Scientific},
    address = {London},
    year = {2016},
    chapter = {6},
    booktitle = {Dynamic Networks and Cybersecurity}
  }
  

• P. Rubin-Delanchy , N. A. Heard , On the mid-p-value of a test statistic with arbitrary real support, arXiv preprint:1505.05068, 2016.
  

  @article{rubin2015mid,
    title = {On the mid-p-value of a test statistic with arbitrary real support},
    author = {Rubin-Delanchy, Patrick and Heard, Nicholas A},
    journal = {arXiv preprint:1505.05068},
    year = {2016}
  }
  

• J. Griffié , M. Shannon , C. L. Bromley , L. Boelen , G. L. Burn , D. J. Williamson , N. A. Heard , A. P. Cope , D. M. Owen , P. Rubin-Delanchy , A Bayesian cluster analysis method for single-molecule localization microscopy data, Nature Protocols, vol. 11, 2499–2514, 2016.
  

  @article{rubindelanchyprot,
    journal = {Nature Protocols},
    title = {A {B}ayesian cluster analysis method for single-molecule localization microscopy data},
    author = {Griffi{\'e}, Juliette and Shannon, Michael and Bromley, Claire L and Boelen, Lies and Burn, Garth L and Williamson, David J and Heard, Nicholas A and Cope, Andrew P and Owen, Dylan M and Rubin-Delanchy, Patrick},
    pages = {2499--2514},
    volume = {11},
    year = {2016}
  }
  

• P. Rubin-Delanchy , N. M. Adams , N. A. Heard , Disassortivity of computer networks, in Proceedings of IEEE workshop on Big Data Analytics for Cyber-security Computing, 2016.
  

  @inproceedings{cyber4,
    author = {Rubin-Delanchy, Patrick and Adams, Niall M and Heard, Nicholas A},
    title = {Disassortivity of computer networks},
    booktitle = {Proceedings of IEEE workshop on Big Data Analytics for Cyber-security Computing},
    year = {2016},
    note = {To appear}
  }
  

• N. A. Heard , P. Rubin-Delanchy , Network-wide anomaly detection via the Dirichlet process, in Proceedings of IEEE workshop on Big Data Analytics for Cyber-security Computing, 2016.
  

  @inproceedings{cyber5,
    author = {Heard, Nicholas A and Rubin-Delanchy, P},
    title = {Network-wide anomaly detection via the {D}irichlet process},
    booktitle = {Proceedings of IEEE workshop on Big Data Analytics for Cyber-security Computing},
    year = {2016},
    note = {To appear}
  }
  

• D. Vukobratovic , D. Jakovetic , V. Skachek , D. Bajovic , D. Sejdinovic , G. Karabulut Kurt , C. Hollanti , I. Fischer , CONDENSE: A Reconfigurable Knowledge Acquisition Architecture for Future 5G IoT, IEEE Access, vol. 4, 3360–3378, 2016.
  
  In forthcoming years, the Internet of Things (IoT) will connect billions of smart devices generating and uploading a deluge of data to the cloud. If successfully extracted, the knowledge buried in the data can significantly improve the quality of life and foster economic growth. However, a critical bottleneck for realising the efficient IoT is the pressure it puts on the existing communication infrastructures, requiring transfer of enormous data volumes. Aiming at addressing this problem, we propose a novel architecture dubbed Condense (reconfigurable knowledge acquisition systems), which integrates the IoT-communication infrastructure into data analysis. This is achieved via the generic concept of network function computation: Instead of merely transferring data from the IoT sources to the cloud, the communication infrastructure should actively participate in the data analysis by carefully designed en-route processing. We define the Condense architecture, its basic layers, and the interactions among its constituent modules. Further, from the implementation side, we describe how Condense can be integrated into the 3rd Generation Partnership Project (3GPP) Machine Type Communications (MTC) architecture, as well as the prospects of making it a practically viable technology in a short time frame, relying on Network Function Virtualization (NFV) and Software Defined Networking (SDN). Finally, from the theoretical side, we survey the relevant literature on computing “atomic” functions in both analog and digital domains, as well as on function decomposition over networks, highlighting challenges, insights, and future directions for exploiting these techniques within practical 3GPP MTC architecture.

  @article{VukJaketal2016a,
    author = {Vukobratovic, D. and Jakovetic, D. and Skachek, V. and Bajovic, D. and Sejdinovic, D. and Karabulut Kurt, G. and Hollanti, C. and Fischer, I.},
    doi = {10.1109/ACCESS.2016.2585468},
    journal = {{IEEE Access}},
    pages = {3360--3378},
    title = {{CONDENSE: A Reconfigurable Knowledge Acquisition Architecture for Future 5G IoT}},
    volume = {4},
    year = {2016},
    bdsk-url-1 = {http://dx.doi.org/10.1109/ACCESS.2016.2585468}
  }
  

• D. Vukobratovic , D. Jakovetic , V. Skachek , D. Bajovic , D. Sejdinovic , Network Function Computation as a Service in Future 5G Machine Type Communications, in International Symposium on Turbo Codes & Iterative Information Processing (ISTC), 2016, 365–369.
  
  The 3GPP machine type communications (MTC) service is expected to contribute a dominant share of the IoT traffic via the upcoming fifth generation (5G) mobile cellular systems. MTC has ambition to connect billions of devices to communicate their data to MTC applications for further processing and data analysis. However, for majority of the applications, collecting all the MTC generated data is inefficient as the data is typically fed into application-dependent functions whose outputs determine the application actions. In this paper, we present a novel MTC architecture that, instead of collecting raw large-volume MTC data, offers the network function computation (NFC) as a service. For a given application demand (function to be computed), different modules (atomic nodes) of the communication infrastructure are orchestrated into a (reconfigurable) directed network topology, and each module is assigned an appropriately defined (reconfigurable) atomic function over the input data, such that the desired global network function is evaluated over the MTC data and a requested MTC-NFC service is delivered. We detail practical viability of incorporating MTC-NFC within the existing 3GPP architecture relying on emerging concepts of Network Function Virtualization and Software Defined Networking. Finally, throughout the paper, we point to the theoretical foundations that inspired the presented architecture highlighting challenges and future directions for designing 3GPP MTC-NFC service.

  @inproceedings{VukJaketal2016b,
    author = {Vukobratovic, D. and Jakovetic, D. and Skachek, V. and Bajovic, D. and Sejdinovic, D.},
    booktitle = {International Symposium on Turbo Codes \& Iterative Information Processing (ISTC)},
    pages = {365-369},
    title = {{Network Function Computation as a Service in Future 5G Machine Type Communications}},
    year = {2016},
    bdsk-url-1 = {http://dx.doi.org/10.1109/ISTC.2016.7593138}
  }
  

• J. Mitrovic , D. Sejdinovic , Y. W. Teh , DR-ABC: Approximate Bayesian Computation with Kernel-Based Distribution Regression, in International Conference on Machine Learning (ICML), 2016, 1482–1491.
  
  Project: bigbayes

  Performing exact posterior inference in complex generative models is often difficult or impossible due to an expensive to evaluate or intractable likelihood function. Approximate Bayesian computation (ABC) is an inference framework that constructs an approximation to the true likelihood based on the similarity between the observed and simulated data as measured by a predefined set of summary statistics. Although the choice of appropriate problem-specific summary statistics crucially influences the quality of the likelihood approximation and hence also the quality of the posterior sample in ABC, there are only few principled general-purpose approaches to the selection or construction of such summary statistics. In this paper, we develop a novel framework for this task using kernel-based distribution regression. We model the functional relationship between data distributions and the optimal choice (with respect to a loss function) of summary statistics using kernel-based distribution regression. We show that our approach can be implemented in a computationally and statistically efficient way using the random Fourier features framework for large-scale kernel learning. In addition to that, our framework shows superior performance when compared to related methods on toy and real-world problems.

  @inproceedings{MitSejTeh2016,
    author = {Mitrovic, J. and Sejdinovic, D. and Teh, Y. W.},
    booktitle = {International Conference on Machine Learning (ICML)},
    pages = {1482--1491},
    title = {{DR-ABC: Approximate Bayesian Computation with Kernel-Based Distribution Regression}},
    year = {2016},
    bdsk-url-1 = {http://jmlr.org/proceedings/papers/v48/mitrovic16.html}
  }
  

• G. Franchi , J. Angulo , D. Sejdinovic , Hyperspectral Image Classification with Support Vector Machines on Kernel Distribution Embeddings, in IEEE International Conference on Image Processing (ICIP), 2016, 1898–1902.
  
  We propose a novel approach for pixel classification in hyperspectral images, leveraging on both the spatial and spectral information in the data. The introduced method relies on a recently proposed framework for learning on distributions – by representing them with mean elements in reproducing kernel Hilbert spaces (RKHS) and formulating a classification algorithm therein. In particular, we associate each pixel to an empirical distribution of its neighbouring pixels, a judicious representation of which in an RKHS, in conjunction with the spectral information contained in the pixel itself, give a new explicit set of features that can be fed into a suite of standard classification techniques – we opt for a well established framework of support vector machines (SVM). Furthermore, the computational complexity is reduced via random Fourier features formalism. We study the consistency and the convergence rates of the proposed method and the experiments demonstrate strong performance on hyperspectral data with gains in comparison to the state-of-the-art results.

  @inproceedings{FraAngSej2016,
    author = {Franchi, G. and Angulo, J. and Sejdinovic, D.},
    booktitle = {IEEE International Conference on Image Processing (ICIP)},
    doi = {10.1109/ICIP.2016.7532688},
    pages = {1898-1902},
    title = {{Hyperspectral Image Classification with Support Vector Machines on Kernel Distribution Embeddings}},
    year = {2016},
    bdsk-url-1 = {http://dx.doi.org/10.1109/ICIP.2016.7532688}
  }
  

• B. Paige , D. Sejdinovic , F. Wood , Super-Sampling with a Reservoir, in Uncertainty in Artificial Intelligence (UAI), 2016, 567–576.
  
  We introduce an alternative to reservoir sampling, a classic and popular algorithm for drawing a fixed-size subsample from streaming data in a single pass. Rather than draw a random sample, our approach performs an online optimization which aims to select the subset which provides the best overall approximation to the full data set, as judged using a kernel two-sample test. This produces subsets which minimize the worst-case relative error when computing expectations of functions in a specified function class, using just the samples from the subset. Kernel functions are approximated using random Fourier features, and the subset of samples itself is stored in a random projection tree, allowing for an algorithm which runs in a single pass through the whole data set, with only a logarithmic time complexity in the size of the subset at each iteration. These “super-samples” subsampled from the full data provide a concise summary, as demonstrated empirically on mixture models and the MNIST dataset.

  @inproceedings{PaiSejWoo2016,
    author = {Paige, B. and Sejdinovic, D. and Wood, F.},
    booktitle = {Uncertainty in Artificial Intelligence (UAI)},
    pages = {567--576},
    title = {{Super-Sampling with a Reservoir}},
    year = {2016},
    bdsk-url-1 = {http://www.auai.org/uai2016/proceedings/papers/293.pdf}
  }
  

• S. Flaxman , D. Sejdinovic , J. Cunningham , S. Filippi , Bayesian Learning of Kernel Embeddings, in Uncertainty in Artificial Intelligence (UAI), 2016, 182–191.
  
  Project: bigbayes

  Kernel methods are one of the mainstays of machine learning, but the problem of kernel learning remains challenging, with only a few heuristics and very little theory. This is of particular importance in methods based on estimation of kernel mean embeddings of probability measures. For characteristic kernels, which include most commonly used ones, the kernel mean embedding uniquely determines its probability measure, so it can be used to design a powerful statistical testing framework, which includes nonparametric two-sample and independence tests. In practice, however, the performance of these tests can be very sensitive to the choice of kernel and its lengthscale parameters. To address this central issue, we propose a new probabilistic model for kernel mean embeddings, the Bayesian Kernel Embedding model, combining a Gaussian process prior over the Reproducing Kernel Hilbert Space containing the mean embedding with a conjugate likelihood function, thus yielding a closed form posterior over the mean embedding. The posterior mean of our model is closely related to recently proposed shrinkage estimators for kernel mean embeddings, while the posterior uncertainty is a new, interesting feature with various possible applications. Critically for the purposes of kernel learning, our model gives a simple, closed form marginal pseudolikelihood of the observed data given the kernel hyperparameters. This marginal pseudolikelihood can either be optimized to inform the hyperparameter choice or fully Bayesian inference can be used.

  @inproceedings{FlaSejCunFil2016,
    author = {Flaxman, S. and Sejdinovic, D. and Cunningham, J.P. and Filippi, S.},
    booktitle = {Uncertainty in Artificial Intelligence (UAI)},
    pages = {182--191},
    title = {{Bayesian Learning of Kernel Embeddings}},
    year = {2016},
    bdsk-url-1 = {http://www.auai.org/uai2016/proceedings/papers/145.pdf},
    bdsk-url-2 = {http://www.auai.org/uai2016/proceedings/supp/145_supp.pdf}
  }
  

• M. Park , W. Jitkrittum , D. Sejdinovic , K2-ABC: Approximate Bayesian Computation with Kernel Embeddings, in Artificial Intelligence and Statistics (AISTATS), 2016, 398–407.
  
  Complicated generative models often result in a situation where computing the likelihood of observed data is intractable, while simulating from the conditional density given a parameter value is relatively easy. Approximate Bayesian Computation (ABC) is a paradigm that enables simulation-based posterior inference in such cases by measuring the similarity between simulated and observed data in terms of a chosen set of summary statistics. However, there is no general rule to construct sufficient summary statistics for complex models. Insufficient summary statistics will “leak” information, which leads to ABC algorithms yielding samples from an incorrect (partial) posterior. In this paper, we propose a fully nonparametric ABC paradigm which circumvents the need for manually selecting summary statistics. Our approach, K2-ABC, uses maximum mean discrepancy (MMD) to construct a dissimilarity measure between the observed and simulated data. The embedding of an empirical distribution of the data into a reproducing kernel Hilbert space plays a role of the summary statistic and is sufficient whenever the corresponding kernels are characteristic. Experiments on a simulated scenario and a real-world biological problem illustrate the effectiveness of the proposed algorithm.

  @inproceedings{ParJitSej2016,
    author = {Park, M. and Jitkrittum, W. and Sejdinovic, D.},
    booktitle = {Artificial Intelligence and Statistics (AISTATS)},
    pages = {398--407},
    title = {{K2-ABC: Approximate Bayesian Computation with Kernel Embeddings}},
    year = {2016},
    bdsk-url-1 = {http://jmlr.org/proceedings/papers/v51/park16.html},
    bdsk-url-2 = {https://github.com/wittawatj/k2abc}
  }
  

• T. Fernandez , Y. W. Teh , Posterior Consistency for a Non-parametric Survival Model under a Gaussian Process Prior, 2016.
  
  Project: bigbayes

  @unpublished{FerTeh2016a,
    author = {Fernandez, T. and Teh, Y. W.},
    note = {ArXiv e-prints: 1611.02335},
    title = {Posterior Consistency for a Non-parametric Survival Model under a {G}aussian Process Prior},
    year = {2016},
    bdsk-url-1 = {https://arxiv.org/abs/1611.02335},
    bdsk-url-2 = {https://arxiv.org/pdf/1611.02335v1.pdf}
  }
  

• T. Fernandez , N. Rivera , Y. W. Teh , Gaussian Processes for Survival Analysis, in Advances in Neural Information Processing Systems (NeurIPS), 2016.
  
  Project: bigbayes

  We introduce a semi-parametric Bayesian model for survival analysis. The model is centred on a parametric baseline hazard, and uses a Gaussian process to model variations away from it nonparametrically, as well as dependence on covariates. As opposed to many other methods in survival analysis, our framework does not impose unnecessary constraints in the hazard rate or in the survival function. Furthermore, our model handles left, right and interval censoring mechanisms common in survival analysis. We propose a MCMC algorithm to perform inference and an approximation scheme based on random Fourier features to make computations faster. We report experimental results on synthetic and real data, showing that our model performs better than competing models such as Cox proportional hazards, ANOVA-DDP and random survival forests.

  @inproceedings{FerRivTeh2016a,
    author = {Fernandez, T. and Rivera, N. and Teh, Y. W.},
    booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
    title = {Gaussian Processes for Survival Analysis},
    year = {2016},
    bdsk-url-1 = {http://papers.nips.cc/paper/6443-gaussian-processes-for-survival-analysis},
    bdsk-url-2 = {http://papers.nips.cc/paper/6443-gaussian-processes-for-survival-analysis.pdf}
  }
  

• H. Kim , Y. W. Teh , Scalable Structure Discovery in Regression using Gaussian Processes, in Proceedings of the 2016 Workshop on Automatic Machine Learning, 2016.
  
  Project: bigbayes

  Automatic Bayesian Covariance Discovery(ABCD) in Lloyd et. al (2014) provides a framework for automating statistical modelling as well as exploratory data analysis for regression problems. However ABCD does not scale due to its O(N^3) running time. This is undesirable not only because the average size of data sets is growing fast, but also because there is potentially more information in bigger data, implying a greater need for more expressive models that can discover sophisticated structure. We propose a scalable version of ABCD, to encompass big data within the boundaries of automated statistical modelling.

  @inproceedings{KimTeh2016a,
    author = {Kim, H. and Teh, Y. W.},
    booktitle = {Proceedings of the 2016 Workshop on Automatic Machine Learning},
    title = {Scalable Structure Discovery in Regression using Gaussian Processes},
    year = {2016},
    bdsk-url-1 = {http://www.jmlr.org/proceedings/papers/v64/kim_scalable_2016.html},
    bdsk-url-2 = {http://www.jmlr.org/proceedings/papers/v64/kim_scalable_2016.pdf}
  }
  

• L. T. Elliott , Y. W. Teh , A Nonparametric HMM for Genetic Imputation and Coalescent Inference, Electronic Journal of Statistics, 2016.
  
  Project: bigbayes

  Genetic sequence data are well described by hidden Markov models (HMMs) in which latent states correspond to clusters of similar mutation patterns. Theory from statistical genetics suggests that these HMMs are nonhomogeneous (their transition probabilities vary along the chromosome) and have large support for self transitions. We develop a new nonparametric model of genetic sequence data, based on the hierarchical Dirichlet process, which supports these self transitions and nonhomogeneity. Our model provides a parameterization of the genetic process that is more parsimonious than other more general nonparametric models which have previously been applied to population genetics. We provide truncation-free MCMC inference for our model using a new auxiliary sampling scheme for Bayesian nonparametric HMMs. In a series of experiments on male X chromosome data from the Thousand Genomes Project and also on data simulated from a population bottleneck we show the benefits of our model over the popular finite model fastPHASE, which can itself be seen as a parametric truncation of our model. We find that the number of HMM states found by our model is correlated with the time to the most recent common ancestor in population bottlenecks. This work demonstrates the flexibility of Bayesian nonparametrics applied to large and complex genetic data.

  @article{EllTeh2016a,
    author = {Elliott, L. T. and Teh, Y. W.},
    journal = {Electronic Journal of Statistics},
    title = {A Nonparametric {HMM} for Genetic Imputation and Coalescent Inference},
    year = {2016}
  }
  

• S. Favaro , A. Lijoi , C. Nava , B. Nipoti , I. Prüenster , Y. W. Teh , On the Stick-Breaking Representation for Homogeneous NRMIs, Bayesian Analysis, vol. 11, 697–724, 2016.
  
  Project: bigbayes

  In this paper, we consider homogeneous normalized random measures with independent increments (hNRMI), a class of nonparametric priors recently introduced in the literature. Many of their distributional properties are known by now but their stick-breaking representation is missing. Here we display such a representation, which will feature dependent stick-breaking weights, and then derive explicit versions for noteworthy special cases of hNRMI. Posterior characterizations are also discussed. Finally, we devise an algorithm for slice sampling mixture models based on hNRMIs, which relies on the representation we have obtained, and implement it to analyze real data.

  @article{FavLijNav2016a,
    author = {Favaro, S. and Lijoi, A. and Nava, C. and Nipoti, B. and Pr\"uenster, I. and Teh, Y. W.},
    journal = {Bayesian Analysis},
    pages = {697-724},
    title = {On the Stick-Breaking Representation for Homogeneous {NRMIs}},
    volume = {11},
    year = {2016},
    bdsk-url-1 = {http://projecteuclid.org/euclid.ba/1440594949},
    bdsk-url-2 = {http://projecteuclid.org/download/pdfview_1/euclid.ba/1440594949}
  }
  

• Y. W. Teh , Bayesian Nonparametric Modelling and the Ubiquitous Ewens Sampling Formula, Statistical Science, vol. 31, no. 1, 34–36, 2016.
  
  Project: bigbayes

  @article{Teh2016a,
    author = {Teh, Y. W.},
    journal = {Statistical Science},
    number = {1},
    pages = {34-36},
    title = {Bayesian Nonparametric Modelling and the Ubiquitous {Ewens} Sampling Formula},
    volume = {31},
    year = {2016},
    bdsk-url-1 = {http://projecteuclid.org/euclid.ss/1455115911},
    bdsk-url-2 = {http://projecteuclid.org/download/pdfview_1/euclid.ss/1455115911}
  }
  

• M. Balog , B. Lakshminarayanan , Z. Ghahramani , D. M. Roy , Y. W. Teh , The Mondrian Kernel, in Uncertainty in Artificial Intelligence (UAI), 2016.
  
  Project: bigbayes

  We introduce the Mondrian kernel, a fast random feature approximation to the Laplace kernel. It is suitable for both batch and online learning, and admits a fast kernel-width-selection procedure as the random features can be re-used efficiently for all kernel widths. The features are constructed by sampling trees via a Mondrian process [Roy and Teh, 2009], and we highlight the connection to Mondrian forests [Lakshminarayanan et al., 2014], where trees are also sampled via a Mondrian process, but fit independently. This link provides a new insight into the relationship between kernel methods and random forests.

  @inproceedings{BalLakGha2016a,
    author = {Balog, M. and Lakshminarayanan, B. and Ghahramani, Z. and Roy, D. M. and Teh, Y. W.},
    booktitle = {Uncertainty in Artificial Intelligence (UAI)},
    title = {The {M}ondrian Kernel},
    year = {2016},
    bdsk-url-1 = {http://auai.org/uai2016/proceedings/papers/236.pdf},
    bdsk-url-2 = {http://auai.org/uai2016/proceedings/supp/236_supp.pdf}
  }
  

• Y. W. Teh , A. H. Thiéry , S. J. Vollmer , Consistency and Fluctuations for Stochastic Gradient Langevin Dynamics, Journal of Machine Learning Research, 2016.
  
  Project: sgmcmc

  Applying standard Markov chain Monte Carlo (MCMC) algorithms to large data sets is computationally expensive. Both the calculation of the acceptance probability and the creation of informed proposals usually require an iteration through the whole data set. The recently proposed stochastic gradient Langevin dynamics (SGLD) method circumvents this problem by generating proposals which are only based on a subset of the data, by skipping the accept-reject step and by using decreasing step-sizes sequence (δ_m)_m≥0. We provide in this article a rigorous mathematical framework for analysing this algorithm. We prove that, under verifiable assumptions, the algorithm is consistent, satisfies a central limit theorem (CLT) and its asymptotic bias-variance decomposition can be characterized by an explicit functional of the step-sizes sequence (δ_m)_m≥0. We leverage this analysis to give practical recommendations for the notoriously difficult tuning of this algorithm: it is asymptotically optimal to use a step-size sequence of the type δ_m≍m^−1/3, leading to an algorithm whose mean squared error (MSE) decreases at rate O(m^−1/3).

  @article{TehThiVol2016a,
    author = {Teh, Y. W. and Thi\'ery, A. H. and Vollmer, S. J.},
    journal = {Journal of Machine Learning Research},
    title = {Consistency and Fluctuations for Stochastic Gradient {L}angevin Dynamics},
    year = {2016},
    bdsk-url-1 = {http://jmlr.org/papers/v17/teh16a.html},
    bdsk-url-2 = {http://www.jmlr.org/papers/volume17/teh16a/teh16a.pdf}
  }
  

• S. J. Vollmer , K. C. Zygalakis , Y. W. Teh , Exploration of the (Non-)asymptotic Bias and Variance of Stochastic Gradient Langevin Dynamics, Journal of Machine Learning Research (JMLR), 2016.
  
  Project: sgmcmc

  Applying standard Markov chain Monte Carlo (MCMC) algorithms to large data sets is computationally infeasible. The recently proposed stochastic gradient Langevin dynamics (SGLD) method circumvents this problem in three ways: it generates proposed moves using only a subset of the data, it skips the Metropolis- Hastings accept-reject step, and it uses sequences of decreasing step sizes. In Teh et al. (2014), we provided the mathematical foundations for the decreasing step size SGLD, including consistency and a central limit theorem. However, in practice the SGLD is run for a relatively small number of iterations, and its step size is not decreased to zero. The present article investigates the behaviour of the SGLD with fixed step size. In particular we characterise the asymptotic bias explicitly, along with its dependence on the step size and the variance of the stochastic gradient. On that basis a modified SGLD which removes the asymptotic bias due to the variance of the stochastic gradients up to first order in the step size is derived. Moreover, we are able to obtain bounds on the finite-time bias, variance and mean squared error (MSE). The theory is illustrated with a Gaussian toy model for which the bias and the MSE for the estimation of moments can be obtained explicitly. For this toy model we study the gain of the SGLD over the standard Euler method in the limit of large data sets.

  @article{VolZygTeh2016a,
    author = {Vollmer, S. J. and Zygalakis, K. C. and Teh, Y. W.},
    journal = {Journal of Machine Learning Research (JMLR)},
    title = {Exploration of the (Non-)asymptotic Bias and Variance of Stochastic Gradient {L}angevin Dynamics},
    year = {2016},
    bdsk-url-1 = {http://jmlr.org/papers/v17/15-494.html},
    bdsk-url-2 = {http://www.jmlr.org/papers/volume17/15-494/15-494.pdf}
  }
  

• B. Lakshminarayanan , D. M. Roy , Y. W. Teh , Mondrian Forests for Large-Scale Regression when Uncertainty Matters, in Artificial Intelligence and Statistics (AISTATS), 2016.
  
  Project: bigbayes

  Many real-world regression problems demand a measure of the uncertainty associated with each prediction. Standard decision forests deliver efficient state-of-the-art predictive performance, but high-quality uncertainty estimates are lacking. Gaussian processes (GPs) deliver uncertainty estimates, but scaling GPs to large-scale data sets comes at the cost of approximating the uncertainty estimates. We extend Mondrian forests, first proposed by Lakshminarayanan et al. (2014) for classification problems, to the large-scale nonparametric regression setting. Using a novel hierarchical Gaussian prior that dovetails with the Mondrian forest framework, we obtain principled uncertainty estimates, while still retaining the computational advantages of decision forests. Through a combination of illustrative examples, real-world large-scale datasets and Bayesian optimization benchmarks, we demonstrate that Mondrian forests outperform approximate GPs on large-scale regression tasks and deliver better-calibrated uncertainty assessments than decision-forest-based methods.

  @inproceedings{LakRoyTeh2016a,
    author = {Lakshminarayanan, B. and Roy, D. M. and Teh, Y. W.},
    booktitle = {Artificial Intelligence and Statistics (AISTATS)},
    title = {{M}ondrian Forests for Large-Scale Regression when Uncertainty Matters},
    year = {2016},
    bdsk-url-1 = {http://jmlr.org/proceedings/papers/v51/lakshminarayanan16.html},
    bdsk-url-2 = {http://www.jmlr.org/proceedings/papers/v51/lakshminarayanan16.pdf},
    bdsk-url-3 = {http://jmlr.org/proceedings/papers/v51/lakshminarayanan16-supp.pdf}
  }
  

• D. Glowacka , Y. W. Teh , J. Shawe-Taylor , Image Retrieval with a Bayesian Model of Relevance Feedback, 2016.
  
  A content-based image retrieval system based on multinomial relevance feedback is proposed. The system relies on an interactive search paradigm where at each round a user is presented with k images and selects the one closest to their ideal target. Two approaches, one based on the Dirichlet distribution and one based the Beta distribution, are used to model the problem motivating an algorithm that trades exploration and exploitation in presenting the images in each round. Experimental results show that the new approach compares favourably with previous work.

  @unpublished{GloTehSha2016a,
    author = {Glowacka, D. and Teh, Y. W. and Shawe-Taylor, J.},
    note = {ArXiv e-prints: 1603.09522},
    title = {Image Retrieval with a {B}ayesian Model of Relevance Feedback},
    year = {2016},
    bdsk-url-1 = {https://arxiv.org/pdf/1603.09522.pdf}
  }
  

• K. Palla , F. Caron , Y. W. Teh , Bayesian Nonparametrics for Sparse Dynamic Networks, 2016.
  
  Project: bigbayes

  We propose a Bayesian nonparametric prior for time-varying networks. To each node of the network is associated a positive parameter, modeling the sociability of that node. Sociabilities are assumed to evolve over time, and are modeled via a dynamic point process model. The model is able to (a) capture smooth evolution of the interaction between nodes, allowing edges to appear/disappear over time (b) capture long term evolution of the sociabilities of the nodes (c) and yield sparse graphs, where the number of edges grows subquadratically with the number of nodes. The evolution of the sociabilities is described by a tractable time-varying gamma process. We provide some theoretical insights into the model and apply it to three real world datasets.

  @unpublished{PalCarTeh2016a,
    author = {Palla, K. and Caron, F. and Teh, Y. W.},
    note = {ArXiv e-prints: 1607.01624},
    title = {Bayesian Nonparametrics for Sparse Dynamic Networks},
    year = {2016},
    bdsk-url-1 = {https://arxiv.org/pdf/1607.01624.pdf}
  }
  

• M. Battiston , S. Favaro , Y. W. Teh , Multi-armed bandit for species discovery: A Bayesian nonparametric approach, Journal of the American Statistical Association, 2016.
  
  Project: bigbayes

  Let (P1,...,PJ) denote J populations of animals from distinct regions. A priori, it is unknown which species are present in each region and what are their corresponding frequencies. Species are shared among populations and each species can be present in more than one region with its frequency varying across populations. In this paper we consider the problem of sequentially sampling these populations in order to observe the greatest number of different species. We adopt a Bayesian nonparametric approach and endow (P1,...,PJ) with a Hierarchical Pitman-Yor process prior. As a consequence of the hierarchical structure, the J unknown discrete probability measures share the same support, that of their common random base measure. Given this prior choice, we propose a sequential rule that, at every time step, given the information available up to that point, selects the population from which to collect the next observation. Rather than picking the population with the highest posterior estimate of producing a new value, the proposed rule includes a Thompson sampling step to better balance the exploration-exploitation trade-off. We also propose an extension of the algorithm to deal with incidence data, where multiple observations are collected in a time period. The performance of the proposed algorithms is assessed through a simulation study and compared to three other strategies. Finally, we compare these algorithms using a dataset of species of trees, collected from different plots in South America.

  @article{BatFavTeh2016a,
    author = {Battiston, M. and Favaro, S. and Teh, Y. W.},
    doi = {http://dx.doi.org/10.1080/01621459.2016.1261711},
    journal = {Journal of the American Statistical Association},
    title = {Multi-armed bandit for species discovery: A Bayesian nonparametric approach},
    year = {2016},
    bdsk-url-1 = {http://www.tandfonline.com/doi/full/10.1080/01621459.2016.1261711},
    bdsk-url-2 = {http://www.tandfonline.com/doi/pdf/10.1080/01621459.2016.1261711?needAccess=true}
  }
  

2015

• I. Castillo , J. Rousseau , A Bernstein-von Mises theorem for smooth functionals in semiparametric models, Ann. Statist., vol. 43, no. 6, 2353–2383, Dec. 2015.
  
  @article{castillo:rousseau:2015,
    author = {Castillo, IsmaÎl and Rousseau, Judith},
    doi = {10.1214/15-AOS1336},
    fjournal = {The Annals of Statistics},
    journal = {Ann. Statist.},
    month = dec,
    number = {6},
    pages = {2353--2383},
    publisher = {The Institute of Mathematical Statistics},
    title = {A {B}ernstein-von {M}ises theorem for smooth functionals in semiparametric models},
    volume = {43},
    year = {2015}
  }
  

• T. Rukat , A. Baker , A. Quinn , M. Woolrich , Resting state brain networks from EEG: Comparing hidden Markov states with classical microstates, Proceedings of the 5th NIPS Workshop on Machine Learning and Interpretation in Neuroimaging (MLINI), Dec. 2015.
  

  @article{Rukat2015d,
    title = {Resting state brain networks from EEG: Comparing hidden Markov states with classical microstates},
    author = {Rukat, T. and Baker, A. and Quinn, A. and Woolrich, M.},
    journal = {Proceedings of the 5th NIPS Workshop on Machine Learning and Interpretation in Neuroimaging (MLINI)},
    year = {2015},
    month = dec
  }
  

• P. Rebeschini , R. Handel , Can local particle filters beat the curse of dimensionality?, Ann. Appl. Probab., vol. 25, no. 5, 2809–2866, Oct. 2015.
  
  @article{rebeschini2015,
    author = {Rebeschini, Patrick and van Handel, Ramon},
    doi = {10.1214/14-AAP1061},
    fjournal = {The Annals of Applied Probability},
    journal = {Ann. Appl. Probab.},
    month = oct,
    number = {5},
    pages = {2809--2866},
    publisher = {The Institute of Mathematical Statistics},
    title = {Can local particle filters beat the curse of dimensionality?},
    volume = {25},
    year = {2015},
    bdsk-url-1 = {https://doi.org/10.1214/14-AAP1061},
    bdsk-url-2 = {http://dx.doi.org/10.1214/14-AAP1061}
  }
  

• M. Hoffmann , J. Rousseau , J. Schmidt-Hieber , On adaptive posterior concentration rates, Ann. Statist., vol. 43, no. 5, 2259–2295, Oct. 2015.
  
  @article{hoffmann2015,
    author = {Hoffmann, Marc and Rousseau, Judith and Schmidt-Hieber, Johannes},
    doi = {10.1214/15-AOS1341},
    fjournal = {The Annals of Statistics},
    journal = {Ann. Statist.},
    month = oct,
    number = {5},
    pages = {2259--2295},
    publisher = {The Institute of Mathematical Statistics},
    title = {On adaptive posterior concentration rates},
    volume = {43},
    year = {2015}
  }
  

• T. Rukat , Distributed analysis of expression quantitative trait loci in Apache Spark, Jul-2015.
  
  A major challenge in current genomic research is the development of computational and statistical tools that are capable of analysing the ever increasing amount of data provided by next generation sequencing methods. Here we investigate the potential of the open-source distributed computing framework Apache Spark, to facilitate fast, horizontally scalable genetic data analysis, exemplified by the search for expression quantitative trait loci (eQTL) in the 1000 Genomes dataset. An algorithm for trans eQTL analysis is proposed and enables the analysis of all available pairwise correlations of gene-SNP- pairs in the dataset (> 3 × 10 11 ) in approximately 90 minutes on a cluster with 768 worker nodes. The algorithm scales linearly with the problem size, and benefits from larger cluster sizes up to 768 nodes. A second algorithm for cis eQTL analysis is introduced but shown to be inherently difficult to design within a distributed system and therefore only recommended for very targeted investigations. Though constantly enhanced and extended, Spark already offers a simple and powerful environment, suitable for distributed genetic data analysis.

  @unpublished{rukat15:_distr_apach_spark,
    title = {Distributed analysis of expression quantitative trait loci in Apache Spark},
    author = {Rukat, Tammo},
    month = jul,
    year = {2015},
    comment = {https://github.com/TammoR/spark_eqtl},
    file = {Rukat_Spark_eQTL.pdf:Rukat_Spark_eQTL.pdf:PDF},
    owner = {tammo},
    timestamp = {2015.08.10}
  }
  

• P. Eirew , A. Steif , J. Khattra , G. Ha , D. Yap , H. Farahani , K. Gelmon , S. Chia , C. Mar , A. Wan , E. Laks , J. Biele , K. Shumansky , J. Rosner , A. McPherson , C. Nielsen , A. J. L. Roth , C. Lefebvre , A. Bashashati , C. de Souza , C. Siu , R. Aniba , J. Brimhall , A. Oloumi , T. Osako , A. Bruna , J. L. Sandoval , T. Algara , W. Greenwood , K. Leung , H. Cheng , H. Xue , Y. Wang , D. Lin , A. J. Mungall , R. Moore , Y. Zhao , J. Lorette , L. Nguyen , D. Huntsman , C. J. Eaves , C. Hansen , M. A. Marra , C. Caldas , S. P. Shah , S. Aparicio , Dynamics of genomic clones in breast cancer patient xenografts at single-cell resolution., Nature, vol. 518, no. 7539, 422–426, Feb. 2015.
  

  @article{EirewSteifKhattraEtAl2015,
    author = {Eirew, Peter and Steif, Adi and Khattra, Jaswinder and Ha, Gavin and Yap, Damian and Farahani, Hossein and Gelmon, Karen and Chia, Stephen and Mar, Colin and Wan, Adrian and Laks, Emma and Biele, Justina and Shumansky, Karey and Rosner, Jamie and McPherson, Andrew and Nielsen, Cydney and Roth, Andrew J L. and Lefebvre, Calvin and Bashashati, Ali and {de Souza}, Camila and Siu, Celia and Aniba, Radhouane and Brimhall, Jazmine and Oloumi, Arusha and Osako, Tomo and Bruna, Alejandra and Sandoval, Jose L. and Algara, Teresa and Greenwood, Wendy and Leung, Kaston and Cheng, Hongwei and Xue, Hui and Wang, Yuzhuo and Lin, Dong and Mungall, Andrew J. and Moore, Richard and Zhao, Yongjun and Lorette, Julie and Nguyen, Long and Huntsman, David and Eaves, Connie J. and Hansen, Carl and Marra, Marco A. and Caldas, Carlos and Shah, Sohrab P. and Aparicio, Samuel},
    doi = {10.1038/nature13952},
    institution = {Centre for Translational and Applied Genomics, BC Cancer Agency, 600 West 10th Avenue, Vancouver, British Columbia V5Z 4E6, Canada.},
    journal = {Nature},
    month = feb,
    number = {7539},
    pages = {422--426},
    title = {{Dynamics of genomic clones in breast cancer patient xenografts at single-cell resolution.}},
    volume = {518},
    year = {2015}
  }
  

• C. C. Holmes , F. Caron , J. E. Griffin , D. A. Stephens , Two-sample Bayesian nonparametric hypothesis testing, Bayesian Analysis, vol. 10, no. 2, 297–320, 2015.
  
  @article{Holmes2015,
    title = {Two-sample Bayesian nonparametric hypothesis testing},
    author = {Holmes, C. C. and Caron, F. and Griffin, J. E. and Stephens, D. A.},
    journal = {Bayesian Analysis},
    year = {2015},
    number = {2},
    pages = {297--320},
    volume = {10},
    file = {1422884976:https\://projecteuclid.org/download/pdfview_1/euclid.ba/1422884976:PDF},
    publisher = {International Society for Bayesian Analysis}
  }
  

• M. Winlaw , M. Hynes , A. Caterini , H. De Sterck , Algorithmic Acceleration of Parallel ALS for Collaborative Filtering: Speeding up Distributed Big Data Recommendation in Spark, in Parallel and Distributed Systems (ICPADS), 2015 IEEE 21st International Conference on, 2015, 682–691.
  

  @inproceedings{winlaw2015algorithmic,
    title = {{Algorithmic Acceleration of Parallel ALS for Collaborative Filtering: Speeding up Distributed Big Data Recommendation in Spark}},
    author = {Winlaw, M. and Hynes, M. and Caterini, A. and De Sterck, H.},
    booktitle = {Parallel and Distributed Systems (ICPADS), 2015 IEEE 21st International Conference on},
    pages = {682--691},
    year = {2015},
    organization = {IEEE}
  }
  

• H. C. Martin , R. Christ , J. G. Hussin , J. O’Connell , S. Gordon , H. Mbarek , J. Hottenga , K. McAloney , G. Willemsen , P. Gasparini , N. Pirastu , G. W. Montgomery , P. Navarro , N. Soranzo , D. Toniolo , V. Vitart , J. F. Wilson , J. Marchini , D. I. Boomsma , N. G. Martin , P. Donnelly , Multicohort analysis of the maternal age effect on recombination, Nature Communications, vol. 6, 7846, 2015.
  
  Several studies have reported that the number of crossovers increases with maternal age in humans, but others have found the opposite. Resolving the true effect has implications for understanding the maternal age effect on aneuploidies. Here, we revisit this question in the largest sample to date using single nucleotide polymorphism (SNP)-chip data, comprising over 6,000 meioses from nine cohorts. We develop and fit a hierarchical model to allow for differences between cohorts and between mothers. We estimate that over 10 years, the expected number of maternal crossovers increases by 2.1% (95% credible interval (0.98%, 3.3%)). Our results are not consistent with the larger positive and negative effects previously reported in smaller cohorts. We see heterogeneity between cohorts that is likely due to chance effects in smaller samples, or possibly to confounders, emphasizing that care should be taken when interpreting results from any specific cohort about the effect of maternal age on recombination.

  @article{Martin2015,
    author = {Martin, Hilary C. and Christ, Ryan and Hussin, Julie G. and O'Connell, Jared and Gordon, Scott and Mbarek, Hamdi and Hottenga, Jouke-Jan and McAloney, Kerrie and Willemsen, Gonnecke and Gasparini, Paolo and Pirastu, Nicola and Montgomery, Grant W. and Navarro, Pau and Soranzo, Nicole and Toniolo, Daniela and Vitart, Veronique and Wilson, James F. and Marchini, Jonathan and Boomsma, Dorret I. and Martin, Nicholas G. and Donnelly, Peter},
    doi = {10.1038/ncomms8846},
    issn = {2041-1723},
    journal = {Nature Communications},
    pages = {7846},
    pmid = {26242864},
    title = {{Multicohort analysis of the maternal age effect on recombination}},
    volume = {6},
    year = {2015}
  }
  

• A. Doucet , M. Pitt , G. Deligiannidis , R. Kohn , Efficient implementation of Markov chain Monte Carlo when using an unbiased likelihood estimator, Biometrika, vol. 102, no. 2, 295–313, 2015.
  

  @article{doucet2015efficient,
    author = {Doucet, A. and Pitt, M.K. and Deligiannidis, G. and Kohn, R.},
    title = {Efficient implementation of Markov chain Monte Carlo when using an unbiased likelihood estimator},
    journal = {Biometrika},
    year = {2015},
    volume = {102},
    number = {2},
    pages = {295-313}
  }
  

• G. Deligiannidis , M. Peligrad , S. Utev , Asymptotic Variance of Stationary Reversible and Normal Markov Processes, Electronic Journal of Probability, vol. 20, 2015.
  

  @article{deligiannidis2015asymptotic,
    author = {Deligiannidis, G. and Peligrad, M. and Utev, S.},
    title = {Asymptotic Variance of Stationary Reversible and Normal Markov Processes},
    journal = {Electronic Journal of Probability},
    year = {2015},
    volume = {20},
    page_count = {26}
  }
  

• R. J. Evans , V. Didelez , Recovering from Selection Bias using Marginal Structure in Discrete Models, in Proceedings of Causal Inference Workshop, Uncertainty in Artificial Intelligence, 2015.
  

  @inproceedings{evans:15,
    author = {Evans, R. J. and Didelez, V.},
    booktitle = {Proceedings of Causal Inference Workshop, Uncertainty in Artificial Intelligence},
    title = {Recovering from Selection Bias using Marginal Structure in Discrete Models},
    year = {2015}
  }
  

• R. J. Evans , Conditional distributions and log-linear parameters, Electronic Journal of Statistics, vol. 9, no. 1, 475–491, 2015.
  

  @article{evans15,
    author = {Evans, R. J.},
    journal = {Electronic Journal of Statistics},
    title = {Conditional distributions and log-linear parameters},
    volume = {9},
    issue = {1},
    pages = {475--491},
    year = {2015}
  }
  

• S. S. M. Mahon , O. Lenive , S. Filippi , M. P. H. Stumpf , Information processing by simple molecular motifs and susceptibility to noise, Journal of The Royal Society Interface, 2015.
  

  @article{Mahon:2015hz,
    author = {Mahon, Siobhan S Mc and Lenive, Oleg and Filippi, Sarah and Stumpf, Michael P H},
    title = {{Information processing by simple molecular motifs and susceptibility to noise}},
    journal = {Journal of The Royal Society Interface},
    year = {2015}
  }
  

• B. J. Gram-Hansen , Electron-Proton Entanglement in the Hydrogen Atom, Master's thesis, 2015.
  
  @mastersthesis{gramelec,
    title = {Electron-Proton Entanglement in the Hydrogen Atom},
    author = {Gram-Hansen, Bradley J},
    year = {2015}
  }
  

• C. C. Holmes , F. Caron , J. E. Griffin , D. A. Stephens , . others , Two-sample Bayesian nonparametric hypothesis testing, Bayesian Analysis, vol. 10, no. 2, 297–320, 2015.
  

  @article{holmes2015two,
    title = {Two-sample Bayesian nonparametric hypothesis testing},
    author = {Holmes, Chris C and Caron, Fran{\c{c}}ois and Griffin, Jim E and Stephens, David A and others},
    journal = {Bayesian Analysis},
    volume = {10},
    number = {2},
    pages = {297--320},
    year = {2015},
    publisher = {International Society for Bayesian Analysis}
  }
  

• R. Bardenet , A. Doucet , C. C. Holmes , On Markov chain Monte Carlo methods for tall data, arXiv preprint arXiv:1505.02827, 2015.
  

  @article{bardenet2015markov,
    title = {On Markov chain Monte Carlo methods for tall data},
    author = {Bardenet, R{\'e}mi and Doucet, Arnaud and Holmes, Chris C.},
    journal = {arXiv preprint arXiv:1505.02827},
    year = {2015}
  }
  

• J. C. Taylor , H. C. Martin , S. Lise , J. Broxholme , J. Cazier , A. Rimmer , A. Kanapin , G. Lunter , S. Fiddy , C. Allan , . others , Factors influencing success of clinical genome sequencing across a broad spectrum of disorders, Nature genetics, vol. 47, no. 7, 717–726, 2015.
  

  @article{taylor2015factors,
    title = {Factors influencing success of clinical genome sequencing across a broad spectrum of disorders},
    author = {Taylor, Jenny C and Martin, Hilary C and Lise, Stefano and Broxholme, John and Cazier, Jean-Baptiste and Rimmer, Andy and Kanapin, Alexander and Lunter, Gerton and Fiddy, Simon and Allan, Chris and others},
    journal = {Nature genetics},
    volume = {47},
    number = {7},
    pages = {717--726},
    year = {2015},
    publisher = {Nature Research}
  }
  

• M. Rantalainen , C. M. Lindgren , C. C. Holmes , Robust Linear Models for Cis-eQTL Analysis, PloS one, vol. 10, no. 5, e0127882, 2015.
  

  @article{rantalainen2015robust,
    title = {Robust Linear Models for Cis-eQTL Analysis},
    author = {Rantalainen, Mattias and Lindgren, Cecilia M and Holmes, Chris C},
    journal = {PloS one},
    volume = {10},
    number = {5},
    pages = {e0127882},
    year = {2015},
    publisher = {Public Library of Science}
  }
  

• M. H. Angelis , G. Nicholson , M. Selloum , J. K. White , H. Morgan , R. Ramirez-Solis , T. Sorg , S. Wells , H. Fuchs , M. Fray , . others , Analysis of mammalian gene function through broad-based phenotypic screens across a consortium of mouse clinics, Nature genetics, vol. 47, no. 9, 969–978, 2015.
  

  @article{de2015analysis,
    title = {Analysis of mammalian gene function through broad-based phenotypic screens across a consortium of mouse clinics},
    author = {de Angelis, Martin Hrab{\v{e}} and Nicholson, George and Selloum, Mohammed and White, Jacqueline K and Morgan, Hugh and Ramirez-Solis, Ramiro and Sorg, Tania and Wells, Sara and Fuchs, Helmut and Fray, Martin and others},
    journal = {Nature genetics},
    volume = {47},
    number = {9},
    pages = {969--978},
    year = {2015},
    publisher = {Nature Publishing Group}
  }
  

• L. J. Aslett , P. M. Esperança , C. C. Holmes , Encrypted statistical machine learning: new privacy preserving methods, arXiv preprint arXiv:1508.06845, 2015.
  

  @article{aslett2015encrypted,
    title = {Encrypted statistical machine learning: new privacy preserving methods},
    author = {Aslett, Louis JM and Esperan{\c{c}}a, Pedro M and Holmes, Chris C},
    journal = {arXiv preprint arXiv:1508.06845},
    year = {2015}
  }
  

• L. J. Aslett , P. M. Esperança , C. C. Holmes , A review of homomorphic encryption and software tools for encrypted statistical machine learning, arXiv preprint arXiv:1508.06574, 2015.
  

  @article{aslett2015review,
    title = {A review of homomorphic encryption and software tools for encrypted statistical machine learning},
    author = {Aslett, Louis JM and Esperan{\c{c}}a, Pedro M and Holmes, Chris C},
    journal = {arXiv preprint arXiv:1508.06574},
    year = {2015}
  }
  

• C. C. Drovandi , C. C. Holmes , J. McGree , K. Mengersen , S. Richardson , E. Ryan , A principled experimental design approach to Big Data analysis, 2015.
  

  @article{drovandi2015principled,
    title = {A principled experimental design approach to Big Data analysis},
    author = {Drovandi, Christopher C and Holmes, Chris C and McGree, James and Mengersen, Kerrie and Richardson, Sylvia and Ryan, Elizabeth},
    year = {2015}
  }
  

• R. G. Stiphout , L. Winchester , S. A. Haider , J. Ragoussis , A. L. Harris , C. C. Holmes , F. M. Buffa , . others , Abstract B1-56: Distinct roles of copy number and loss-of-heterozygosity in predicting prognosis for breast cancer patients. American Association for Cancer Research, 2015.
  

  @misc{van2015abstract,
    title = {Abstract B1-56: Distinct roles of copy number and loss-of-heterozygosity in predicting prognosis for breast cancer patients},
    author = {van Stiphout, Ruud GPM and Winchester, Laura and Haider, Syed A and Ragoussis, Jiannis and Harris, Adrian L and Holmes, Chris C. and Buffa, Francesca M and others},
    year = {2015},
    publisher = {American Association for Cancer Research}
  }
  

• A. C. Daly , D. J. Gavaghan , C. C. Holmes , J. Cooper , Hodgkin–Huxley revisited: reparametrization and identifiability analysis of the classic action potential model with approximate Bayesian methods, Royal Society open science, vol. 2, no. 12, 150499, 2015.
  

  @article{daly2015hodgkin,
    title = {Hodgkin--Huxley revisited: reparametrization and identifiability analysis of the classic action potential model with approximate Bayesian methods},
    author = {Daly, Aidan C and Gavaghan, David J and Holmes, Chris C. and Cooper, Jonathan},
    journal = {Royal Society open science},
    volume = {2},
    number = {12},
    pages = {150499},
    year = {2015},
    publisher = {The Royal Society}
  }
  

• R. Bardenet , A. Doucet , C. Holmes , Markov chain Monte Carlo and tall data, preprint, 2015.
  

  @article{bardenet2015markow,
    title = {Markov chain Monte Carlo and tall data},
    author = {Bardenet, R{\'e}mi and Doucet, A and Holmes, C},
    journal = {preprint},
    year = {2015}
  }
  

• C. Holmes , P. Bissiri , S. Walker , A general framework for updating belief distributions, Journal of the Royal Statistical Society Series B: Statistical Methodology, 2015.
  

  @article{holmes2015general,
    title = {A general framework for updating belief distributions},
    author = {Holmes, CC and Bissiri, PG and Walker, SG},
    journal = {Journal of the Royal Statistical Society Series B: Statistical Methodology},
    year = {2015},
    publisher = {Wiley: 12 months}
  }
  

• J. Lee , S. Choi , Tree-guided MCMC inference for normalized random measure mixture models, in Advances in Neural Information Processing Systems (NeurIPS), 2015.
  

  @inproceedings{Lee2015a,
    title = {Tree-guided {MCMC} inference for normalized random measure mixture models},
    author = {Lee, J. and Choi, S.},
    booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
    year = {2015}
  }
  

• J. Lee , S. Choi , Bayesian hierarchical clustering with exponential family: small-variance asymptotics and reducibility, in Artificial Intelligence and Statistics (AISTATS), 2015.
  

  @inproceedings{Lee2015b,
    title = {Bayesian hierarchical clustering with exponential family: small-variance asymptotics and reducibility},
    author = {Lee, J. and Choi, S.},
    booktitle = {Artificial Intelligence and Statistics (AISTATS)},
    year = {2015}
  }
  

• C. J. Maddison , A. Huang , I. Sutskever , D. Silver , Move Evaluation in Go Using Deep Convolutional Neural Networks, in International Conference on Learning Representations, 2015.
  
  @inproceedings{maddison2015go,
    author = {Maddison, Chris J. and Huang, Aja and Sutskever, Ilya and Silver, David},
    booktitle = {International Conference on Learning Representations},
    title = {{Move Evaluation in Go Using Deep Convolutional Neural Networks}},
    year = {2015},
    bdsk-url-1 = {https://arxiv.org/pdf/1412.6564.pdf}
  }
  

• T. Rainforth , F. Wood , Canonical Correlation Forests, arXiv preprint arXiv:1507.05444, 2015.
  
  We introduce canonical correlation forests (CCFs), a new decision tree ensemble method for classification and regression. Individual canonical correlation trees are binary decision trees with hyperplane splits based on local canonical correlation coefficients calculated during training. Unlike axis-aligned alternatives, the decision surfaces of CCFs are not restricted to the coordinate system of the inputs features and therefore more naturally represent data with correlated inputs. CCFs naturally accommodate multiple outputs, provide a similar computational complexity to random forests, and inherit their impressive robustness to the choice of input parameters. As part of the CCF training algorithm, we also introduce projection bootstrapping, a novel alternative to bagging for oblique decision tree ensembles which maintains use of the full dataset in selecting split points, often leading to improvements in predictive accuracy. Our experiments show that, even without parameter tuning, CCFs out-perform axis-aligned random forests and other state-of-the-art tree ensemble methods on both classification and regression problems, delivering both improved predictive accuracy and faster training times. We further show that they outperform all of the 179 classifiers considered in a recent extensive survey.

  @article{rainforth2015canonical,
    title = {Canonical Correlation Forests},
    author = {Rainforth, Tom and Wood, Frank},
    journal = {arXiv preprint arXiv:1507.05444},
    year = {2015},
    arixv = {https://arxiv.org/abs/1507.05444}
  }
  

• P. Rebeschini , R. Handel , Phase transitions in nonlinear filtering, Electron. J. Probab., vol. 20, 46 pp., 2015.
  
  @article{rebeschini2015b,
    author = {Rebeschini, Patrick and van Handel, Ramon},
    doi = {10.1214/EJP.v20-3281},
    fjournal = {Electronic Journal of Probability},
    journal = {Electron. J. Probab.},
    pages = {46 pp.},
    pno = {7},
    publisher = {The Institute of Mathematical Statistics and the Bernoulli Society},
    title = {Phase transitions in nonlinear filtering},
    volume = {20},
    year = {2015},
    bdsk-url-1 = {https://doi.org/10.1214/EJP.v20-3281},
    bdsk-url-2 = {http://dx.doi.org/10.1214/EJP.v20-3281}
  }
  

• P. Rebeschini , A. Karbasi , Fast Mixing for Discrete Point Processes, in Proceedings of The 28th Conference on Learning Theory, Paris, France, 2015, vol. 40, 1480–1500.
  
  We investigate the systematic mechanism for designing fast mixing Markov chain Monte Carlo algorithms to sample from discrete point processes under the Dobrushin uniqueness condition for Gibbs measures. Discrete point processes are defined as probability distributions μ(S)∝\exp(βf(S)) over all subsets S∈2^V of a finite set V through a bounded set function f:2^V→\mathbbR and a parameter β>0. A subclass of discrete point processes characterized by submodular functions (which include log-submodular distributions, submodular point processes, and determinantal point processes) has recently gained a lot of interest in machine learning and shown to be effective for modeling diversity and coverage. We show that if the set function (not necessarily submodular) displays a natural notion of decay of correlation, then, for βsmall enough, it is possible to design fast mixing Markov chain Monte Carlo methods that yield error bounds on marginal approximations that do not depend on the size of the set V. The sufficient conditions that we derive involve a control on the (discrete) Hessian of set functions, a quantity that has not been previously considered in the literature. We specialize our results for submodular functions, and we discuss canonical examples where the Hessian can be easily controlled.

  @inproceedings{pmlr-v40-Rebeschini15,
    address = {Paris, France},
    author = {Rebeschini, Patrick and Karbasi, Amin},
    booktitle = {Proceedings of The 28th Conference on Learning Theory},
    editor = {Gr{\"u}nwald, Peter and Hazan, Elad and Kale, Satyen},
    month = {03--06 Jul},
    pages = {1480--1500},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {Fast Mixing for Discrete Point Processes},
    volume = {40},
    year = {2015},
    bdsk-url-1 = {http://proceedings.mlr.press/v40/Rebeschini15.html}
  }
  

• Z. Havre , N. White , J. Rousseau , K. Mengersen , Overfitting Bayesian mixture models with an unknown number of components, PloS one, vol. 10, no. 7, e0131739, 2015.
  

  @article{van2015overfitting,
    title = {Overfitting Bayesian mixture models with an unknown number of components},
    author = {van Havre, Zo{\'e} and White, Nicole and Rousseau, Judith and Mengersen, Kerrie},
    journal = {PloS one},
    volume = {10},
    number = {7},
    pages = {e0131739},
    year = {2015},
    publisher = {Public Library of Science}
  }
  

• P. Rubin-Delanchy , G. L. Burn , J. Griffié , D. J. Williamson , N. A. Heard , A. P. Cope , D. M. Owen , Bayesian cluster identification in single-molecule localization microscopy data, Nature methods, vol. 12, 1072–1076, 2015.
  

  @article{rubin2015,
    title = {Bayesian cluster identification in single-molecule localization microscopy data},
    author = {Rubin-Delanchy, Patrick and Burn, Garth L and Griffi{\'e}, Juliette and Williamson, David J and Heard, Nicholas A and Cope, Andrew P and Owen, Dylan M},
    journal = {Nature methods},
    volume = {12},
    pages = {1072--1076},
    year = {2015},
    publisher = {Nature Publishing Group}
  }
  

• P. Rubin-Delanchy , D. J. Lawson , Posterior predictive p-values and the convex order, arXiv preprint:1412.3442, 2015.
  

  @article{rubin2015posterior,
    title = {Posterior predictive p-values and the convex order},
    author = {Rubin-Delanchy, Patrick and Lawson, Daniel J},
    journal = {arXiv preprint:1412.3442},
    year = {2015}
  }
  

• T. Rukat , S. A. Reinsberg , A Bayesian Information Criterion for Multi-Model Inference in DCE-MRI, in Proc. Intl. Soc. Mag. Reson. Med. 23 (2015) 2339, 2015, no. 2339.
  

  @inproceedings{Rukat2015a,
    title = {A {Bayes}ian Information Criterion for Multi-Model Inference in {DCE-MRI}},
    author = {Rukat, Tammo and Reinsberg, Stefan A.},
    booktitle = {Proc. Intl. Soc. Mag. Reson. Med. 23 (2015) 2339},
    year = {2015},
    number = {2339},
    file = {Rukat2014a_Information Criteria weighted Parameter Estimates in DCE-MRI.pdf:Rukat2015.pdf:PDF;Rukat2014a_Information Criteria weighted Parameter Estimates in DCE-MRI_Abstract.pdf:Rukat2014a_Information Criteria weighted Parameter Estimates in DCE-MRI_Abstract.pdf:PDF},
    owner = {tammo},
    timestamp = {2014.09.18}
  }
  

• T. Rukat , S. Walker-Samuel , S. A. Reinsberg , Dynamic contrast-enhanced MRI in mice: An investigation of model parameter uncertainties, Magnetic Resonance in Medicine, vol. 73, 1979–1987, 2015.
  
  Purpose To establish the experimental factors that dominate the uncertainty of hemodynamic parameters in commonly used pharmacokinetic models. Methods By fitting simulation results from a multiregion tissue exchange model (Multiple path, Multiple tracer, Indicator Dilution, 4 region), the precision and accuracy of hemodynamic parameters in dynamic contrast-enhanced MRI with four tracer kinetic models is investigated. The impact of various injection rates as well as imprecise knowledge of the arterial input functions is examined. Results Fast injections are beneficial for Ktrans precision within the extended Tofts model and within the two-compartment exchange model but do not affect the other models under investigation. Biases from errors in the arterial input functions are mostly consistent in size and direction for the simple and the extended Tofts model, while they are hardly predictable for the other models. Errors in the hematocrit introduce the greatest loss in parameter accuracy, amounting to an average Ktrans bias of 40% for a 30% overestimation throughout all models. Conclusion This simulation study allows the detailed inspection of the isolated impact from various experimental conditions on parameter uncertainty. Because parameter uncertainty comparable to human studies was found, this study represents a validation of preclinical dynamic contrast-enhanced MRI for modeling human tumor physiology. Magn Reson Med, 2014. Â\copyright 2014 Wiley Periodicals, Inc.

  @article{Rukat2015,
    title = {Dynamic contrast-enhanced {MRI} in mice: An investigation of model parameter uncertainties},
    author = {Rukat, T. and Walker-Samuel, S and Reinsberg, S. A.},
    journal = {Magnetic Resonance in Medicine},
    year = {2015},
    pages = {1979–1987},
    volume = {73},
    copyright = {© 2014 Wiley Periodicals, Inc.},
    doi = {10.1002/mrm.25319},
    file = {Rukat2014_Dynamic contrast-enhanced {MRI} in mice\: An investigation of model parameter uncertainties.pdf:Rukat2014.pdf:PDF},
    issn = {1522-2594},
    keywords = {Dynamic contrast-enhanced-{MRI}; Perfusion; Permeability; pharmacokinetic models; tracer-kinetic models},
    language = {en},
    shorttitle = {Dynamic contrast-enhanced {MRI} in mice},
    urldate = {2014-07-27}
  }
  

• H. Strathmann , D. Sejdinovic , M. Girolami , Unbiased Bayes for Big Data: Paths of Partial Posteriors, ArXiv e-prints:1501.03326, 2015.
  
  A key quantity of interest in Bayesian inference are expectations of functions with respect to a posterior distribution. Markov Chain Monte Carlo is a fundamental tool to consistently compute these expectations via averaging samples drawn from an approximate posterior. However, its feasibility is being challenged in the era of so called Big Data as all data needs to be processed in every iteration. Realising that such simulation is an unnecessarily hard problem if the goal is estimation, we construct a computationally scalable methodology that allows unbiased estimation of the required expectations – without explicit simulation from the full posterior. The scheme’s variance is finite by construction and straightforward to control, leading to algorithms that are provably unbiased and naturally arrive at a desired error tolerance. This is achieved at an average computational complexity that is sub-linear in the size of the dataset and its free parameters are easy to tune. We demonstrate the utility and generality of the methodology on a range of common statistical models applied to large-scale benchmark and real-world datasets.

  @unpublished{StrSejGir2015,
    author = {Strathmann, H. and Sejdinovic, D. and Girolami, M.},
    journal = {ArXiv e-prints:1501.03326},
    title = {{Unbiased Bayes for Big Data: Paths of Partial Posteriors}},
    year = {2015}
  }
  

• H. Strathmann , D. Sejdinovic , S. Livingstone , Z. Szabo , A. Gretton , Gradient-free Hamiltonian Monte Carlo with Efficient Kernel Exponential Families, in Advances in Neural Information Processing Systems (NeurIPS), vol. 28, 2015, 955–963.
  
  We propose Kamiltonian Monte Carlo (KMC), a gradient-free adaptive MCMC algorithm based on Hamiltonian Monte Carlo (HMC). On target densities where HMC is unavailable due to intractable gradients, KMC adaptively learns the target’s gradient structure by fitting an exponential family model in a Reproducing Kernel Hilbert Space. Computational costs are reduced by two novel efficient approximations to this gradient. While being asymptotically exact, KMC mimics HMC in terms of sampling efficiency and offers substantial mixing improvements to state-of-the-art gradient free-samplers. We support our claims with experimental studies on both toy and real-world applications, including Approximate Bayesian Computation and exact-approximate MCMC.

  @incollection{StrSejLivSzaGre2015,
    author = {Strathmann, H. and Sejdinovic, D. and Livingstone, S. and Szabo, Z. and Gretton, A.},
    booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
    pages = {955--963},
    title = {{Gradient-free Hamiltonian Monte Carlo with Efficient Kernel Exponential Families}},
    volume = {28},
    year = {2015},
    bdsk-url-1 = {http://papers.nips.cc/paper/5890-gradient-free-hamiltonian-monte-carlo-with-efficient-kernel-exponential-families}
  }
  

• K. Chwialkowski , A. Ramdas , D. Sejdinovic , A. Gretton , Fast Two-Sample Testing with Analytic Representations of Probability Measures, in Advances in Neural Information Processing Systems (NeurIPS), vol. 28, 2015, 1981–1989.
  
  We propose a class of nonparametric two-sample tests with a cost linear in the sample size. Two tests are given, both based on an ensemble of distances between analytic functions representing each of the distributions. The first test uses smoothed empirical characteristic functions to represent the distributions, the second uses distribution embeddings in a reproducing kernel Hilbert space. Analyticity implies that differences in the distributions may be detected almost surely at a finite number of randomly chosen locations/frequencies. The new tests are consistent against a larger class of alternatives than the previous linear-time tests based on the (non-smoothed) empirical characteristic functions, while being much faster than the current state-of-the-art quadratic-time kernel-based or energy distance-based tests. Experiments on artificial benchmarks and on challenging real-world testing problems demonstrate that our tests give a better power/time tradeoff than competing approaches, and in some cases, better outright power than even the most expensive quadratic-time tests. This performance advantage is retained even in high dimensions, and in cases where the difference in distributions is not observable with low order statistics.

  @incollection{ChwRamSejGre2015,
    author = {Chwialkowski, K. and Ramdas, A. and Sejdinovic, D. and Gretton, A.},
    booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
    pages = {1981--1989},
    title = {{Fast Two-Sample Testing with Analytic Representations of Probability Measures}},
    volume = {28},
    year = {2015},
    bdsk-url-1 = {http://papers.nips.cc/paper/5685-fast-two-sample-testing-with-analytic-representations-of-probability-measures}
  }
  

• D. Vukobratovic , D. Sejdinovic , A. Pizurica , Compressed Sensing Using Sparse Binary Measurements: A Rateless Coding Perspective, in IEEE International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), 2015.
  
  Compressed Sensing (CS) methods using sparse binary measurement matrices and iterative message-passing recovery procedures have been recently investigated due to their low computational complexity and excellent performance. Drawing much of inspiration from sparse-graph codes such as Low-Density Parity-Check (LDPC) codes, these studies use analytical tools from modern coding theory to analyze CS solutions. In this paper, we consider and systematically analyze the CS setup inspired by a class of efficient, popular and flexible sparse-graph codes called rateless codes. The proposed rateless CS setup is asymptotically analyzed using tools such as Density Evolution and EXIT charts and fine-tuned using degree distribution optimization techniques.

  @inproceedings{VukSejPiz2015,
    author = {Vukobratovic, Dejan and Sejdinovic, Dino and Pizurica, Aleksandra},
    booktitle = {IEEE International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)},
    doi = {10.1109/SPAWC.2015.7227005},
    title = {Compressed Sensing Using Sparse Binary Measurements: A Rateless Coding Perspective},
    year = {2015},
    bdsk-url-1 = {http://dx.doi.org/10.1109/SPAWC.2015.7227005}
  }
  

• Z. Kurth-Nelson , G. Barnes , D. Sejdinovic , R. Dolan , P. Dayan , Temporal structure in associative retrieval, eLife, vol. 4, no. e04919, 2015.
  
  Electrophysiological data disclose rich dynamics in patterns of neural activity evoked by sensory objects. Retrieving objects from memory reinstates components of this activity. In humans, the temporal structure of this retrieved activity remains largely unexplored, and here we address this gap using the spatiotemporal precision of magnetoencephalography (MEG). In a sensory preconditioning paradigm, ’indirect’ objects were paired with ’direct’ objects to form associative links, and the latter were then paired with rewards. Using multivariate analysis methods we examined the short-time evolution of neural representations of indirect objects retrieved during reward-learning about direct objects. We found two components of the evoked representation of the indirect stimulus, 200 ms apart. The strength of retrieval of one, but not the other, representational component correlated with generalization of reward learning from direct to indirect stimuli. We suggest the temporal structure within retrieved neural representations may be key to their function.

  @article{KNeBarSejDolDay2015,
    author = {Kurth-Nelson, Zeb and Barnes, Gareth and Sejdinovic, Dino and Dolan, Ray and Dayan, Peter},
    doi = {10.7554/eLife.04919},
    journal = {eLife},
    number = {e04919},
    publisher = {eLife Sciences Publications Limited},
    title = {Temporal structure in associative retrieval},
    volume = {4},
    year = {2015},
    bdsk-url-1 = {http://dx.doi.org/10.7554/eLife.04919}
  }
  

• W. Jitkrittum , A. Gretton , N. Heess , S. M. A. Eslami , B. Lakshminarayanan , D. Sejdinovic , Z. Szabó , Kernel-Based Just-In-Time Learning for Passing Expectation Propagation Messages, in Uncertainty in Artificial Intelligence (UAI), 2015.
  
  We propose an efficient nonparametric strategy for learning a message operator in expectation propagation (EP), which takes as input the set of incoming messages to a factor node, and produces an outgoing message as output. This learned operator replaces the multivariate integral required in classical EP, which may not have an analytic expression. We use kernel-based regression, which is trained on a set of probability distributions representing the incoming messages, and the associated outgoing messages. The kernel approach has two main advantages: first, it is fast, as it is implemented using a novel two-layer random feature representation of the input message distributions; second, it has principled uncertainty estimates, and can be cheaply updated online, meaning it can request and incorporate new training data when it encounters inputs on which it is uncertain. In experiments, our approach is able to solve learning problems where a single message operator is required for multiple, substantially different data sets (logistic regression for a variety of classification problems), where the ability to accurately assess uncertainty and to efficiently and robustly update the message operator are essential.

  @inproceedings{JitGreHeeEslLakSejSza2015,
    author = {Jitkrittum, Wittawat and Gretton, Arthur and Heess, Nicolas and Eslami, S. M. Ali and Lakshminarayanan, Balaji and Sejdinovic, Dino and Szab\'{o}, Zolt\'{a}n},
    booktitle = {Uncertainty in Artificial Intelligence (UAI)},
    title = {{Kernel-Based Just-In-Time Learning for Passing Expectation Propagation Messages}},
    year = {2015},
    bdsk-url-1 = {http://auai.org/uai2015/proceedings/papers/235.pdf},
    bdsk-url-2 = {http://auai.org/uai2015/proceedings/supp/239_supp.pdf},
    bdsk-url-3 = {https://github.com/wittawatj/kernel-ep}
  }
  

• A. G. Deshwar , L. Boyles , J. Wintersinger , P. C. Boutros , Y. W. Teh , Q. Morris , Abstract B2-59: PhyloSpan: using multimutation reads to resolve subclonal architectures from heterogeneous tumor samples, AACR Special Conference on Computational and Systems Biology of Cancer, vol. 75, 2015.
  
  Project: bigbayes

  @article{BouBoyDes2015a,
    author = {Deshwar, A. G. and Boyles, L. and Wintersinger, J. and Boutros, P. C. and Teh, Y. W. and Morris, Q.},
    booktitle = {AACR Special Conference on Computational and Systems Biology of Cancer},
    doi = {10.1158/1538-7445.AM2015-4865},
    journal = {Cancer Research},
    title = {Abstract {B2-59}: {PhyloSpan}: using multimutation reads to resolve subclonal architectures from heterogeneous tumor samples},
    volume = {75},
    year = {2015},
    bdsk-url-1 = {http://cancerres.aacrjournals.org/content/75/22_Supplement_2/B2-59.short},
    bdsk-url-2 = {http://dx.doi.org/10.1158/1538-7445.AM2015-4865}
  }
  

• S. Favaro , B. Nipoti , Y. W. Teh , Rediscovery of Good-Turing Estimators via Bayesian Nonparametrics, Biometrics, 2015.
  
  Project: bigbayes

  The problem of estimating discovery probabilities originated in the context of statistical ecology, and in recent years it has become popular due to its frequent appearance in challenging applications arising in genetics, bioinformatics, linguistics, designs of experiments, machine learning, etc. A full range of statistical approaches, parametric and nonparametric as well as frequentist and Bayesian, has been proposed for estimating discovery probabilities. In this article, we investigate the relationships between the celebrated Good–Turing approach, which is a frequentist nonparametric approach developed in the 1940s, and a Bayesian nonparametric approach recently introduced in the literature. Specifically, under the assumption of a two parameter Poisson-Dirichlet prior, we show that Bayesian nonparametric estimators of discovery probabilities are asymptotically equivalent, for a large sample size, to suitably smoothed Good–Turing estimators. As a by-product of this result, we introduce and investigate a methodology for deriving exact and asymptotic credible intervals to be associated with the Bayesian nonparametric estimators of discovery probabilities. The proposed methodology is illustrated through a comprehensive simulation study and the analysis of Expressed Sequence Tags data generated by sequencing a benchmark complementary DNA library.

  @article{FavNipTeh2015b,
    author = {Favaro, S. and Nipoti, B. and Teh, Y. W.},
    doi = {10.1111/biom.12366},
    journal = {Biometrics},
    title = {Rediscovery of {Good-Turing} Estimators via {B}ayesian Nonparametrics},
    year = {2015},
    bdsk-url-1 = {http://onlinelibrary.wiley.com/doi/10.1111/biom.12366/abstract},
    bdsk-url-2 = {http://dx.doi.org/10.1111/biom.12366},
    bdsk-url-3 = {https://arxiv.org/pdf/1401.0303.pdf}
  }
  

• P. G. Moreno , A. Artés-Rodríguez , Y. W. Teh , F. Perez-Cruz , Bayesian Nonparametric Crowdsourcing, Journal of Machine Learning Research (JMLR), 2015.
  
  Project: bigbayes

  Crowdsourcing has been proven to be an effective and efficient tool to annotate large data-sets. User annotations are often noisy, so methods to combine the annotations to produce reliable estimates of the ground truth are necessary. We claim that considering the existence of clusters of users in this combination step can improve the performance. This is especially important in early stages of crowdsourcing implementations, where the number of annotations is low. At this stage there is not enough information to accurately estimate the bias introduced by each annotator separately, so we have to resort to models that consider the statistical links among them. In addition, finding these clusters is interesting in itself as knowing the behavior of the pool of annotators allows implementing efficient active learning strategies. Based on this, we propose in this paper two new fully unsupervised models based on a Chinese restaurant process (CRP) prior and a hierarchical structure that allows inferring these groups jointly with the ground truth and the properties of the users. Efficient inference algorithms based on Gibbs sampling with auxiliary variables are proposed. Finally, we perform experiments, both on synthetic and real databases, to show the advantages of our models over state-of-the-art algorithms.

  @article{MorArtTeh2015a,
    author = {Moreno, P. G. and Art\'es-Rodr\'iguez, A. and Teh, Y. W. and Perez-Cruz, F.},
    journal = {Journal of Machine Learning Research (JMLR)},
    title = {{B}ayesian Nonparametric Crowdsourcing},
    year = {2015},
    bdsk-url-1 = {http://www.jmlr.org/papers/v16/moreno15a.html},
    bdsk-url-2 = {http://www.jmlr.org/papers/volume16/moreno15a/moreno15a.pdf}
  }
  

• R. P. Adams , E. B. Fox , E. B. Sudderth , Y. W. Teh , Guest Editors’ Introduction to the Special Issue on Bayesian Nonparametrics, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015.
  
  @article{AdaFoxSud2015a,
    author = {Adams, R. P. and Fox, E. B. and Sudderth, E. B. and Teh, Y. W.},
    journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
    title = {Guest Editors' Introduction to the Special Issue on Bayesian Nonparametrics},
    year = {2015},
    bdsk-url-1 = {http://ieeexplore.ieee.org/xpl/tocresult.jsp?isnumber=7004096}
  }
  

• M. Lomeli , S. Favaro , Y. W. Teh , A hybrid sampler for Poisson-Kingman mixture models, in Advances in Neural Information Processing Systems (NeurIPS), 2015.
  
  Project: bigbayes

  This paper concerns the introduction of a new Markov Chain Monte Carlo scheme for posterior sampling in Bayesian nonparametric mixture models with priors that belong to the general Poisson-Kingman class. We present a novel and compact way of representing the infinite dimensional component of the model such that while explicitly representing this infinite component it has less memory and storage requirements than previous MCMC schemes. We describe comparative simulation results demonstrating the efficacy of the proposed MCMC algorithm against existing marginal and conditional MCMC samplers.

  @inproceedings{LomFavTeh2015a,
    author = {Lomeli, M. and Favaro, S. and Teh, Y. W.},
    booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
    title = {A hybrid sampler for {Poisson-Kingman} mixture models},
    year = {2015},
    bdsk-url-1 = {http://papers.nips.cc/paper/5799-a-hybrid-sampler-for-poisson-kingman-mixture-models},
    bdsk-url-2 = {http://papers.nips.cc/paper/5799-a-hybrid-sampler-for-poisson-kingman-mixture-models.pdf},
    bdsk-url-3 = {http://papers.nips.cc/paper/5799-a-hybrid-sampler-for-poisson-kingman-mixture-models-supplemental.zip}
  }
  

• M. De Iorio , S. Favaro , Y. W. Teh , Bayesian Inference on Population Structure: From Parametric to Nonparametric Modeling, in Nonparametric Bayesian Inference in Biostatistics, Springer, 2015.
  
  Project: bigbayes

  Making inference on population structure from genotype data requires to identify the actual subpopulations and assign individuals to these populations. The source populations are assumed to be in Hardy-Weinberg equilibrium, but the allelic frequencies of these populations and even the number of populations present in a sample are unknown. In this chapter we present a review of some Bayesian parametric and nonparametric models for making inference on population structure, with emphasis on model-based clustering methods. Our aim is to show how recent developments in Bayesian nonparametrics have been usefully exploited in order to introduce natural nonparametric counterparts of some of the most celebrated parametric approaches for inferring population structure. We use data from the 1000 Genomes project (http://​www.​1000genomes.​org/​) to provide a brief illustration of some of these nonparametric approaches.

  @incollection{De-FavTeh2015a,
    author = {{De Iorio}, M. and Favaro, S. and Teh, Y. W.},
    booktitle = {Nonparametric {B}ayesian Inference in Biostatistics},
    doi = {10.1007/978-3-319-19518-6_7},
    publisher = {Springer},
    title = {{B}ayesian Inference on Population Structure: From Parametric to Nonparametric Modeling},
    year = {2015},
    bdsk-url-1 = {http://link.springer.com/chapter/10.1007/978-3-319-19518-6_7},
    bdsk-url-2 = {http://dx.doi.org/10.1007/978-3-319-19518-6_7}
  }
  

• T. Lienart , Y. W. Teh , A. Doucet , Expectation Particle Belief Propagation, in Advances in Neural Information Processing Systems (NeurIPS), 2015.
  
  Project: sgmcmc

  We propose an original particle-based implementation of the Loopy Belief Propagation (LPB) algorithm for pairwise Markov Random Fields (MRF) on a continuous state space. The algorithm constructs adaptively efficient proposal distributions approximating the local beliefs at each note of the MRF. This is achieved by considering proposal distributions in the exponential family whose parameters are updated iterately in an Expectation Propagation (EP) framework. The proposed particle scheme provides consistent estimation of the LBP marginals as the number of particles increases. We demonstrate that it provides more accurate results than the Particle Belief Propagation (PBP) algorithm of Ihler and McAllester (2009) at a fraction of the computational cost and is additionally more robust empirically. The computational complexity of our algorithm at each iteration is quadratic in the number of particles. We also propose an accelerated implementation with sub-quadratic computational complexity which still provides consistent estimates of the loopy BP marginal distributions and performs almost as well as the original procedure.

  @inproceedings{LieTehDou2015a,
    author = {Lienart, T. and Teh, Y. W. and Doucet, A.},
    booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
    title = {Expectation Particle Belief Propagation},
    year = {2015},
    bdsk-url-1 = {http://papers.nips.cc/paper/5674-expectation-particle-belief-propagation},
    bdsk-url-2 = {http://papers.nips.cc/paper/5674-expectation-particle-belief-propagation.pdf},
    bdsk-url-3 = {http://papers.nips.cc/paper/5674-expectation-particle-belief-propagation-supplemental.zip}
  }
  

• S. Favaro , B. Nipoti , Y. W. Teh , Random variate generation for Laguerre-type exponentially tilted α-stable distributions, Electronic Journal of Statistics, vol. 9, 1230–1242, 2015.
  
  Project: bigbayes

  Exact sampling methods have been recently developed for generating random variates for exponentially tilted α-stable distributions. In this paper we show how to generate, exactly, random variates for a more general class of tilted α-stable distributions, which is referred to as the class of Laguerre-type exponentially tilted α-stable distributions. Beside the exponentially tilted α-stable distribution, such a class includes also the Erlang tilted α-stable distribution. This is a special case of the so-called gamma tilted α-stable distribution, for which an efficient exact random variate generator is currently not available in the literature. Our result fills this gap.

  @article{FavNipTeh2015a,
    author = {Favaro, S. and Nipoti, B. and Teh, Y. W.},
    doi = {10.1214/15-EJS1033},
    journal = {Electronic Journal of Statistics},
    pages = {1230-1242},
    title = {Random variate generation for {L}aguerre-type exponentially tilted {$\alpha$}-stable distributions},
    volume = {9},
    year = {2015},
    bdsk-url-1 = {http://projecteuclid.org/euclid.ejs/1433982944},
    bdsk-url-2 = {http://dx.doi.org/10.1214/15-EJS1033},
    bdsk-url-3 = {http://projecteuclid.org/download/pdfview_1/euclid.ejs/1433982944}
  }
  

• M. Balog , Y. W. Teh , The Mondrian Process for Machine Learning, 2015.
  
  Project: bigbayes

  This report is concerned with the Mondrian process and its applications in machine learning. The Mondrian process is a guillotine-partition-valued stochastic process that possesses an elegant self-consistency property. The first part of the report uses simple concepts from applied probability to define the Mondrian process and explore its properties. The Mondrian process has been used as the main building block of a clever online random forest classification algorithm that turns out to be equivalent to its batch counterpart. We outline a slight adaptation of this algorithm to regression, as the remainder of the report uses regression as a case study of how Mondrian processes can be utilized in machine learning. In particular, the Mondrian process will be used to construct a fast approximation to the computationally expensive kernel ridge regression problem with a Laplace kernel. The complexity of random guillotine partitions generated by a Mondrian process and hence the complexity of the resulting regression models is controlled by a lifetime hyperparameter. It turns out that these models can be efficiently trained and evaluated for all lifetimes in a given range at once, without needing to retrain them from scratch for each lifetime value. This leads to an efficient procedure for determining the right model complexity for a dataset at hand. The limitation of having a single lifetime hyperparameter will motivate the final Mondrian grid model, in which each input dimension is endowed with its own lifetime parameter. In this model we preserve the property that its hyperparameters can be tweaked without needing to retrain the modified model from scratch.

  @unpublished{BalTeh2015a,
    author = {Balog, M. and Teh, Y. W.},
    note = {ArXiv e-prints: 1507.05181},
    title = {The {M}ondrian Process for Machine Learning},
    year = {2015},
    bdsk-url-1 = {https://arxiv.org/pdf/1507.05181.pdf}
  }
  

• P. Orbanz , L. James , Y. W. Teh , Scaled subordinators and generalizations of the Indian buffet process, 2015.
  
  Project: bigbayes

  We study random families of subsets of ℕ that are similar to exchangeable random partitions, but do not require constituent sets to be disjoint: Each element of ℕ may be contained in multiple subsets. One class of such objects, known as Indian buffet processes, has become a popular tool in machine learning. Based on an equivalence between Indian buffet and scale-invariant Poisson processes, we identify a random scaling variable whose role is similar to that played in exchangeable partition models by the total mass of a random measure. Analogous to the construction of exchangeable partitions from normalized subordinators, random families of sets can be constructed from randomly scaled subordinators. Coupling to a heavy-tailed scaling variable induces a power law on the number of sets containing the first n elements. Several examples, with properties desirable in applications, are derived explicitly. A relationship to exchangeable partitions is made precise as a correspondence between scaled subordinators and Poisson-Kingman measures, generalizing a result of Arratia, Barbour and Tavare on scale-invariant processes.

  @unpublished{OrbJamTeh2015a,
    author = {Orbanz, P. and James, L. and Teh, Y. W.},
    note = {ArXiv e-prints: 1510.07309},
    title = {Scaled subordinators and generalizations of the {I}ndian buffet process},
    year = {2015},
    bdsk-url-1 = {https://arxiv.org/pdf/1510.07309v1.pdf}
  }
  

• M. De Iorio , L. Elliott , S. Favaro , Y. W. Teh , Bayesian Nonparametric Inference of Population Admixtures, 2015.
  
  Project: bigbayes

  We propose a Bayesian nonparametric model to infer population admixture, extending the Hierarchical Dirichlet Process to allow for correlation between loci due to Linkage Disequilibrium. Given multilocus genotype data from a sample of individuals, the model allows inferring classifying individuals as unadmixed or admixed, inferring the number of subpopulations ancestral to an admixed population and the population of origin of chromosomal regions. Our model does not assume any specific mutation process and can be applied to most of the commonly used genetic markers. We present a MCMC algorithm to perform posterior inference from the model and discuss methods to summarise the MCMC output for the analysis of population admixture. We demonstrate the performance of the proposed model in simulations and in a real application, using genetic data from the EDAR gene, which is considered to be ancestry-informative due to well-known variations in allele frequency as well as phenotypic effects across ancestry. The structure analysis of this dataset leads to the identification of a rare haplotype in Europeans.

  @unpublished{De-EllFav2015a,
    author = {{De Iorio}, M. and Elliott, L. and Favaro, S. and Teh, Y. W.},
    note = {ArXiv e-prints: 1503.08278},
    title = {{B}ayesian Nonparametric Inference of Population Admixtures},
    year = {2015},
    bdsk-url-1 = {https://arxiv.org/pdf/1503.08278v1.pdf}
  }
  

• B. Lakshminarayanan , D. M. Roy , Y. W. Teh , Particle Gibbs for Bayesian Additive Regression Trees, in Proceedings of the International Conference on Artificial Intelligence and Statistics, 2015.
  
  Project: bigbayes sgmcmc

  Additive regression trees are flexible non-parametric models and popular off-the-shelf tools for real-world non-linear regression. In application domains, such as bioinformatics, where there is also demand for probabilistic predictions with measures of uncertainty, the Bayesian additive regression trees (BART) model, introduced by Chipman et al. (2010), is increasingly popular. As data sets have grown in size, however, the standard Metropolis–Hastings algorithms used to per- form inference in BART are proving inadequate. In particular, these Markov chains make local changes to the trees and suffer from slow mixing when the data are high- dimensional or the best-fitting trees are more than a few layers deep. We present a novel sampler for BART based on the Particle Gibbs (PG) algorithm (Andrieu et al., 2010) and a top-down particle filtering algorithm for Bayesian decision trees (Lakshminarayanan et al., 2013). Rather than making local changes to individual trees, the PG sampler proposes a complete tree to fit the residual. Experiments show that the PG sampler outperforms existing samplers in many settings.

  @inproceedings{LakRoyTeh2015b,
    author = {Lakshminarayanan, B. and Roy, D. M. and Teh, Y. W.},
    booktitle = {Proceedings of the International Conference on Artificial Intelligence and Statistics},
    title = {Particle {G}ibbs for {B}ayesian Additive Regression Trees},
    year = {2015},
    bdsk-url-1 = {http://jmlr.org/proceedings/papers/v38/lakshminarayanan15.html},
    bdsk-url-2 = {http://jmlr.org/proceedings/papers/v38/lakshminarayanan15.pdf},
    bdsk-url-3 = {http://jmlr.org/proceedings/papers/v38/lakshminarayanan15-supp.pdf}
  }
  

2014

• G. Ha , A. Roth , J. Khattra , J. Ho , D. Yap , L. M. Prentice , N. Melnyk , A. McPherson , A. Bashashati , E. Laks , J. Biele , J. Ding , A. Le , J. Rosner , K. Shumansky , M. A. Marra , C. B. Gilks , D. G. Huntsman , J. N. McAlpine , S. Aparicio , S. P. Shah , TITAN: inference of copy number architectures in clonal cell populations from tumor whole-genome sequence data., Genome Res, vol. 24, no. 11, 1881–1893, Nov. 2014.
  

  @article{HaRothKhattraEtAl2014,
    author = {Ha, Gavin and Roth, Andrew and Khattra, Jaswinder and Ho, Julie and Yap, Damian and Prentice, Leah M. and Melnyk, Nataliya and McPherson, Andrew and Bashashati, Ali and Laks, Emma and Biele, Justina and Ding, Jiarui and Le, Alan and Rosner, Jamie and Shumansky, Karey and Marra, Marco A. and Gilks, C Blake and Huntsman, David G. and McAlpine, Jessica N. and Aparicio, Samuel and Shah, Sohrab P.},
    doi = {10.1101/gr.180281.114},
    institution = {Department of Molecular Oncology, British Columbia Cancer Agency, Vancouver, BC V5Z 1L3, Canada; Department of Computer Science, University of British Columbia, Vancouver, BC V6T 1Z4, Canada; Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC V6T 2B5, Canada; sshah@bccrc.ca.},
    journal = {Genome Res},
    month = nov,
    number = {11},
    pages = {1881--1893},
    title = {{TITAN: inference of copy number architectures in clonal cell populations from tumor whole-genome sequence data.}},
    volume = {24},
    year = {2014}
  }
  

• P. Rebeschini , R. Handel , Comparison Theorems for Gibbs Measures, Journal of Statistical Physics, vol. 157, no. 2, 234–281, Oct. 2014.
  
  The Dobrushin comparison theorem is a powerful tool to bound the difference between the marginals of high-dimensional probability distributions in terms of their local specifications. Originally introduced to prove uniqueness and decay of correlations of Gibbs measures, it has been widely used in statistical mechanics as well as in the analysis of algorithms on random fields and interacting Markov chains. However, the classical comparison theorem requires validity of the Dobrushin uniqueness criterion, essentially restricting its applicability in most models to a small subset of the natural parameter space. In this paper we develop generalized Dobrushin comparison theorems in terms of influences between blocks of sites, in the spirit of Dobrushin–Shlosman and Weitz, that substantially extend the range of applicability of the classical comparison theorem. Our proofs are based on the analysis of an associated family of Markov chains. We develop in detail an application of our main results to the analysis of sequential Monte Carlo algorithms for filtering in high dimension.

  @article{Rebeschini2014,
    author = {Rebeschini, Patrick and van Handel, Ramon},
    day = {01},
    doi = {10.1007/s10955-014-1087-7},
    issn = {1572-9613},
    journal = {Journal of Statistical Physics},
    month = oct,
    number = {2},
    pages = {234--281},
    title = {Comparison Theorems for Gibbs Measures},
    volume = {157},
    year = {2014},
    bdsk-url-1 = {https://doi.org/10.1007/s10955-014-1087-7},
    bdsk-url-2 = {http://dx.doi.org/10.1007/s10955-014-1087-7}
  }
  

• I. Nordentoft , P. Lamy , K. Birkenkamp-Demtröder , K. Shumansky , S. Vang , H. Hornshøj , M. Juul , P. Villesen , J. Hedegaard , A. Roth , K. Thorsen , S. Høyer , M. Borre , T. Reinert , N. Fristrup , L. Dyrskjøt , S. Shah , J. S. Pedersen , T. F. Ørntoft , Mutational context and diverse clonal development in early and late bladder cancer., Cell Rep, vol. 7, no. 5, 1649–1663, Jun. 2014.
  

  @article{NordentoftLamyBirkenkamp-DemtroederEtAl2014,
    author = {Nordentoft, Iver and Lamy, Philippe and Birkenkamp-Demtr{\"o}der, Karin and Shumansky, Karey and Vang, S{\o}ren and Hornsh{\o}j, Henrik and Juul, Malene and Villesen, Palle and Hedegaard, Jakob and Roth, Andrew and Thorsen, Kasper and H{\o}yer, S{\o}ren and Borre, Michael and Reinert, Thomas and Fristrup, Niels and Dyrskj{\o}t, Lars and Shah, Sohrab and Pedersen, Jakob Skou and {\O}rntoft, Torben F.},
    doi = {10.1016/j.celrep.2014.04.038},
    institution = {Department of Molecular Medicine, Aarhus University Hospital, Brendstrupgaardsvej 100, 8200 Aarhus, Denmark. Electronic address: orntoft@ki.au.dk.},
    journal = {Cell Rep},
    month = jun,
    number = {5},
    pages = {1649--1663},
    title = {{Mutational context and diverse clonal development in early and late bladder cancer.}},
    volume = {7},
    year = {2014}
  }
  

• A. Roth , J. Khattra , D. Yap , A. Wan , E. Laks , J. Biele , G. Ha , S. Aparicio , A. A. Bouchard-Côté , S. P. Shah , PyClone: statistical inference of clonal population structure in cancer., Nat Methods, vol. 11, no. 4, 396–398, Apr. 2014.
  

  @article{RothKhattraYapEtAl2014,
    author = {Roth, Andrew and Khattra, Jaswinder and Yap, Damian and Wan, Adrian and Laks, Emma and Biele, Justina and Ha, Gavin and Aparicio, Samuel and Bouchard-C{\^o}t{\'e}, Alexandre Alexandre and Shah, Sohrab P.},
    doi = {10.1038/nmeth.2883},
    institution = {Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, British Columbia, Canada.},
    journal = {Nat Methods},
    month = apr,
    number = {4},
    pages = {396--398},
    title = {{PyClone: statistical inference of clonal population structure in cancer.}},
    volume = {11},
    year = {2014}
  }
  

• A. Todeschini , F. Caron , M. Fuentes , P. Legrand , P. Del Moral , Biips: software for Bayesian inference with interacting particle systems, arXiv:1412.3779, 2014.
  
  @article{Todeschini2014,
    title = {Biips: software for Bayesian inference with interacting particle systems},
    author = {Todeschini, Adrien and Caron, Fran{\c{c}}ois and Fuentes, Marc and Legrand, Pierrick and Del Moral, Pierre},
    journal = {arXiv:1412.3779},
    year = {2014}
  }
  

• R. J. Evans , T. S. Richardson , Markovian acyclic directed mixed graphs for discrete data, Annals of Statistics, vol. 42, no. 4, 1452–1482, 2014.
  

  @article{evans:14,
    author = {Evans, R. J. and Richardson, T. S.},
    journal = {Annals of Statistics},
    number = {4},
    pages = {1452--1482},
    archiveprefix = {arXiv},
    eprint = {1301.6624},
    title = {Markovian acyclic directed mixed graphs for discrete data},
    volume = {42},
    year = {2014}
  }
  

• S. S. Mc Mahon , A. Sim , S. Filippi , R. Johnson , J. Liepe , D. Smith , M. P. H. Stumpf , Information theory and signal transduction systems: From molecular information processing to network inference., Seminars in cell & developmental biology, 2014.
  

  @article{McMahon:2014cs,
    author = {Mc Mahon, Siobhan S and Sim, Aaron and Filippi, Sarah and Johnson, Robert and Liepe, Juliane and Smith, Dominic and Stumpf, Michael P H},
    title = {{Information theory and signal transduction systems: From molecular information processing to network inference.}},
    journal = {Seminars in cell {\&} developmental biology},
    year = {2014}
  }
  

• A. L. MacLean , S. Filippi , M. P. H. Stumpf , The ecology in the hematopoietic stem cell niche determines the clinical outcome in chronic myeloid leukemia, PNAS, 2014.
  

  @article{MacLean:2014if,
    author = {MacLean, Adam L and Filippi, Sarah and Stumpf, Michael P H},
    title = {{The ecology in the hematopoietic stem cell niche determines the clinical outcome in chronic myeloid leukemia}},
    journal = {PNAS},
    year = {2014}
  }
  

• J. Liepe , P. Kirk , S. Filippi , T. Toni , C. P. Barnes , M. P. H. Stumpf , A framework for parameter estimation and model selection from experimental data in systems biology using approximate Bayesian computation., Nature Protocols, 2014.
  

  @article{Liepe:2014iw,
    author = {Liepe, Juliane and Kirk, Paul and Filippi, Sarah and Toni, Tina and Barnes, Chris P and Stumpf, Michael P H},
    title = {{A framework for parameter estimation and model selection from experimental data in systems biology using approximate Bayesian computation.}},
    journal = {Nature Protocols},
    year = {2014}
  }
  

• B. J. Gram-Hansen , An Insight Into: Quantum Random Walks, Master's thesis, 2014.
  
  @mastersthesis{graminsight,
    title = {An Insight Into: Quantum Random Walks},
    author = {Gram-Hansen, Bradley J},
    year = {2014}
  }
  

• R. Bardenet , A. Doucet , C. C. Holmes , Towards scaling up Markov chain Monte Carlo: an adaptive subsampling approach, in Proceedings of the 31st International Conference on Machine Learning (ICML-14), 2014, 405–413.
  

  @inproceedings{bardenet2014towards,
    title = {Towards scaling up Markov chain Monte Carlo: an adaptive subsampling approach},
    author = {Bardenet, R{\'e}mi and Doucet, Arnaud and Holmes, Chris C.},
    booktitle = {Proceedings of the 31st International Conference on Machine Learning (ICML-14)},
    pages = {405--413},
    year = {2014}
  }
  

• R. Bardenet , A. Doucet , C. C. Holmes , An adaptive subsampling approach for MCMC inference in large datasets, in Proceedings of The 31st International Conference on Machine Learning, 2014, 405–413.
  

  @inproceedings{bardenet2014adaptive,
    title = {An adaptive subsampling approach for MCMC inference in large datasets},
    author = {Bardenet, R{\'e}mi and Doucet, Arnaud and Holmes, Chris C.},
    booktitle = {Proceedings of The 31st International Conference on Machine Learning},
    pages = {405--413},
    year = {2014}
  }
  

• A. R. Taylor , J. A. Flegg , S. L. Nsobya , A. Yeka , M. R. Kamya , P. J. Rosenthal , G. Dorsey , C. H. Sibley , P. J. Guerin , C. C. Holmes , Estimation of malaria haplotype and genotype frequencies: a statistical approach to overcome the challenge associated with multiclonal infections, Malaria journal, vol. 13, no. 1, 102, 2014.
  

  @article{taylor2014estimation,
    title = {Estimation of malaria haplotype and genotype frequencies: a statistical approach to overcome the challenge associated with multiclonal infections},
    author = {Taylor, Aimee R and Flegg, Jennifer A and Nsobya, Samuel L and Yeka, Adoke and Kamya, Moses R and Rosenthal, Philip J and Dorsey, Grant and Sibley, Carol H and Guerin, Philippe J and Holmes, Chris C},
    journal = {Malaria journal},
    volume = {13},
    number = {1},
    pages = {102},
    year = {2014},
    publisher = {BioMed Central}
  }
  

• K. E. Pinnick , G. Nicholson , K. N. Manolopoulos , S. E. McQuaid , P. Valet , K. N. Frayn , N. Denton , J. L. Min , K. T. Zondervan , J. Fleckner , . others , Distinct developmental profile of lower-body adipose tissue defines resistance against obesity-associated metabolic complications, Diabetes, DB_140385, 2014.
  

  @article{pinnick2014distinct,
    title = {Distinct developmental profile of lower-body adipose tissue defines resistance against obesity-associated metabolic complications},
    author = {Pinnick, Katherine E and Nicholson, George and Manolopoulos, Konstantinos N and McQuaid, Siobh{\'a}n E and Valet, Philippe and Frayn, Keith N and Denton, Nathan and Min, Josine L and Zondervan, Krina T and Fleckner, Jan and others},
    journal = {Diabetes},
    pages = {DB\_140385},
    year = {2014},
    publisher = {American Diabetes Association}
  }
  

• R. M. Clifford , P. Robbe , S. Weller , A. T. Timbs , M. Titsias , A. Burns , M. Cabes , R. Alsolami , C. Yau , A. Hamblin , . others , Towards Response Prediction Using Integrated Genomics in Chronic Lymphocytic Leukaemia: Results on 250 First-Line FCR Treated Patients from UK Clinical Trials, Blood, vol. 124, no. 21, 1942–1942, 2014.
  

  @article{clifford2014towards,
    title = {Towards Response Prediction Using Integrated Genomics in Chronic Lymphocytic Leukaemia: Results on 250 First-Line FCR Treated Patients from UK Clinical Trials},
    author = {Clifford, Ruth M and Robbe, Pauline and Weller, Susanne and Timbs, Adele T and Titsias, Michalis and Burns, Adam and Cabes, Maite and Alsolami, Reem and Yau, Christopher and Hamblin, Angela and others},
    journal = {Blood},
    volume = {124},
    number = {21},
    pages = {1942--1942},
    year = {2014},
    publisher = {American Society of Hematology}
  }
  

• S. J. Knight , R. Clifford , P. Robbe , S. D. Ramos , A. Burns , A. T. Timbs , R. Alsolami , S. Weller , A. Hamblin , J. Mason , . others , The Identification of Further Minimal Regions of Overlap in Chronic Lymphocytic Leukemia Using High-Resolution SNP Arrays, Blood, vol. 124, no. 21, 3315–3315, 2014.
  

  @article{knight2014identification,
    title = {The Identification of Further Minimal Regions of Overlap in Chronic Lymphocytic Leukemia Using High-Resolution SNP Arrays},
    author = {Knight, Samantha JL and Clifford, Ruth and Robbe, Pauline and Ramos, Sara DC and Burns, Adam and Timbs, Adele T and Alsolami, Reem and Weller, Susanne and Hamblin, Angela and Mason, Joanne and others},
    journal = {Blood},
    volume = {124},
    number = {21},
    pages = {3315--3315},
    year = {2014},
    publisher = {American Society of Hematology}
  }
  

• N. Petousi , R. R. Copley , T. R. Lappin , S. E. Haggan , C. M. Bento , H. Cario , M. J. Percy , P. J. Ratcliffe , P. A. Robbins , M. F. McMullin , . others , Erythrocytosis associated with a novel missense mutation in the BPGM gene, haematologica, vol. 99, no. 10, e201–e204, 2014.
  

  @article{petousi2014erythrocytosis,
    title = {Erythrocytosis associated with a novel missense mutation in the BPGM gene},
    author = {Petousi, Nayia and Copley, Richard R and Lappin, Terence RJ and Haggan, Sally E and Bento, Celeste M and Cario, Holger and Percy, Melanie J and Ratcliffe, Peter J and Robbins, Peter A and McMullin, Mary Frances and others},
    journal = {haematologica},
    volume = {99},
    number = {10},
    pages = {e201--e204},
    year = {2014},
    publisher = {Haematologica}
  }
  

• J. Lee , S. Choi , Incremental tree-based inference with dependent normalized random measures, in Artificial Intelligence and Statistics (AISTATS), 2014.
  

  @inproceedings{Lee2014,
    title = {Incremental tree-based inference with dependent normalized random measures},
    author = {Lee, J. and Choi, S.},
    booktitle = {Artificial Intelligence and Statistics (AISTATS)},
    year = {2014}
  }
  

• C. J. Maddison , D. Tarlow , Structured Generative Models of Natural Source Code, in Proceedings of the 31st International Conference on Machine Learning, 2014.
  
  @inproceedings{maddison2014nsc,
    author = {Maddison, Chris J. and Tarlow, Daniel},
    booktitle = {Proceedings of the 31st International Conference on Machine Learning},
    title = {{Structured Generative Models of Natural Source Code}},
    year = {2014},
    bdsk-url-1 = {https://arxiv.org/pdf/1401.0514.pdf}
  }
  

• C. J. Maddison , D. Tarlow , T. Minka , A* Sampling, in Advances in Neural Information Processing Systems 27, 2014.
  
  The problem of drawing samples from a discrete distribution can be converted into a discrete optimization problem. In this work, we show how sampling from a continuous distribution can be converted into an optimization problem over continuous space. Central to the method is a stochastic process recently described in mathematical statistics that we call the Gumbel process. We present a new construction of the Gumbel process and A* sampling, a practical generic sampling algorithm that searches for the maximum of a Gumbel process using A* search. We analyze the correctness and convergence time of A* sampling and demonstrate empirically that it makes more efficient use of bound and likelihood evaluations than the most closely related adaptive rejection sampling-based algorithms.

  @inproceedings{maddison2014astar,
    author = {Maddison, Chris J. and Tarlow, Daniel and Minka, Tom},
    booktitle = {Advances in Neural Information Processing Systems 27},
    title = {{A* Sampling}},
    year = {2014},
    bdsk-url-1 = {https://arxiv.org/pdf/1411.0030.pdf}
  }
  

• K. Lokk , V. Modhukur , B. Rajashekar , K. Märtens , R. Mägi , R. Kolde , M. Koltšina , T. K. Nilsson , J. Vilo , A. Salumets , . others , DNA methylome profiling of human tissues identifies global and tissue-specific methylation patterns, Genome Biology, vol. 15, no. 4, 3248, 2014.
  

  @article{lokk2014dna,
    title = {DNA methylome profiling of human tissues identifies global and tissue-specific methylation patterns},
    author = {Lokk, Kaie and Modhukur, Vijayachitra and Rajashekar, Balaji and Märtens, Kaspar and Mägi, Reedik and Kolde, Raivo and Kolt{\v{s}}ina, Marina and Nilsson, Torbj{\"o}rn K and Vilo, Jaak and Salumets, Andres and others},
    journal = {Genome Biology},
    volume = {15},
    number = {4},
    pages = {3248},
    year = {2014},
    publisher = {BioMed Central}
  }
  

• D. Wraith , K. Mengersen , C. Alston , J. Rousseau , T. Hussein , . others , Using informative priors in the estimation of mixtures over time with application to aerosol particle size distributions, The Annals of Applied Statistics, vol. 8, no. 1, 232–258, 2014.
  

  @article{wraith2014using,
    title = {Using informative priors in the estimation of mixtures over time with application to aerosol particle size distributions},
    author = {Wraith, Darren and Mengersen, Kerrie and Alston, Clair and Rousseau, Judith and Hussein, Tareq and others},
    journal = {The Annals of Applied Statistics},
    volume = {8},
    number = {1},
    pages = {232--258},
    year = {2014},
    publisher = {Institute of Mathematical Statistics}
  }
  

• E. Gassiat , J. Rousseau , . others , About the posterior distribution in hidden Markov models with unknown number of states, Bernoulli, vol. 20, no. 4, 2039–2075, 2014.
  

  @article{gassiat2014posterior,
    title = {About the posterior distribution in hidden Markov models with unknown number of states},
    author = {Gassiat, Elisabeth and Rousseau, Judith and others},
    journal = {Bernoulli},
    volume = {20},
    number = {4},
    pages = {2039--2075},
    year = {2014},
    publisher = {Bernoulli Society for Mathematical Statistics and Probability}
  }
  

• P. Alquier , V. Cottet , N. Chopin , J. Rousseau , Bayesian matrix completion: prior specification, arXiv preprint arXiv:1406.1440, 2014.
  

  @article{alquier2014bayesian,
    title = {Bayesian matrix completion: prior specification},
    author = {Alquier, Pierre and Cottet, Vincent and Chopin, Nicolas and Rousseau, Judith},
    journal = {arXiv preprint arXiv:1406.1440},
    year = {2014}
  }
  

• S. Petrone , S. Rizzelli , J. Rousseau , C. Scricciolo , Empirical Bayes methods in classical and Bayesian inference, Metron, vol. 72, no. 2, 201–215, 2014.
  

  @article{petrone2014empirical,
    title = {Empirical Bayes methods in classical and Bayesian inference},
    author = {Petrone, Sonia and Rizzelli, Stefano and Rousseau, Judith and Scricciolo, Catia},
    journal = {Metron},
    volume = {72},
    number = {2},
    pages = {201--215},
    year = {2014},
    publisher = {Springer}
  }
  

• S. Petrone , J. Rousseau , C. Scricciolo , Bayes and empirical Bayes: do they merge?, Biometrika, vol. 101, no. 2, 285–302, 2014.
  
  @article{doi:10.1093/biomet/ast067,
    author = {Petrone, S. and Rousseau, J. and Scricciolo, C.},
    title = {Bayes and empirical Bayes: do they merge?},
    journal = {Biometrika},
    volume = {101},
    number = {2},
    pages = {285-302},
    year = {2014},
    doi = {10.1093/biomet/ast067},
    eprint = {/oup/backfile/content_public/journal/biomet/101/2/10.1093/biomet/ast067/2/ast067.pdf}
  }
  

• J. Marin , N. S. Pillai , C. P. Robert , J. Rousseau , Relevant statistics for Bayesian model choice, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol. 76, no. 5, 833–859, 2014.
  
  @article{RSSB:RSSB12056,
    author = {Marin, Jean-Michel and Pillai, Natesh S. and Robert, Christian P. and Rousseau, Judith},
    title = {Relevant statistics for Bayesian model choice},
    journal = {Journal of the Royal Statistical Society: Series B (Statistical Methodology)},
    volume = {76},
    number = {5},
    issn = {1467-9868},
    doi = {10.1111/rssb.12056},
    pages = {833--859},
    keywords = {Ancillarity, Approximate Bayesian computation, Asymptotics, Bayes factor, Bayesian model choice, Gaussianity, Likelihood-free methods, Sufficiency},
    year = {2014}
  }
  

• P. Rubin-Delanchy , N. A. Heard , A test for dependence between two point processes on the real line, arXiv preprint:1408.3845, 2014.
  

  @article{rubin2014test,
    title = {A test for dependence between two point processes on the real line},
    author = {Rubin-Delanchy, Patrick and Heard, Nicholas A},
    journal = {arXiv preprint:1408.3845},
    year = {2014}
  }
  

• P. Rubin-Delanchy , D. J. Lawson , M. J. Turcotte , N. A. Heard , N. M. Adams , Three statistical approaches to sessionizing network flow data, in Proceedings of the IEEE Joint Intelligence and Security Informatics Conference (JISIC), 2014.
  

  @inproceedings{cyber1,
    author = {Rubin-Delanchy, Patrick and Lawson, Daniel J and Turcotte, Melissa JM and Heard, Nicholas A and Adams, Niall M},
    title = {Three statistical approaches to sessionizing network flow data},
    booktitle = {Proceedings of the IEEE Joint Intelligence and Security Informatics Conference (JISIC)},
    year = {2014}
  }
  

• N. A. Heard , D. J. Lawson , P. Rubin-Delanchy , Filtering automated polling traffic in computer network flow data, in Proceedings of the IEEE Joint Intelligence and Security Informatics Conference (JISIC), 2014.
  

  @inproceedings{cyber2,
    author = {Heard, Nicholas A and Lawson, Daniel J and Rubin-Delanchy, Patrick},
    title = {Filtering automated polling traffic in computer network flow data},
    booktitle = {Proceedings of the IEEE Joint Intelligence and Security Informatics Conference (JISIC)},
    year = {2014}
  }
  

• D. J. Lawson , P. Rubin-Delanchy , N. A. Heard , N. M. Adams , Statistical frameworks for detecting tunnelling in cyber defence using Big Data, in Proceedings of the IEEE Joint Intelligence and Security Informatics Conference (JISIC), 2014.
  

  @inproceedings{cyber3,
    author = {Lawson, Daniel J and Rubin-Delanchy, Patrick and Heard, Nicholas A and Adams, Niall M},
    title = {Statistical frameworks for detecting tunnelling in cyber defence using {B}ig {D}ata},
    booktitle = {Proceedings of the IEEE Joint Intelligence and Security Informatics Conference (JISIC)},
    year = {2014}
  }
  

• T. Rukat , S. A. Reinsberg , Information Criteria weighted Parameter Estimates in DCE-MRI, in Proc. Intl. Soc. Mag. Reson. Med. 22 (2014) 2741, 2014, no. 2741.
  

  @inproceedings{Rukat2014,
    title = {Information Criteria weighted Parameter Estimates in {DC}E-{MRI}},
    author = {Rukat, Tammo and Reinsberg, Stefan A.},
    booktitle = {Proc. Intl. Soc. Mag. Reson. Med. 22 (2014) 2741},
    year = {2014},
    number = {2741},
    file = {Rukat2014a_Information Criteria weighted Parameter Estimates in DCE-MRI.pdf:Rukat2014a.pdf:PDF;Rukat2014a_Information Criteria weighted Parameter Estimates in DCE-MRI_Abstract.pdf:Rukat2014a_Information Criteria weighted Parameter Estimates in DCE-MRI_Abstract.pdf:PDF},
    owner = {tammo},
    timestamp = {2014.09.18}
  }
  

• K. Chwialkowski , D. Sejdinovic , A. Gretton , A Wild Bootstrap for Degenerate Kernel Tests, in Advances in Neural Information Processing Systems (NeurIPS), vol. 27, 2014, 3608–3616.
  
  A wild bootstrap method for nonparametric hypothesis tests based on kernel distribution embeddings is proposed. This bootstrap method is used to construct provably consistent tests that apply to random processes, for which the naive permutation-based bootstrap fails. It applies to a large group of kernel tests based on V-statistics, which are degenerate under the null hypothesis, and non-degenerate elsewhere. To illustrate this approach, we construct a two-sample test, an instantaneous independence test and a multiple lag independence test for time series. In experiments, the wild bootstrap gives strong performance on synthetic examples, on audio data, and in performance benchmarking for the Gibbs sampler.

  @incollection{ChwSejGre2014,
    author = {Chwialkowski, Kacper and Sejdinovic, Dino and Gretton, Arthur},
    booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
    pages = {3608--3616},
    title = {A Wild Bootstrap for Degenerate Kernel Tests},
    volume = {27},
    year = {2014},
    bdsk-url-1 = {http://papers.nips.cc/paper/5452-a-wild-bootstrap-for-degenerate-kernel-tests.pdf},
    bdsk-url-2 = {https://github.com/kacperChwialkowski/wildBootstrap},
    bdsk-url-3 = {http://research.microsoft.com/apps/video/?id=240378}
  }
  

• D. Sejdinovic , H. Strathmann , M. Lomeli , C. Andrieu , A. Gretton , Kernel Adaptive Metropolis-Hastings, in International Conference on Machine Learning (ICML), 2014, 1665–1673.
  
  A Kernel Adaptive Metropolis-Hastings algorithm is introduced, for the purpose of sampling from a target distribution with strongly nonlinear support. The algorithm embeds the trajectory of the Markov chain into a reproducing kernel Hilbert space (RKHS), such that the feature space covariance of the samples informs the choice of proposal. The procedure is computationally efficient and straightforward to implement, since the RKHS moves can be integrated out analytically: our proposal distribution in the original space is a normal distribution whose mean and covariance depend on where the current sample lies in the support of the target distribution, and adapts to its local covariance structure. Furthermore, the procedure requires neither gradients nor any other higher order information about the target, making it particularly attractive for contexts such as Pseudo-Marginal MCMC. Kernel Adaptive Metropolis-Hastings outperforms competing fixed and adaptive samplers on multivariate, highly nonlinear target distributions, arising in both real-world and synthetic examples.

  @inproceedings{SejStrGarAndGre14,
    author = {Sejdinovic, D. and Strathmann, H. and Lomeli, M.G. and Andrieu, C. and Gretton, A.},
    booktitle = {International Conference on Machine Learning (ICML)},
    code = {https://github.com/karlnapf/kameleon-mcmc},
    pages = {1665--1673},
    title = {{Kernel Adaptive Metropolis-Hastings}},
    year = {2014},
    bdsk-url-1 = {http://jmlr.org/proceedings/papers/v32/sejdinovic14.pdf},
    bdsk-url-2 = {http://jmlr.org/proceedings/papers/v32/sejdinovic14-supp.zip}
  }
  

• O. Johnson , D. Sejdinovic , J. Cruise , R. Piechocki , A. Ganesh , Non-Parametric Change-Point Estimation using String Matching Algorithms, Methodology and Computing in Applied Probability, vol. 16, no. 4, 987–1008, 2014.
  
  Given the output of a data source taking values in a finite alphabet, we wish to estimate change-points, that is times when the statistical properties of the source change. Motivated by ideas of match lengths in information theory, we introduce a novel non-parametric estimator which we call CRECHE (CRossings Enumeration CHange Estimator). We present simulation evidence that this estimator performs well, both for simulated sources and for real data formed by concatenating text sources. For example, we show that we can accurately estimate the point at which a source changes from a Markov chain to an IID source with the same stationary distribution. Our estimator requires no assumptions about the form of the source distribution, and avoids the need to estimate its probabilities. Further, establishing a fluid limit and using martingale arguments.

  @article{JohSejCruPieGan2014,
    author = {Johnson, Oliver and Sejdinovic, Dino and Cruise, James and Piechocki, Robert and Ganesh, Ayalvadi},
    doi = {10.1007/s11009-013-9359-2},
    issn = {1387-5841},
    journal = {Methodology and Computing in Applied Probability},
    number = {4},
    pages = {987-1008},
    publisher = {Springer US},
    title = {{Non-Parametric Change-Point Estimation using String Matching Algorithms}},
    volume = {16},
    year = {2014},
    bdsk-url-1 = {http://dx.doi.org/10.1007/s11009-013-9359-2}
  }
  

• M. Welling , Y. W. Teh , C. Andrieu , J. Kominiarczuk , T. Meeds , B. Shahbaba , S. Vollmer , Bayesian Inference and Big Data: A Snapshot from a Workshop, ISBA Bulletin, 2014.
  
  @article{WelTehAnd2014a,
    author = {Welling, M. and Teh, Y. W. and Andrieu, C. and Kominiarczuk, J. and Meeds, T. and Shahbaba, B. and Vollmer, S.},
    journal = {ISBA Bulletin},
    title = {Bayesian Inference and Big Data: A Snapshot from a Workshop},
    year = {2014},
    bdsk-url-1 = {https://bayesian.org/sites/default/files/fm/bulletins/1412.pdf}
  }
  

• M. Xu , B. Lakshminarayanan , Y. W. Teh , J. Zhu , B. Zhang , Distributed Bayesian Posterior Sampling via Moment Sharing, in Advances in Neural Information Processing Systems, 2014.
  
  Project: sgmcmc

  We propose a distributed Markov chain Monte Carlo (MCMC) inference algorithm for large scale Bayesian posterior simulation. We assume that the dataset is partitioned and stored across nodes of a cluster. Our procedure involves an independent MCMC posterior sampler at each node based on its local partition of the data. Moment statistics of the local posteriors are collected from each sampler and propagated across the cluster using expectation propagation message passing with low communication costs. The moment sharing scheme improves posterior estimation quality by enforcing agreement among the samplers. We demonstrate the speed and inference quality of our method with empirical studies on Bayesian logistic regression and sparse linear regression with a spike-and-slab prior.

  @inproceedings{XuLakTeh2014a,
    author = {Xu, M. and Lakshminarayanan, B. and Teh, Y. W. and Zhu, J. and Zhang, B.},
    booktitle = {Advances in Neural Information Processing Systems},
    title = {Distributed {B}ayesian Posterior Sampling via Moment Sharing},
    year = {2014},
    bdsk-url-1 = {http://papers.nips.cc/paper/5596-distributed-bayesian-posterior-sampling-via-moment-sharing},
    bdsk-url-2 = {http://papers.nips.cc/paper/5596-distributed-bayesian-posterior-sampling-via-moment-sharing.pdf},
    bdsk-url-3 = {https://github.com/BigBayes/SMS}
  }
  

• S. Favaro , M. Lomeli , Y. W. Teh , On a Class of σ-stable Poisson-Kingman Models and an Effective Marginalized Sampler, Statistics and Computing, 2014.
  
  Project: bigbayes

  We investigate the use of a large class of discrete random probability measures, which is referred to as the class Q, in the context of Bayesian nonparametric mixture modeling. The class Q encompasses both the the two-parameter Poisson–Dirichlet process and the normalized generalized Gamma process, thus allowing us to comparatively study the inferential advantages of these two well-known nonparametric priors. Apart from a highly flexible parameterization, the distinguishing feature of the class Q is the availability of a tractable posterior distribution. This feature, in turn, leads to derive an efficient marginal MCMC algorithm for posterior sampling within the framework of mixture models. We demonstrate the efficacy of our modeling framework on both one-dimensional and multi-dimensional datasets.

  @article{FavLomTeh2014a,
    author = {Favaro, S. and Lomeli, M. and Teh, Y. W.},
    doi = {10.1007/s11222-014-9499-4},
    journal = {Statistics and Computing},
    title = {On a Class of {$\sigma$}-stable {P}oisson-{K}ingman Models and an Effective Marginalized Sampler},
    year = {2014},
    bdsk-url-1 = {http://projecteuclid.org/euclid.ejs/1433982944},
    bdsk-url-2 = {http://dx.doi.org/10.1007/s11222-014-9499-4}
  }
  

• T. Herlau , M. Mörup , Y. W. Teh , M. N. Schmidt , Adaptive Reconfiguration Moves for Dirichlet Mixtures, submitted, 2014.
  
  Bayesian mixture models are widely applied for unsupervised learning and exploratory data analysis. Markov chain Monte Carlo based on Gibbs sampling and split-merge moves are widely used for inference in these models. However, both methods are restricted to limited types of transitions and suffer from torpid mixing and low accept rates even for problems of modest size. We propose a method that considers a broader range of transitions that are close to equilibrium by exploiting multiple chains in parallel and using the past states adaptively to inform the proposal distribution. The method significantly improves on Gibbs and split-merge sampling as quantified using convergence diagnostics and acceptance rates. Adaptive MCMC methods which use past states to inform the proposal distribution has given rise to many ingenious sampling schemes for continuous problems and the present work can be seen as an important first step in bringing these benefits to partition-based problems

  @article{HerMorTeh2014a,
    author = {Herlau, T. and M\"orup, M. and Teh, Y. W. and Schmidt, M. N.},
    journal = {submitted},
    title = {Adaptive Reconfiguration Moves for Dirichlet Mixtures},
    year = {2014},
    bdsk-url-1 = {https://arxiv.org/pdf/1406.0071v1.pdf}
  }
  

• S. Favaro , M. Lomeli , B. Nipoti , Y. W. Teh , On the Stick-Breaking Representation of σ-stable Poisson-Kingman Models, Electronic Journal of Statistics, vol. 8, 1063–1085, 2014.
  
  Project: bigbayes

  In this paper we investigate the stick-breaking representation for the class of σ-stable Poisson-Kingman models, also known as Gibbs-type random probability measures. This class includes as special cases most of the discrete priors commonly used in Bayesian nonparametrics, such as the two parameter Poisson-Dirichlet process and the normalized generalized Gamma process. Under the assumption σ=u/v, for any coprime integers 1≤u<v such that u/v≤1/2, we show that a σ-stable Poisson-Kingman model admits an explicit stick-breaking representation in terms of random variables which are obtained by suitably transforming Gamma random variables and products of independent Beta and Gamma random variables.

  @article{FavLomNip2014a,
    author = {Favaro, S. and Lomeli, M. and Nipoti, B. and Teh, Y. W.},
    doi = {10.1214/14-EJS921},
    journal = {Electronic Journal of Statistics},
    pages = {1063-1085},
    title = {On the Stick-Breaking Representation of {$\sigma$}-stable {P}oisson-{K}ingman Models},
    volume = {8},
    year = {2014},
    bdsk-url-1 = {http://projecteuclid.org/euclid.ejs/1407243242},
    bdsk-url-2 = {http://dx.doi.org/10.1214/14-EJS921},
    bdsk-url-3 = {http://projecteuclid.org/download/pdfview_1/euclid.ejs/1407243242}
  }
  

• B. Lakshminarayanan , D. Roy , Y. W. Teh , Mondrian Forests: Efficient Online Random Forests, in Advances in Neural Information Processing Systems (NeurIPS), 2014.
  
  Project: bigbayes

  @inproceedings{LakRoyTeh2014a,
    author = {Lakshminarayanan, B. and Roy, D. and Teh, Y. W.},
    booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
    title = {{M}ondrian Forests: Efficient Online Random Forests},
    year = {2014},
    bdsk-url-1 = {http://papers.nips.cc/paper/5234-mondrian-forests-efficient-online-random-forests},
    bdsk-url-2 = {http://papers.nips.cc/paper/5234-mondrian-forests-efficient-online-random-forests.pdf},
    bdsk-url-3 = {http://papers.nips.cc/paper/5234-mondrian-forests-efficient-online-random-forests-supplemental.zip},
    bdsk-url-4 = {https://github.com/balajiln/mondrianforest}
  }
  

• B. Paige , F. Wood , A. Doucet , Y. W. Teh , Asynchronous Anytime Sequential Monte Carlo, in Advances in Neural Information Processing Systems (NeurIPS), 2014.
  
  Project: sgmcmc

  We introduce a new sequential Monte Carlo algorithm we call the particle cascade. The particle cascade is an asynchronous, anytime alternative to traditional sequential Monte Carlo algorithms that is amenable to parallel and distributed implementations. It uses no barrier synchronizations which leads to improved particle throughput and memory efficiency. It is an anytime algorithm in the sense that it can be run forever to emit an unbounded number of particles while keeping within a fixed memory budget. We prove that the particle cascade provides an unbiased marginal likelihood estimator which can be straightforwardly plugged into existing pseudo-marginal methods.

  @inproceedings{PaiWooDou2014a,
    author = {Paige, B. and Wood, F. and Doucet, A. and Teh, Y. W.},
    booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
    title = {Asynchronous Anytime Sequential {M}onte {C}arlo},
    year = {2014},
    bdsk-url-1 = {http://papers.nips.cc/paper/5450-asynchronous-anytime-sequential-monte-carlo},
    bdsk-url-2 = {http://papers.nips.cc/paper/5450-asynchronous-anytime-sequential-monte-carlo.pdf},
    bdsk-url-3 = {http://papers.nips.cc/paper/5450-asynchronous-anytime-sequential-monte-carlo-supplemental.zip}
  }
  

• F. Caron , Y. W. Teh , B. T. Murphy , Bayesian Nonparametric Plackett-Luce Models for the Analysis of Preferences for College Degree Programmes, Annals of Applied Statistics, vol. 8, no. 2, 1145–1181, 2014.
  
  In this paper we propose a Bayesian nonparametric model for clustering partial ranking data. We start by developing a Bayesian nonparametric extension of the popular Plackett–Luce choice model that can handle an infinite number of choice items. Our framework is based on the theory of random atomic measures, with the prior specified by a completely random measure. We characterise the posterior distribution given data, and derive a simple and effective Gibbs sampler for posterior simulation. We then develop a Dirichlet process mixture extension of our model and apply it to investigate the clustering of preferences for college degree programmes amongst Irish secondary school graduates. The existence of clusters of applicants who have similar preferences for degree programmes is established and we determine that subject matter and geographical location of the third level institution characterise these clusters.

  @article{CarTehMur2014a,
    author = {Caron, F. and Teh, Y. W. and Murphy, B. T.},
    doi = {10.1214/14-AOAS717},
    journal = {Annals of Applied Statistics},
    number = {2},
    pages = {1145-1181},
    title = {{B}ayesian Nonparametric {P}lackett-{L}uce Models for the Analysis of Preferences for College Degree Programmes},
    volume = {8},
    year = {2014},
    bdsk-url-1 = {https://projecteuclid.org/euclid.aoas/1404229529},
    bdsk-url-2 = {http://dx.doi.org/10.1214/14-AOAS717},
    bdsk-url-3 = {https://projecteuclid.org/download/pdfview_1/euclid.aoas/1404229529},
    bdsk-url-4 = {http://www.stats.ox.ac.uk/\\~{}caron/code/bnppl/index.html}
  }
  

2013

• T. Rukat , Parameter Uncertainties in Tracer Kinetic Modelling of Dynamic Contrast Enhanced MRI, Master's thesis, Humboldt University Berlin / University of British Columbia, 2013.
  

  @mastersthesis{Rukat2013a,
    title = {Parameter Uncertainties in Tracer Kinetic Modelling of Dynamic Contrast Enhanced {MRI}},
    author = {Rukat, Tammo},
    school = {Humboldt University Berlin / University of British Columbia},
    year = {2013},
    month = oct,
    file = {Rukat2013a_Parameter Uncertainties in Tracer Kinetic Modelling of Dynamic Contrast Enhanced MRI.pdf:Rukat2013a.pdf:PDF},
    owner = {tammo},
    timestamp = {2014.09.18}
  }
  

• D. Sejdinovic , B. Sriperumbudur , A. Gretton , K. Fukumizu , Equivalence of distance-based and RKHS-based statistics in hypothesis testing, Annals of Statistics, vol. 41, no. 5, 2263–2291, Oct. 2013.
  
  We provide a unifying framework linking two classes of statistics used in two-sample and independence testing: on the one hand, the energy distances and distance covariances from the statistics literature; on the other, maximum mean discrepancies (MMD), that is, distances between embeddings of distributions to reproducing kernel Hilbert spaces (RKHS), as established in machine learning. In the case where the energy distance is computed with a semimetric of negative type, a positive definite kernel, termed distance kernel, may be defined such that the MMD corresponds exactly to the energy distance. Conversely, for any positive definite kernel, we can interpret the MMD as energy distance with respect to some negative-type semimetric. This equivalence readily extends to distance covariance using kernels on the product space. We determine the class of probability distributions for which the test statistics are consistent against all alternatives. Finally, we investigate the performance of the family of distance kernels in two-sample and independence tests: we show in particular that the energy distance most commonly employed in statistics is just one member of a parametric family of kernels, and that other choices from this family can yield more powerful tests.

  @article{SejSriGreFuk2013,
    author = {Sejdinovic, Dino and Sriperumbudur, Bharath and Gretton, Arthur and Fukumizu, Kenji},
    doi = {10.1214/13-AOS1140},
    journal = {Annals of Statistics},
    month = oct,
    number = {5},
    pages = {2263--2291},
    title = {{Equivalence of distance-based and RKHS-based statistics in hypothesis testing}},
    volume = {41},
    year = {2013},
    bdsk-url-1 = {http://dx.doi.org/10.1214/13-AOS1140}
  }
  

• A. Bashashati , G. Ha , A. Tone , J. Ding , L. M. Prentice , A. Roth , J. Rosner , K. Shumansky , S. Kalloger , J. Senz , W. Yang , M. McConechy , N. Melnyk , M. Anglesio , M. T. Y. Luk , K. Tse , T. Zeng , R. Moore , Y. Zhao , M. A. Marra , B. Gilks , S. Yip , D. G. Huntsman , J. N. McAlpine , S. P. Shah , Distinct evolutionary trajectories of primary high-grade serous ovarian cancers revealed through spatial mutational profiling., J Pathol, vol. 231, no. 1, 21–34, Sep. 2013.
  

  @article{BashashatiHaToneEtAl2013,
    author = {Bashashati, Ali and Ha, Gavin and Tone, Alicia and Ding, Jiarui and Prentice, Leah M. and Roth, Andrew and Rosner, Jamie and Shumansky, Karey and Kalloger, Steve and Senz, Janine and Yang, Winnie and McConechy, Melissa and Melnyk, Nataliya and Anglesio, Michael and Luk, Margaret T Y. and Tse, Kane and Zeng, Thomas and Moore, Richard and Zhao, Yongjun and Marra, Marco A. and Gilks, Blake and Yip, Stephen and Huntsman, David G. and McAlpine, Jessica N. and Shah, Sohrab P.},
    doi = {10.1002/path.4230},
    institution = {Department of Molecular Oncology, British Columbia Cancer Agency, Vancouver, BC, Canada.},
    journal = {J Pathol},
    month = sep,
    number = {1},
    pages = {21--34},
    title = {{Distinct evolutionary trajectories of primary high-grade serous ovarian cancers revealed through spatial mutational profiling.}},
    volume = {231},
    year = {2013}
  }
  

• R. D. Morin , K. Mungall , E. Pleasance , A. J. Mungall , R. Goya , R. D. Huff , D. W. Scott , J. Ding , A. Roth , R. Chiu , R. D. Corbett , F. C. Chan , M. Mendez-Lago , D. L. Trinh , M. Bolger-Munro , G. Taylor , A. Hadj Khodabakhshi , S. Ben-Neriah , J. Pon , B. Meissner , B. Woolcock , N. Farnoud , S. Rogic , E. L. Lim , N. A. Johnson , S. Shah , S. Jones , C. Steidl , R. Holt , I. Birol , R. Moore , J. M. Connors , R. D. Gascoyne , M. A. Marra , Mutational and structural analysis of diffuse large B-cell lymphoma using whole-genome sequencing., Blood, vol. 122, no. 7, 1256–1265, Aug. 2013.
  

  @article{MorinMungallPleasanceEtAl2013,
    author = {Morin, Ryan D. and Mungall, Karen and Pleasance, Erin and Mungall, Andrew J. and Goya, Rodrigo and Huff, Ryan D. and Scott, David W. and Ding, Jiarui and Roth, Andrew and Chiu, Readman and Corbett, Richard D. and Chan, Fong Chun and Mendez-Lago, Maria and Trinh, Diane L. and Bolger-Munro, Madison and Taylor, Greg and {Hadj Khodabakhshi}, Alireza and Ben-Neriah, Susana and Pon, Julia and Meissner, Barbara and Woolcock, Bruce and Farnoud, Noushin and Rogic, Sanja and Lim, Emilia L. and Johnson, Nathalie A. and Shah, Sohrab and Jones, Steven and Steidl, Christian and Holt, Robert and Birol, Inanc and Moore, Richard and Connors, Joseph M. and Gascoyne, Randy D. and Marra, Marco A.},
    doi = {10.1182/blood-2013-02-483727},
    institution = {Genome Sciences Centre, BC Cancer Agency, Vancouver, Canada.},
    journal = {Blood},
    month = aug,
    number = {7},
    pages = {1256--1265},
    title = {{Mutational and structural analysis of diffuse large B-cell lymphoma using whole-genome sequencing.}},
    volume = {122},
    year = {2013}
  }
  

• A. Todeschini , F. Caron , M. Chavent , Probabilistic low-rank matrix completion with adaptive spectral regularization algorithms, in Advances in Neural Information Processing Systems (NeurIPS), 2013, 845–853.
  
  @inproceedings{Todeschini2013,
    title = {Probabilistic low-rank matrix completion with adaptive spectral regularization algorithms},
    author = {Todeschini, Adrien and Caron, Fran{\c{c}}ois and Chavent, Marie},
    booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
    year = {2013},
    pages = {845--853},
    file = {5005-probabilistic-low-rank-matrix-completion-with-adaptive-spectral-regularization-algorithms.pdf:http\://papers.nips.cc/paper/5005-probabilistic-low-rank-matrix-completion-with-adaptive-spectral-regularization-algorithms.pdf:PDF}
  }
  

• G. Deligiannidis , S. Utev , Variance of partial sums of stationary sequences, Annals of Probability, vol. 41, no. 5, 3606–3616, 2013.
  

  @article{deligiannidis2013variance,
    author = {Deligiannidis, G. and Utev, S.},
    title = {Variance of partial sums of stationary sequences},
    journal = {Annals of Probability},
    year = {2013},
    volume = {41},
    number = {5},
    pages = {3606-3616}
  }
  

• R. J. Evans , A. Forcina , Two algorithms for fitting constrained marginal models, Computational Statistics and Data Analysis, vol. 66, 1–7, 2013.
  

  @article{evans:forcina:13,
    author = {Evans, R. J. and Forcina, A.},
    journal = {Computational Statistics and Data Analysis},
    pages = {1--7},
    title = {Two algorithms for fitting constrained marginal models},
    volume = {66},
    year = {2013}
  }
  

• I. Shpitser , T. Richardson , R. J. Evans , J. Robins , Sparse nested Markov models with log-linear parameters, in Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence (UAI-13), 2013, 576–585.
  

  @inproceedings{shpitser:13,
    author = {Shpitser, I. and Richardson, T.\\~{}S. and Evans, R. J. and Robins, J.\\~{}M.},
    booktitle = {Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence (UAI-13)},
    title = {Sparse nested Markov models with log-linear parameters},
    pages = {576-585},
    year = {2013}
  }
  

• R. J. Evans , T. S. Richardson , Marginal log-linear parameters for graphical Markov models, Journal of the Royal Statistical Society: Series B, vol. 75, no. 4, 743–768, 2013.
  

  @article{evans:13,
    title = {Marginal log-linear parameters for graphical {M}arkov models},
    author = {Evans, R. J. and Richardson, T. S.},
    journal = {Journal of the Royal Statistical Society: Series B},
    volume = {75},
    number = {4},
    pages = {743--768},
    year = {2013},
    publisher = {Wiley Online Library}
  }
  

• D. Silk , S. Filippi , M. P. H. Stumpf , Optimizing threshold-schedules for sequential approximate Bayesian computation: applications to molecular systems., Statistical Applications in Genetics and Molecular Biology, 2013.
  

  @article{Silk:2013bi,
    author = {Silk, Daniel and Filippi, Sarah and Stumpf, Michael P H},
    title = {{Optimizing threshold-schedules for sequential approximate Bayesian computation: applications to molecular systems.}},
    journal = {Statistical Applications in Genetics and Molecular Biology},
    year = {2013}
  }
  

• S. Filippi , C. P. Barnes , J. Cornebise , M. P. H. Stumpf , On optimality of kernels for approximate Bayesian computation using sequential Monte Carlo., Statistical Applications in Genetics and Molecular Biology, 2013.
  

  @article{Filippi:2013io,
    author = {Filippi, Sarah and Barnes, Chris P and Cornebise, Julien and Stumpf, Michael P H},
    title = {{On optimality of kernels for approximate Bayesian computation using sequential Monte Carlo.}},
    journal = {Statistical Applications in Genetics and Molecular Biology},
    year = {2013}
  }
  

• J. Liepe , S. Filippi , M. Komorowski , M. P. H. Stumpf , Maximizing the Information Content of Experiments in Systems Biology, PLoS computational biology, 2013.
  

  @article{Liepe:2013cl,
    author = {Liepe, Juliane and Filippi, Sarah and Komorowski, Micha{\l} and Stumpf, Michael P H},
    title = {{Maximizing the Information Content of Experiments in Systems Biology}},
    journal = {PLoS computational biology},
    year = {2013}
  }
  

• D. D. Denison , M. H. Hansen , C. C. Holmes , B. Mallick , B. Yu , Nonlinear estimation and classification. Springer Science & Business Media, 2013.
  

  @misc{denison2013nonlinear,
    title = {Nonlinear estimation and classification},
    author = {Denison, David D and Hansen, Mark H and Holmes, Chris C and Mallick, Bani and Yu, Bin},
    year = {2013},
    publisher = {Springer Science \& Business Media}
  }
  

• K. J. Livak , Q. F. Wills , A. J. Tipping , K. Datta , R. Mittal , A. J. Goldson , D. W. Sexton , C. C. Holmes , Methods for qPCR gene expression profiling applied to 1440 lymphoblastoid single cells, Methods, vol. 59, no. 1, 71–79, 2013.
  

  @article{livak2013methods,
    title = {Methods for qPCR gene expression profiling applied to 1440 lymphoblastoid single cells},
    author = {Livak, Kenneth J and Wills, Quin F and Tipping, Alex J and Datta, Krishnalekha and Mittal, Rowena and Goldson, Andrew J and Sexton, Darren W and Holmes, Chris C},
    journal = {Methods},
    volume = {59},
    number = {1},
    pages = {71--79},
    year = {2013},
    publisher = {Academic Press}
  }
  

• E. Domingo , R. Ramamoorthy , D. Oukrif , D. Rosmarin , M. Presz , H. Wang , H. Pulker , H. Lockstone , T. Hveem , T. Cranston , . others , Use of multivariate analysis to suggest a new molecular classification of colorectal cancer, The Journal of pathology, vol. 229, no. 3, 441–448, 2013.
  

  @article{domingo2013use,
    title = {Use of multivariate analysis to suggest a new molecular classification of colorectal cancer},
    author = {Domingo, Enric and Ramamoorthy, Rajarajan and Oukrif, Dahmane and Rosmarin, Daniel and Presz, Michal and Wang, Haitao and Pulker, Hannah and Lockstone, Helen and Hveem, Tarjei and Cranston, Treena and others},
    journal = {The Journal of pathology},
    volume = {229},
    number = {3},
    pages = {441--448},
    year = {2013},
    publisher = {John Wiley \& Sons, Ltd}
  }
  

• C. Palles , J. Cazier , K. M. Howarth , E. Domingo , A. M. Jones , P. Broderick , Z. Kemp , S. L. Spain , E. Guarino , I. Salguero , . others , Germline mutations affecting the proofreading domains of POLE and POLD1 predispose to colorectal adenomas and carcinomas, Nature genetics, vol. 45, no. 2, 136–144, 2013.
  

  @article{palles2013germline,
    title = {Germline mutations affecting the proofreading domains of POLE and POLD1 predispose to colorectal adenomas and carcinomas},
    author = {Palles, Claire and Cazier, Jean-Baptiste and Howarth, Kimberley M and Domingo, Enric and Jones, Angela M and Broderick, Peter and Kemp, Zoe and Spain, Sarah L and Guarino, Estrella and Salguero, Israel and others},
    journal = {Nature genetics},
    volume = {45},
    number = {2},
    pages = {136--144},
    year = {2013},
    publisher = {Nature Research}
  }
  

• J. Becker , C. Yau , J. M. Hancock , C. C. Holmes , NucleoFinder: a statistical approach for the detection of nucleosome positions, Bioinformatics, vol. 29, no. 6, 711–716, 2013.
  

  @article{becker2013nucleofinder,
    title = {NucleoFinder: a statistical approach for the detection of nucleosome positions},
    author = {Becker, Jeremie and Yau, Christopher and Hancock, John M and Holmes, Chris C},
    journal = {Bioinformatics},
    volume = {29},
    number = {6},
    pages = {711--716},
    year = {2013},
    publisher = {Oxford University Press}
  }
  

• Q. F. Wills , K. J. Livak , A. J. Tipping , T. Enver , A. J. Goldson , D. W. Sexton , C. C. Holmes , Single-cell gene expression analysis reveals genetic associations masked in whole-tissue experiments, Nature biotechnology, vol. 31, no. 8, 748–752, 2013.
  

  @article{wills2013single,
    title = {Single-cell gene expression analysis reveals genetic associations masked in whole-tissue experiments},
    author = {Wills, Quin F and Livak, Kenneth J and Tipping, Alex J and Enver, Tariq and Goldson, Andrew J and Sexton, Darren W and Holmes, Chris C.},
    journal = {Nature biotechnology},
    volume = {31},
    number = {8},
    pages = {748--752},
    year = {2013},
    publisher = {Nature Publishing Group}
  }
  

• D. Mouradov , E. Domingo , P. Gibbs , R. N. Jorissen , S. Li , P. Y. Soo , L. Lipton , J. Desai , H. E. Danielsen , D. Oukrif , . others , Survival in stage II/III colorectal cancer is independently predicted by chromosomal and microsatellite instability, but not by specific driver mutations, The American journal of gastroenterology, vol. 108, no. 11, 1785–1793, 2013.
  

  @article{mouradov2013survival,
    title = {Survival in stage II/III colorectal cancer is independently predicted by chromosomal and microsatellite instability, but not by specific driver mutations},
    author = {Mouradov, Dmitri and Domingo, Enric and Gibbs, Peter and Jorissen, Robert N and Li, Shan and Soo, Pik Ying and Lipton, Lara and Desai, Jayesh and Danielsen, Havard E and Oukrif, Dahmane and others},
    journal = {The American journal of gastroenterology},
    volume = {108},
    number = {11},
    pages = {1785--1793},
    year = {2013},
    publisher = {Nature Publishing Group}
  }
  

• C. Yau , C. C. Holmes , A decision-theoretic approach for segmental classification, The Annals of Applied Statistics, vol. 7, no. 3, 1814–1835, 2013.
  

  @article{yau2013decision,
    title = {A decision-theoretic approach for segmental classification},
    author = {Yau, Christopher and Holmes, Chris C},
    journal = {The Annals of Applied Statistics},
    volume = {7},
    number = {3},
    pages = {1814--1835},
    year = {2013},
    publisher = {Institute of Mathematical Statistics}
  }
  

• A. Taylor , J. Flegg , G. Dorsey , P. Guerin , C. Holmes , Statistical estimation of malaria genotype frequencies: a Bayesian approach, Tropical Medicine & International Health, vol. 18, 67, 2013.
  

  @article{taylor2013statistical,
    title = {Statistical estimation of malaria genotype frequencies: a Bayesian approach},
    author = {Taylor, A and Flegg, J and Dorsey, G and Guerin, P and Holmes, C},
    journal = {Tropical Medicine \& International Health},
    volume = {18},
    pages = {67},
    year = {2013}
  }
  

• W. Wang , V. Baladandayuthapani , C. C. Holmes , K. Do , Integrative network-based Bayesian analysis of diverse genomics data, BMC bioinformatics, vol. 14, no. 13, S8, 2013.
  

  @article{wang2013integrative,
    title = {Integrative network-based Bayesian analysis of diverse genomics data},
    author = {Wang, Wenting and Baladandayuthapani, Veerabhadran and Holmes, Chris C and Do, Kim-Anh},
    journal = {BMC bioinformatics},
    volume = {14},
    number = {13},
    pages = {S8},
    year = {2013},
    publisher = {BioMed Central}
  }
  

• R. Grosse , C. J. Maddison , R. Salakhutdinov , Annealing Between Distributions by Averaging Moments, in Advances in Neural Information Processing Systems 26, 2013.
  
  Many powerful Monte Carlo techniques for estimating partition functions, such as annealed importance sampling (AIS), are based on sampling from a sequence of intermediate distributions which interpolate between a tractable initial distribution and an intractable target distribution. The near-universal practice is to use geometric averages of the initial and target distributions, but alternative paths can perform substantially better. We present a novel sequence of intermediate distributions for exponential families: averaging the moments of the initial and target distributions. We derive an asymptotically optimal piecewise linear schedule for the moments path and show that it performs at least as well as geometric averages with a linear schedule. Moment averaging performs well empirically at estimating partition functions of restricted Boltzmann machines (RBMs), which form the building blocks of many deep learning models, including Deep Belief Networks and Deep Boltzmann Machines.

  @inproceedings{grosse2013momentaverage,
    author = {Grosse, Roger and Maddison, Chris J. and Salakhutdinov, Ruslan},
    booktitle = {Advances in Neural Information Processing Systems 26},
    title = {{Annealing Between Distributions by Averaging Moments}},
    year = {2013},
    bdsk-url-1 = {http://www.stats.ox.ac.uk/\\~{}cmaddis/pubs/aais.pdf},
    bdsk-url-2 = {http://www.stats.ox.ac.uk/\\~{}cmaddis/pubs/aaissup.pdf}
  }
  

• L. Schumacher , G. K. Nicholls , A Cell-Level Mechanism of Contrast Gain Control, arXiv preprint arXiv:1310.5968, 2013.
  

  @article{schumacher2013celllevel,
    title = {A Cell-Level Mechanism of Contrast Gain Control},
    author = {Schumacher, L.J. and Nicholls, G. K.},
    year = {2013},
    journal = {arXiv preprint arXiv:1310.5968}
  }
  

• J. Arbel , G. Gayraud , J. Rousseau , Bayesian optimal adaptive estimation using a sieve prior, Scandinavian journal of statistics, vol. 40, no. 3, 549–570, 2013.
  

  @article{arbel2013bayesian,
    title = {Bayesian optimal adaptive estimation using a sieve prior},
    author = {Arbel, Julyan and Gayraud, Ghislaine and Rousseau, Judith},
    journal = {Scandinavian journal of statistics},
    volume = {40},
    number = {3},
    pages = {549--570},
    year = {2013},
    publisher = {Wiley Online Library}
  }
  

• R. McVinish , K. Mengersen , D. Nur , J. Rousseau , C. Guihenneuc-Jouyaux , Recentered importance sampling with applications to Bayesian model validation, Journal of Computational and Graphical Statistics, vol. 22, no. 1, 215–228, 2013.
  

  @article{mcvinish2013recentered,
    title = {Recentered importance sampling with applications to Bayesian model validation},
    author = {McVinish, Ross and Mengersen, Kerrie and Nur, Darfiana and Rousseau, Judith and Guihenneuc-Jouyaux, Chantal},
    journal = {Journal of Computational and Graphical Statistics},
    volume = {22},
    number = {1},
    pages = {215--228},
    year = {2013},
    publisher = {Taylor \& Francis}
  }
  

• W. Kruijer , J. Rousseau , . others , Bayesian semi-parametric estimation of the long-memory parameter under FEXP-priors, Electronic Journal of Statistics, vol. 7, 2947–2969, 2013.
  

  @article{kruijer2013bayesian,
    title = {Bayesian semi-parametric estimation of the long-memory parameter under FEXP-priors},
    author = {Kruijer, Willem and Rousseau, Judith and others},
    journal = {Electronic Journal of Statistics},
    volume = {7},
    pages = {2947--2969},
    year = {2013},
    publisher = {The Institute of Mathematical Statistics and the Bernoulli Society}
  }
  

• N. Chopin , J. Rousseau , B. Liseo , Computational aspects of Bayesian spectral density estimation, Journal of Computational and Graphical Statistics, vol. 22, no. 3, 533–557, 2013.
  

  @article{chopin2013computational,
    title = {Computational aspects of Bayesian spectral density estimation},
    author = {Chopin, Nicolas and Rousseau, Judith and Liseo, Brunero},
    journal = {Journal of Computational and Graphical Statistics},
    volume = {22},
    number = {3},
    pages = {533--557},
    year = {2013},
    publisher = {Taylor \& Francis}
  }
  

• A. Gandy , P. Rubin-Delanchy , An algorithm to compute the power of Monte Carlo tests with guaranteed precision, The Annals of Statistics, vol. 41, no. 1, 125–142, 2013.
  

  @article{gandy2013algorithm,
    title = {An algorithm to compute the power of {M}onte {C}arlo tests with guaranteed precision},
    author = {Gandy, A. and Rubin-Delanchy, P.},
    journal = {The Annals of Statistics},
    volume = {41},
    number = {1},
    pages = {125--142},
    year = {2013},
    publisher = {Institute of Mathematical Statistics}
  }
  

• T. Rukat , S. Walker-Samuel , S. A. Reinsberg , AIF Induced Limits of Parameter Uncertainty in Pharmakokinetic Models of Pre-Clinical DCE-MRI, in Proc. Intl. Soc. Mag. Reson. Med. 21 (2013) 2214, 2013, no. 2214.
  
  The reliability of small animal DCE-MRI data analysis with quantitative pharmakokinetic models depends strongly on the time course of contrast agent (CA) administration, known as arterial input function (AIF). In this study the dependence of the intrinsic limits of a parameter predictability on the width of the AIF-peak (i.e. the speed of CA administration) is quantified to provide investigators with a tool to determine reasonable limits for CA administration protocols.

  @inproceedings{Rukat2013,
    title = {{AIF} Induced Limits of Parameter Uncertainty in Pharmakokinetic Models of Pre-Clinical {DCE-MRI}},
    author = {Rukat, T. and Walker-Samuel, S. and Reinsberg, S. A.},
    booktitle = {Proc. Intl. Soc. Mag. Reson. Med. 21 (2013) 2214},
    year = {2013},
    number = {2214},
    file = {Rukat2013_AIF Induced Limist of Parameter Uncertainty in Pharmakokinetic Models of Pre-Clinical DCE-MRI.pdf:Rukat2013.pdf:PDF;Rukat2013_AIF Induced Limist of Parameter Uncertainty in Pharmakokinetic Models of Pre-Clinical DCE-MRI_Abstract.pdf:Rukat2013_AIF Induced Limist of Parameter Uncertainty in Pharmakokinetic Models of Pre-Clinical DCE-MRI_Abstract.pdf:PDF},
    journal = {Proc. Intl. Soc. Mag. Reson. Med. 21 (2013) 2214}
  }
  

• D. Sejdinovic , A. Gretton , W. Bergsma , A Kernel Test for Three-Variable Interactions, in Advances in Neural Information Processing Systems (NeurIPS), vol. 26, 2013, 1124–1132.
  
  We introduce kernel nonparametric tests for Lancaster three-variable interaction and for total independence, using embeddings of signed measures into a reproducing kernel Hilbert space. The resulting test statistics are straightforward to compute, and are used in powerful three-variable interaction tests, which are consistent against all alternatives for a large family of reproducing kernels. We show the Lancaster test to be sensitive to cases where two independent causes individually have weak influence on a third dependent variable, but their combined effect has a strong influence. This makes the Lancaster test especially suited to finding structure in directed graphical models, where it outperforms competing nonparametric tests in detecting such V-structures.

  @incollection{SejGreBer2013,
    author = {Sejdinovic, Dino and Gretton, Arthur and Bergsma, Wicher},
    booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
    pages = {1124--1132},
    title = {A Kernel Test for Three-Variable Interactions},
    volume = {26},
    year = {2013},
    bdsk-url-1 = {http://papers.nips.cc/paper/4893-a-kernel-test-for-three-variable-interactions.pdf},
    bdsk-url-2 = {http://papers.nips.cc/paper/4893-a-kernel-test-for-three-variable-interactions-supplemental.zip},
    bdsk-url-3 = {http://www.gatsby.ucl.ac.uk/%7Egretton/interact/threeWayInteract.htm},
    bdsk-url-4 = {http://research.microsoft.com/apps/video/default.aspx?id=206943}
  }
  

• S. Favaro , Y. W. Teh , MCMC for Normalized Random Measure Mixture Models, Statistical Science, vol. 28, no. 3, 335–359, 2013.
  
  This paper concerns the use of Markov chain Monte Carlo methods for posterior sampling in Bayesian nonparametric mixture models with normalized random measure priors. Making use of some recent posterior characterizations for the class of normalized random measures, we propose novel Markov chain Monte Carlo methods of both marginal type and conditional type. The proposed marginal samplers are generalizations of Neal’s well-regarded Algorithm 8 for Dirichlet process mixture models, whereas the conditional sampler is a variation of those recently introduced in the literature. For both the marginal and conditional methods, we consider as a running example a mixture model with an underlying normalized generalized Gamma process prior, and describe comparative simulation results demonstrating the efficacies of the proposed methods.

  @article{FavTeh2013a,
    author = {Favaro, S. and Teh, Y. W.},
    journal = {Statistical Science},
    number = {3},
    pages = {335-359},
    title = {{MCMC} for Normalized Random Measure Mixture Models},
    volume = {28},
    year = {2013},
    bdsk-url-1 = {https://projecteuclid.org/euclid.ss/1377696940},
    bdsk-url-2 = {https://projecteuclid.org/download/pdfview_1/euclid.ss/1377696940}
  }
  

• B. Lakshminarayanan , D. Roy , Y. W. Teh , Top-down Particle Filtering for Bayesian Decision Trees, in International Conference on Machine Learning (ICML), 2013.
  
  Decision tree learning is a popular approach for classification and regression in machine learning and statistics, and Bayesian formulations—which introduce a prior distribution over decision trees, and formulate learning as posterior inference given data—have been shown to produce competitive performance. Unlike classic decision tree learning algorithms like ID3, C4.5 and CART, which work in a top-down manner, existing Bayesian algorithms produce an approximation to the posterior distribution by evolving a complete tree (or collection thereof) iteratively via local Monte Carlo modifications to the structure of the tree, e.g., using Markov chain Monte Carlo (MCMC). We present a sequential Monte Carlo (SMC) algorithm that instead works in a top-down manner, mimicking the behavior and speed of classic algorithms. We demonstrate empirically that our approach delivers accuracy comparable to the most popular MCMC method, but operates more than an order of magnitude faster, and thus represents a better computation-accuracy tradeoff.

  @inproceedings{LakRoyTeh2013a,
    author = {Lakshminarayanan, B. and Roy, D. and Teh, Y. W.},
    booktitle = {International Conference on Machine Learning (ICML)},
    title = {Top-down Particle Filtering for {B}ayesian Decision Trees},
    year = {2013},
    bdsk-url-1 = {http://jmlr.csail.mit.edu/proceedings/papers/v28/lakshminarayanan13.pdf},
    bdsk-url-2 = {http://www.jmlr.org/proceedings/papers/v28/lakshminarayanan13-supp.pdf},
    bdsk-url-3 = {http://www.gatsby.ucl.ac.uk/\\~{}balaji/treesmc/}
  }
  

• C. Blundell , Y. W. Teh , Bayesian Hierarchical Community Discovery, in Advances in Neural Information Processing Systems (NeurIPS), 2013.
  
  We propose an efficient Bayesian nonparametric model for discovering hierarchical community structure in social networks. Our model is a tree-structured mixture of potentially exponentially many stochastic blockmodels. We describe a family of greedy agglomerative model selection algorithms whose worst case scales quadratically in the number of vertices of the network, but independent of the number of communities. Our algorithms are two orders of magnitude faster than the infinite relational model, achieving comparable or better accuracy.

  @inproceedings{BluTeh2013a,
    author = {Blundell, C. and Teh, Y. W.},
    booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
    title = {{B}ayesian Hierarchical Community Discovery},
    year = {2013},
    bdsk-url-1 = {https://papers.nips.cc/paper/5048-bayesian-hierarchical-community-discovery},
    bdsk-url-2 = {https://papers.nips.cc/paper/5048-bayesian-hierarchical-community-discovery.pdf}
  }
  

• V. Rao , Y. W. Teh , Fast MCMC sampling for Markov jump processes and extensions, Journal of Machine Learning Research (JMLR), vol. 14, 3295–3320, 2013.
  
  Markov jump processes (or continuous-time Markov chains) are a simple and important class of continuous-time dynamical systems. In this paper, we tackle the problem of simulating from the posterior distribution over paths in these models, given partial and noisy observations. Our approach is an auxiliary variable Gibbs sampler, and is based on the idea of uniformization. This sets up a Markov chain over paths by alternately sampling a finite set of virtual jump times given the current path, and then sampling a new path given the set of extant and virtual jump times. The first step involves simulating a piecewise-constant inhomogeneous Poisson process, while for the second, we use a standard hidden Markov model forward filtering-backward sampling algorithm. Our method is exact and does not involve approximations like time- discretization. We demonstrate how our sampler extends naturally to MJP-based models like Markov-modulated Poisson processes and continuous-time Bayesian networks, and show significant computational benefits over state-of-the-art MCMC samplers for these models.

  @article{RaoTeh2013a,
    author = {Rao, V. and Teh, Y. W.},
    journal = {Journal of Machine Learning Research (JMLR)},
    pages = {3295-3320},
    title = {Fast {MCMC} sampling for {M}arkov jump processes and extensions},
    volume = {14},
    year = {2013},
    bdsk-url-1 = {http://jmlr.org/papers/v14/rao13a.html},
    bdsk-url-2 = {http://jmlr.org/papers/volume14/rao13a/rao13a.pdf}
  }
  

• S. Patterson , Y. W. Teh , Stochastic Gradient Riemannian Langevin Dynamics on the Probability Simplex, in Advances in Neural Information Processing Systems (NeurIPS), 2013.
  
  In this paper we investigate the use of Langevin Monte Carlo methods on the probability simplex and propose a new method, Stochastic gradient Riemannian Langevin dynamics, which is simple to implement and can be applied online. We apply this method to latent Dirichlet allocation in an online setting, and demonstrate that it achieves substantial performance improvements to the state of the art online variational Bayesian methods.

  @inproceedings{PatTeh2013a,
    author = {Patterson, S. and Teh, Y. W.},
    booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
    title = {Stochastic Gradient {R}iemannian {L}angevin Dynamics on the Probability Simplex},
    year = {2013},
    bdsk-url-1 = {https://papers.nips.cc/paper/4883-stochastic-gradient-riemannian-langevin-dynamics-on-the-probability-simplex},
    bdsk-url-2 = {https://papers.nips.cc/paper/4883-stochastic-gradient-riemannian-langevin-dynamics-on-the-probability-simplex.pdf},
    bdsk-url-3 = {https://papers.nips.cc/paper/4883-stochastic-gradient-riemannian-langevin-dynamics-on-the-probability-simplex-supplemental.zip},
    bdsk-url-4 = {https://github.com/BigBayes/SGRLD}
  }
  

• X. Zhang , W. S. Lee , Y. W. Teh , Learning with Invariances via Linear Functionals on Reproducing Kernel Hilbert Space, in Advances in Neural Information Processing Systems, 2013.
  
  Incorporating invariance information is important for many learning problems. To exploit invariances, most existing methods resort to approximations that either lead to expensive optimization problems such as semi-definite programming, or rely on separation oracles to retain tractability. Some methods further limit the space of functions and settle for non-convex models. In this paper, we propose a framework for learning in reproducing kernel Hilbert spaces (RKHS) using local invariances that explicitly characterize the behavior of the target function around data instances. These invariances are \emphcompactly encoded as linear functionals whose value are penalized by some loss function. Based on a representer theorem that we establish, our formulation can be efficiently optimized via a convex program. For the representer theorem to hold, the linear functionals are required to be bounded in the RKHS, and we show that this is true for a variety of commonly used RKHS and invariances. Experiments on learning with unlabeled data and transform invariances show that the proposed method yields better or similar results compared with the state of the art.

  @inproceedings{ZhaLeeTeh2013a,
    author = {Zhang, X. and Lee, W. S. and Teh, Y. W.},
    booktitle = {Advances in Neural Information Processing Systems},
    title = {Learning with Invariances via Linear Functionals on Reproducing Kernel {H}ilbert Space},
    year = {2013},
    bdsk-url-1 = {http://papers.nips.cc/paper/4895-learning-with-invariance-via-linear-functionals-on-reproducing-kernel-hilbert-space},
    bdsk-url-2 = {http://papers.nips.cc/paper/4895-learning-with-invariance-via-linear-functionals-on-reproducing-kernel-hilbert-space.pdf},
    bdsk-url-3 = {http://papers.nips.cc/paper/4895-learning-with-invariance-via-linear-functionals-on-reproducing-kernel-hilbert-space-supplemental.zip}
  }
  

• C. Chen , V. A. Rao , W. Buntine , Y. W. Teh , Dependent Normalized Random Measures, in International Conference on Machine Learning (ICML), 2013.
  
  @inproceedings{CheRaoBun2013a,
    author = {Chen, C. and Rao, V. A. and Buntine, W. and Teh, Y. W.},
    booktitle = {International Conference on Machine Learning (ICML)},
    title = {Dependent Normalized Random Measures},
    year = {2013},
    bdsk-url-1 = {http://www.jmlr.org/proceedings/papers/v28/chen13i.html},
    bdsk-url-2 = {http://www.jmlr.org/proceedings/papers/v28/chen13i.pdf},
    bdsk-url-3 = {http://www.jmlr.org/proceedings/papers/v28/chen13i-supp.pdf}
  }
  

• B. Lakshminarayanan , Y. W. Teh , Inferring Ground Truth from Multi-annotator Ordinal Data: A Probabilistic Approach, 2013.
  
  A popular approach for large scale data annotation tasks is crowdsourcing, wherein each data point is labeled by multiple noisy annotators. We consider the problem of inferring ground truth from noisy ordinal labels obtained from multiple annotators of varying and unknown expertise levels. Annotation models for ordinal data have been proposed mostly as extensions of their binary/categorical counterparts and have received little attention in the crowdsourcing literature. We propose a new model for crowdsourced ordinal data that accounts for instance difficulty as well as annotator expertise, and derive a variational Bayesian inference algorithm for parameter estimation. We analyze the ordinal extensions of several state-of-the-art annotator models for binary/categorical labels and evaluate the performance of all the models on two real world datasets containing ordinal query-URL relevance scores, collected through Amazon’s Mechanical Turk. Our results indicate that the proposed model performs better or as well as existing state-of-the-art methods and is more resistant to ‘spammy’ annotators (i.e., annotators who assign labels randomly without actually looking at the instance) than popular baselines such as mean, median, and majority vote which do not account for annotator expertise.

  @unpublished{LakTeh2013a,
    author = {Lakshminarayanan, B. and Teh, Y. W.},
    note = {ArXiv e-prints: 1305.0015},
    title = {Inferring Ground Truth from Multi-annotator Ordinal Data: A Probabilistic Approach},
    year = {2013},
    bdsk-url-1 = {https://arxiv.org/pdf/1305.0015.pdf}
  }
  

2012

• G. Ha , A. Roth , D. Lai , A. Bashashati , J. Ding , R. Goya , R. Giuliany , J. Rosner , A. Oloumi , K. Shumansky , S. Chin , G. Turashvili , M. Hirst , C. Caldas , M. A. Marra , S. Aparicio , S. P. Shah , Integrative analysis of genome-wide loss of heterozygosity and monoallelic expression at nucleotide resolution reveals disrupted pathways in triple-negative breast cancer., Genome Res, vol. 22, no. 10, 1995–2007, Oct. 2012.
  

  @article{HaRothLaiEtAl2012,
    author = {Ha, Gavin and Roth, Andrew and Lai, Daniel and Bashashati, Ali and Ding, Jiarui and Goya, Rodrigo and Giuliany, Ryan and Rosner, Jamie and Oloumi, Arusha and Shumansky, Karey and Chin, Suet-Feung and Turashvili, Gulisa and Hirst, Martin and Caldas, Carlos and Marra, Marco A. and Aparicio, Samuel and Shah, Sohrab P.},
    doi = {10.1101/gr.137570.112},
    institution = {Department of Molecular Oncology, British Columbia Cancer Agency, Vancouver, British Columbia V5Z 1L3, Canada.},
    journal = {Genome Res},
    month = oct,
    number = {10},
    pages = {1995--2007},
    title = {{Integrative analysis of genome-wide loss of heterozygosity and monoallelic expression at nucleotide resolution reveals disrupted pathways in triple-negative breast cancer.}},
    volume = {22},
    year = {2012}
  }
  

• S. P. Shah , A. Roth , R. Goya , A. Oloumi , G. Ha , Y. Zhao , G. Turashvili , J. Ding , K. Tse , G. Haffari , A. Bashashati , L. M. Prentice , J. Khattra , A. Burleigh , D. Yap , V. Bernard , A. McPherson , K. Shumansky , A. Crisan , R. Giuliany , A. Heravi-Moussavi , J. Rosner , D. Lai , I. Birol , R. Varhol , A. Tam , N. Dhalla , T. Zeng , K. Ma , S. K. Chan , M. Griffith , A. Moradian , S. G. Cheng , G. B. Morin , P. Watson , K. Gelmon , S. Chia , S. Chin , C. Curtis , O. M. Rueda , P. D. Pharoah , S. Damaraju , J. Mackey , K. Hoon , T. Harkins , V. Tadigotla , M. Sigaroudinia , P. Gascard , T. Tlsty , J. F. Costello , I. M. Meyer , C. J. Eaves , W. W. Wasserman , S. Jones , D. Huntsman , M. Hirst , C. Caldas , M. A. Marra , S. Aparicio , The clonal and mutational evolution spectrum of primary triple-negative breast cancers., Nature, vol. 486, no. 7403, 395–399, Jun. 2012.
  

  @article{ShahRothGoyaEtAl2012,
    author = {Shah, Sohrab P. and Roth, Andrew and Goya, Rodrigo and Oloumi, Arusha and Ha, Gavin and Zhao, Yongjun and Turashvili, Gulisa and Ding, Jiarui and Tse, Kane and Haffari, Gholamreza and Bashashati, Ali and Prentice, Leah M. and Khattra, Jaswinder and Burleigh, Angela and Yap, Damian and Bernard, Virginie and McPherson, Andrew and Shumansky, Karey and Crisan, Anamaria and Giuliany, Ryan and Heravi-Moussavi, Alireza and Rosner, Jamie and Lai, Daniel and Birol, Inanc and Varhol, Richard and Tam, Angela and Dhalla, Noreen and Zeng, Thomas and Ma, Kevin and Chan, Simon K. and Griffith, Malachi and Moradian, Annie and Cheng, S-W Grace and Morin, Gregg B. and Watson, Peter and Gelmon, Karen and Chia, Stephen and Chin, Suet-Feung and Curtis, Christina and Rueda, Oscar M. and Pharoah, Paul D. and Damaraju, Sambasivarao and Mackey, John and Hoon, Kelly and Harkins, Timothy and Tadigotla, Vasisht and Sigaroudinia, Mahvash and Gascard, Philippe and Tlsty, Thea and Costello, Joseph F. and Meyer, Irmtraud M. and Eaves, Connie J. and Wasserman, Wyeth W. and Jones, Steven and Huntsman, David and Hirst, Martin and Caldas, Carlos and Marra, Marco A. and Aparicio, Samuel},
    doi = {10.1038/nature10933},
    institution = {Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, British Columbia V6T 2B5, Canada. sshah@bccrc.ca},
    journal = {Nature},
    month = jun,
    number = {7403},
    pages = {395--399},
    title = {{The clonal and mutational evolution spectrum of primary triple-negative breast cancers.}},
    volume = {486},
    year = {2012}
  }
  

• A. Roth , J. Ding , R. Morin , A. Crisan , G. Ha , R. Giuliany , A. Bashashati , M. Hirst , G. Turashvili , A. Oloumi , M. A. Marra , S. Aparicio , S. P. Shah , JointSNVMix: a probabilistic model for accurate detection of somatic mutations in normal/tumour paired next-generation sequencing data., Bioinformatics, vol. 28, no. 7, 907–913, Apr. 2012.
  

  @article{RothDingMorinEtAl2012,
    author = {Roth, Andrew and Ding, Jiarui and Morin, Ryan and Crisan, Anamaria and Ha, Gavin and Giuliany, Ryan and Bashashati, Ali and Hirst, Martin and Turashvili, Gulisa and Oloumi, Arusha and Marra, Marco A. and Aparicio, Samuel and Shah, Sohrab P.},
    doi = {10.1093/bioinformatics/bts053},
    institution = {Department of Molecular Oncology, BC Cancer Agency, BC, Canada.},
    journal = {Bioinformatics},
    month = apr,
    number = {7},
    pages = {907--913},
    title = {{JointSNVMix: a probabilistic model for accurate detection of somatic mutations in normal/tumour paired next-generation sequencing data.}},
    volume = {28},
    year = {2012}
  }
  

• J. Ding , A. Bashashati , A. Roth , A. Oloumi , K. Tse , T. Zeng , G. Haffari , M. Hirst , M. A. Marra , A. Condon , S. Aparicio , S. P. Shah , Feature-based classifiers for somatic mutation detection in tumour-normal paired sequencing data., Bioinformatics, vol. 28, no. 2, 167–175, Jan. 2012.
  

  @article{DingBashashatiRothEtAl2012,
    author = {Ding, Jiarui and Bashashati, Ali and Roth, Andrew and Oloumi, Arusha and Tse, Kane and Zeng, Thomas and Haffari, Gholamreza and Hirst, Martin and Marra, Marco A. and Condon, Anne and Aparicio, Samuel and Shah, Sohrab P.},
    doi = {10.1093/bioinformatics/btr629},
    institution = {Department of Molecular Oncology, BC Cancer Agency, Vancouver, BC, Canada.},
    journal = {Bioinformatics},
    month = jan,
    number = {2},
    pages = {167--175},
    title = {{Feature-based classifiers for somatic mutation detection in tumour-normal paired sequencing data.}},
    volume = {28},
    year = {2012}
  }
  

• A. Heravi-Moussavi , M. S. Anglesio , S. G. Cheng , J. Senz , W. Yang , L. Prentice , A. P. Fejes , C. Chow , A. Tone , S. E. Kalloger , N. Hamel , A. Roth , G. Ha , A. N. C. Wan , S. Maines-Bandiera , C. Salamanca , B. Pasini , B. A. Clarke , A. F. Lee , C. Lee , C. Zhao , R. H. Young , S. A. Aparicio , P. H. B. Sorensen , M. M. M. Woo , N. Boyd , S. J. M. Jones , M. Hirst , M. A. Marra , B. Gilks , S. P. Shah , W. D. Foulkes , G. B. Morin , D. G. Huntsman , Recurrent somatic DICER1 mutations in nonepithelial ovarian cancers., N Engl J Med, vol. 366, no. 3, 234–242, Jan. 2012.
  

  @article{Heravi-MoussaviAnglesioChengEtAl2012,
    author = {Heravi-Moussavi, Alireza and Anglesio, Michael S. and Cheng, S-W Grace and Senz, Janine and Yang, Winnie and Prentice, Leah and Fejes, Anthony P. and Chow, Christine and Tone, Alicia and Kalloger, Steve E. and Hamel, Nancy and Roth, Andrew and Ha, Gavin and Wan, Adrian N C. and Maines-Bandiera, Sarah and Salamanca, Clara and Pasini, Barbara and Clarke, Blaise A. and Lee, Anna F. and Lee, Cheng-Han and Zhao, Chengquan and Young, Robert H. and Aparicio, Samuel A. and Sorensen, Poul H B. and Woo, Michelle M M. and Boyd, Niki and Jones, Steven J M. and Hirst, Martin and Marra, Marco A. and Gilks, Blake and Shah, Sohrab P. and Foulkes, William D. and Morin, Gregg B. and Huntsman, David G.},
    doi = {10.1056/NEJMoa1102903},
    institution = {British Columbia Cancer Research Centre, Vancouver, BC, Canada.},
    journal = {N Engl J Med},
    month = jan,
    number = {3},
    pages = {234--242},
    title = {{Recurrent somatic DICER1 mutations in nonepithelial ovarian cancers.}},
    volume = {366},
    year = {2012}
  }
  

• C. Archambeau , F. Caron , Plackett-Luce regression: A new Bayesian model for polychotomous data, in Uncertainty in Artificial Intelligence (UAI), 2012.
  

  @inproceedings{Archambeau2012,
    title = {{Plackett-Luce} regression: A new {B}ayesian model for polychotomous data},
    author = {Archambeau, C{\'e}dric and Caron, Francois},
    booktitle = {Uncertainty in Artificial Intelligence (UAI)},
    year = {2012},
    file = {Archambeau_Caron_UAI_2012.pdf:http\://www.stats.ox.ac.uk/\\~{}caron/Publications/Archambeau_Caron_UAI_2012.pdf:PDF}
  }
  

• F. Caron , Bayesian nonparametric models for bipartite graphs, in Advances in Neural Information Processing Systems (NeurIPS), 2012.
  
  @inproceedings{Caron2012,
    title = {Bayesian nonparametric models for bipartite graphs},
    author = {Caron, F.},
    booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
    year = {2012}
  }
  

• R. J. Evans , Graphical methods for inequality constraints in marginalized DAGs, in Machine Learning for Signal Processing, 2012.
  

  @inproceedings{evans:12,
    author = {Evans, R. J.},
    booktitle = {Machine Learning for Signal Processing},
    organization = {IEEE},
    title = {Graphical methods for inequality constraints in marginalized {DAGs}},
    year = {2012}
  }
  

• I. Shpitser , T. Richardson , J. M. Robins , R. J. Evans , Parameter and Structure Learning in Nested Markov Models, in UAI Workshop on Structure Learning, 2012.
  

  @inproceedings{shpitser:12,
    author = {Shpitser, I. and Richardson, T.S. and Robins, J. M. and Evans, R. J.},
    booktitle = {UAI Workshop on Structure Learning},
    title = {Parameter and Structure Learning in Nested {M}arkov Models},
    year = {2012}
  }
  

• A. Roy , G. Cowan , A. J. Mead , S. Filippi , G. Bohn , A. Chaidos , O. Tunstall , J. K. Y. Chan , M. Choolani , P. Bennett , S. Kumar , D. Atkinson , J. Wyatt-Ashmead , M. Hu , M. P. H. Stumpf , K. Goudevenou , D. O’Connor , S. T. Chou , M. J. Weiss , A. Karadimitris , S. E. Jacobsen , P. Vyas , I. Roberts , Perturbation of fetal liver hematopoietic stem and progenitor cell development by trisomy 21., Proceedings of the National Academy of Sciences, 2012.
  

  @article{Roy:2012fs,
    author = {Roy, Anindita and Cowan, Gillian and Mead, Adam J and Filippi, Sarah and Bohn, Georg and Chaidos, Aristeidis and Tunstall, Oliver and Chan, Jerry K Y and Choolani, Mahesh and Bennett, Phillip and Kumar, Sailesh and Atkinson, Deborah and Wyatt-Ashmead, Josephine and Hu, Ming and Stumpf, Michael P H and Goudevenou, Katerina and O'Connor, David and Chou, Stella T and Weiss, Mitchell J and Karadimitris, Anastasios and Jacobsen, Sten Eirik and Vyas, Paresh and Roberts, Irene},
    title = {{Perturbation of fetal liver hematopoietic stem and progenitor cell development by trisomy 21.}},
    journal = {Proceedings of the National Academy of Sciences},
    year = {2012}
  }
  

• C. P. Barnes , S. Filippi , M. P. H. Stumpf , T. Thorne , Considerate approaches to constructing summary statistics for ABC model selection, Statistics and Computing, 2012.
  

  @article{Barnes:2012hf,
    author = {Barnes, Chris P and Filippi, Sarah and Stumpf, Michael P H and Thorne, Thomas},
    title = {{Considerate approaches to constructing summary statistics for ABC model selection}},
    journal = {Statistics and Computing},
    year = {2012}
  }
  

• A. Lee , F. Caron , A. Doucet , C. C. Holmes , . others , Bayesian sparsity-path-analysis of genetic association signal using generalized t priors, Statistical applications in genetics and molecular biology, vol. 11, no. 2, 1–29, 2012.
  

  @article{lee2012bayesian,
    title = {Bayesian sparsity-path-analysis of genetic association signal using generalized t priors},
    author = {Lee, Anthony and Caron, Francois and Doucet, Arnaud and Holmes, Chris C. and others},
    journal = {Statistical applications in genetics and molecular biology},
    volume = {11},
    number = {2},
    pages = {1--29},
    year = {2012},
    publisher = {Walter de Gruyter GmbH \& Co. KG}
  }
  

• T. W. Chittenden , E. A. Howe , J. M. Taylor , J. C. Mar , M. J. Aryee , H. Gómez , R. Sultana , J. Braisted , S. J. Nair , J. Quackenbush , . others , nEASE: a method for gene ontology subclassification of high-throughput gene expression data, Bioinformatics, vol. 28, no. 5, 726–728, 2012.
  

  @article{chittenden2012nease,
    title = {nEASE: a method for gene ontology subclassification of high-throughput gene expression data},
    author = {Chittenden, Thomas W and Howe, Eleanor A and Taylor, Jennifer M and Mar, Jessica C and Aryee, Martin J and G{\'o}mez, Harold and Sultana, Razvan and Braisted, John and Nair, Sarita J and Quackenbush, John and others},
    journal = {Bioinformatics},
    volume = {28},
    number = {5},
    pages = {726--728},
    year = {2012},
    publisher = {Oxford Univ Press}
  }
  

• J. L. Min , G. Nicholson , I. Halgrimsdottir , K. Almstrup , A. Petri , A. Barrett , M. Travers , N. W. Rayner , R. Mägi , F. H. Pettersson , . others , Coexpression network analysis in abdominal and gluteal adipose tissue reveals regulatory genetic loci for metabolic syndrome and related phenotypes, PLoS Genet, vol. 8, no. 2, e1002505, 2012.
  

  @article{min2012coexpression,
    title = {Coexpression network analysis in abdominal and gluteal adipose tissue reveals regulatory genetic loci for metabolic syndrome and related phenotypes},
    author = {Min, Josine L and Nicholson, George and Halgrimsdottir, Ingileif and Almstrup, Kristian and Petri, Andreas and Barrett, Amy and Travers, Mary and Rayner, Nigel W and M{\"a}gi, Reedik and Pettersson, Fredrik H and others},
    journal = {PLoS Genet},
    volume = {8},
    number = {2},
    pages = {e1002505},
    year = {2012},
    publisher = {Public Library of Science}
  }
  

• S. Knight , C. Yau , R. Clifford , A. Timbs , E. S. Akha , H. Dreau , A. Burns , C. Ciria , D. Oscier , A. Pettitt , . others , Quantification of subclonal distributions of recurrent genomic aberrations in paired pre-treatment and relapse samples from patients with B-cell chronic lymphocytic leukemia, Leukemia, vol. 26, no. 7, 2012.
  

  @article{knight2012quantification,
    title = {Quantification of subclonal distributions of recurrent genomic aberrations in paired pre-treatment and relapse samples from patients with B-cell chronic lymphocytic leukemia},
    author = {Knight, SJL and Yau, C and Clifford, R and Timbs, AT and Akha, E Sadighi and Dreau, HM and Burns, A and Ciria, C and Oscier, DG and Pettitt, AR and others},
    journal = {Leukemia},
    volume = {26},
    number = {7},
    year = {2012},
    publisher = {Nature Publishing Group}
  }
  

• F. Caron , C. C. Holmes , E. Rio , On the sampling distribution of an $\backslash ell\^ 2$ distance between Empirical Distribution Functions with applications to nonparametric testing, PhD thesis, INRIA, 2012.
  

  @phdthesis{caron2012sampling,
    title = {On the sampling distribution of an $$\backslash$ ell\^{} 2$ distance between Empirical Distribution Functions with applications to nonparametric testing},
    author = {Caron, Francois and Holmes, Chris C. and Rio, Emmanuel},
    year = {2012},
    school = {INRIA}
  }
  

• W. Valdar , J. Sabourin , A. Nobel , C. C. Holmes , Reprioritizing Genetic Associations in Hit Regions Using LASSO-Based Resample Model Averaging, Genetic epidemiology, vol. 36, no. 5, 451–462, 2012.
  

  @article{valdar2012reprioritizing,
    title = {Reprioritizing Genetic Associations in Hit Regions Using LASSO-Based Resample Model Averaging},
    author = {Valdar, William and Sabourin, Jeremy and Nobel, Andrew and Holmes, Chris C},
    journal = {Genetic epidemiology},
    volume = {36},
    number = {5},
    pages = {451--462},
    year = {2012}
  }
  

• J. Cazier , C. C. Holmes , J. Broxholme , GREVE: Genomic Recurrent Event ViEwer to assist the identification of patterns across individual cancer samples, Bioinformatics, vol. 28, no. 22, 2981–2982, 2012.
  

  @article{cazier2012greve,
    title = {GREVE: Genomic Recurrent Event ViEwer to assist the identification of patterns across individual cancer samples},
    author = {Cazier, Jean-Baptiste and Holmes, Chris C and Broxholme, John},
    journal = {Bioinformatics},
    volume = {28},
    number = {22},
    pages = {2981--2982},
    year = {2012},
    publisher = {Oxford University Press}
  }
  

• A. R. Taylor , J. A. Flegg , P. J. Guerin , C. Roper , C. C. Holmes , A Bayesian model for estimating with-in host P. falciparum haplotype frequencies, Malaria Journal, vol. 11, no. S1, P36, 2012.
  

  @article{taylor2012bayesian,
    title = {A Bayesian model for estimating with-in host P. falciparum haplotype frequencies},
    author = {Taylor, Aimee R and Flegg, Jennifer A and Guerin, Philippe J and Roper, Cally and Holmes, Chris C.},
    journal = {Malaria Journal},
    volume = {11},
    number = {S1},
    pages = {P36},
    year = {2012},
    publisher = {BioMed Central}
  }
  

• J. L. Davies , J. Cazier , M. G. Dunlop , R. S. Houlston , I. P. Tomlinson , C. C. Holmes , A Novel Test for Gene-Ancestry Interactions in Genome-Wide Association Data, PloS one, vol. 7, no. 12, e48687, 2012.
  

  @article{davies2012novel,
    title = {A Novel Test for Gene-Ancestry Interactions in Genome-Wide Association Data},
    author = {Davies, Joanna L and Cazier, Jean-Baptiste and Dunlop, Malcolm G and Houlston, Richard S and Tomlinson, Ian P and Holmes, Chris C},
    journal = {PloS one},
    volume = {7},
    number = {12},
    pages = {e48687},
    year = {2012},
    publisher = {Public Library of Science}
  }
  

• M. Rantalainen , C. C. Holmes , Robust Statistical Methods For Genome-Wide Eqtl Analysis, 2012.
  

  @inproceedings{rantalainen2012robust,
    title = {Robust Statistical Methods For Genome-Wide Eqtl Analysis},
    author = {Rantalainen, Mattias and Holmes, Chris C.},
    year = {2012}
  }
  

• F. Caron , C. C. Holmes , E. Rio , On the sampling distribution of an l2 norm of the Empirical Distribution Function, with applications to two-sample nonparametric testing, 2012.
  

  @article{caron2012samplinh,
    title = {On the sampling distribution of an l2 norm of the Empirical Distribution Function, with applications to two-sample nonparametric testing},
    author = {Caron, Francois and Holmes, Chris C. and Rio, Emmanuel},
    year = {2012}
  }
  

• C. C. Holmes , Auxiliary Counting Variables for Posterior Decoding of Hidden Markov Models with Applications to Cancer Genomics, in ISBA Regional Meeting and International Workshop/Conference on Bayesian Theory and Applications (IWCBTA), 2012.
  

  @inproceedings{holmes2012auxiliary,
    title = {Auxiliary Counting Variables for Posterior Decoding of Hidden Markov Models with Applications to Cancer Genomics},
    author = {Holmes, Chris C.},
    booktitle = {ISBA Regional Meeting and International Workshop/Conference on Bayesian Theory and Applications (IWCBTA)},
    year = {2012}
  }
  

• C. C. Holmes , Large-scale genetic analysis of quantitative traits, PhD thesis, University of Oxford, 2012.
  

  @phdthesis{holmes2012large,
    title = {Large-scale genetic analysis of quantitative traits},
    author = {Holmes, Chris C.},
    year = {2012},
    school = {University of Oxford}
  }
  

• C. C. Holmes , Extensions of the case-control design in genome-wide association studies, PhD thesis, University of Oxford, 2012.
  

  @phdthesis{holmes2012extensions,
    title = {Extensions of the case-control design in genome-wide association studies},
    author = {Holmes, Chris C.},
    year = {2012},
    school = {University of Oxford}
  }
  

• B. Smolyak , M. Zakharov , Slowing-down of the vortex motion in the flux creep process by counter forces exerted on the vortex ends, Superconductor Science and Technology, vol. 25, no. 12, 125019, 2012.
  

  @article{smolyak2012slowing,
    title = {Slowing-down of the vortex motion in the flux creep process by counter forces exerted on the vortex ends},
    author = {Smolyak, BM and Zakharov, MS},
    journal = {Superconductor Science and Technology},
    volume = {25},
    number = {12},
    pages = {125019},
    year = {2012},
    publisher = {IOP Publishing}
  }
  

• S. A. Heimovics , N. H. Prior , C. J. Maddison , K. K. Soma , Rapid and Widespread Effects of 17-beta-estradiol on Intracellular Signaling in the Male Songbird Brain: A Seasonal Comparison, Endocrinology, vol. 153, no. 3, 1364–1376, 2012.
  
  @article{Heimovics01032012,
    author = {Heimovics, Sarah A. and Prior, Nora H. and Maddison, Chris J. and Soma, Kiran K.},
    doi = {10.1210/en.2011-1525},
    journal = {Endocrinology},
    number = {3},
    pages = {1364-1376},
    title = {{Rapid and Widespread Effects of 17-beta-estradiol on Intracellular Signaling in the Male Songbird Brain: A Seasonal Comparison}},
    volume = {153},
    year = {2012},
    bdsk-url-1 = {http://endo.endojournals.org/content/153/3/1364.abstract},
    bdsk-url-2 = {http://dx.doi.org/10.1210/en.2011-1525}
  }
  

• C. J. Maddison , R. C. Anderson , N. H. Prior , M. D. Taves , K. K. Soma , Soft song during aggressive interactions: Seasonal changes and endocrine correlates in song sparrows, Hormones and Behavior, vol. 62, no. 4, 455–463, 2012.
  
  @article{maddison2012softsong,
    author = {Maddison, Chris J. and Anderson, Rindy C. and Prior, Nora H. and Taves, Matthew D. and Soma, Kiran K.},
    journal = {Hormones and Behavior},
    number = {4},
    pages = {455 - 463},
    title = {{Soft song during aggressive interactions: Seasonal changes and endocrine correlates in song sparrows}},
    volume = {62},
    year = {2012},
    bdsk-url-1 = {https://www.ncbi.nlm.nih.gov/pubmed/22902893}
  }
  

• G. K. Nicholls , C. Fox , A. Watt , Coupled MCMC with a randomized acceptance probability, arXiv preprint arXiv:1205.6857, 2012.
  

  @article{nicholls2012coupled,
    title = {Coupled MCMC with a randomized acceptance probability},
    author = {Nicholls, G. K. and Fox, C. and Watt, A.M.},
    journal = {arXiv preprint arXiv:1205.6857},
    year = {2012}
  }
  

• V. Rivoirard , J. Rousseau , . others , Bernstein–von Mises theorem for linear functionals of the density, The Annals of Statistics, vol. 40, no. 3, 1489–1523, 2012.
  

  @article{rivoirard2012bernstein,
    title = {Bernstein--von Mises theorem for linear functionals of the density},
    author = {Rivoirard, Vincent and Rousseau, Judith and others},
    journal = {The Annals of Statistics},
    volume = {40},
    number = {3},
    pages = {1489--1523},
    year = {2012},
    publisher = {Institute of Mathematical Statistics}
  }
  

• V. Rivoirard , J. Rousseau , . others , Posterior concentration rates for infinite dimensional exponential families, Bayesian Analysis, vol. 7, no. 2, 311–334, 2012.
  

  @article{rivoirard2012posterior,
    title = {Posterior concentration rates for infinite dimensional exponential families},
    author = {Rivoirard, Vincent and Rousseau, Judith and others},
    journal = {Bayesian Analysis},
    volume = {7},
    number = {2},
    pages = {311--334},
    year = {2012},
    publisher = {International Society for Bayesian Analysis}
  }
  

• I. Albert , S. Donnet , C. Guihenneuc-Jouyaux , S. Low-Choy , K. Mengersen , J. Rousseau , . others , Combining expert opinions in prior elicitation, Bayesian Analysis, vol. 7, no. 3, 503–532, 2012.
  

  @article{albert2012combining,
    title = {Combining expert opinions in prior elicitation},
    author = {Albert, Isabelle and Donnet, Sophie and Guihenneuc-Jouyaux, Chantal and Low-Choy, Samantha and Mengersen, Kerrie and Rousseau, Judith and others},
    journal = {Bayesian Analysis},
    volume = {7},
    number = {3},
    pages = {503--532},
    year = {2012},
    publisher = {International Society for Bayesian Analysis}
  }
  

• J. Rousseau , N. Chopin , B. Liseo , . others , Bayesian nonparametric estimation of the spectral density of a long or intermediate memory Gaussian process, The Annals of Statistics, vol. 40, no. 2, 964–995, 2012.
  

  @article{rousseau2012bayesian,
    title = {Bayesian nonparametric estimation of the spectral density of a long or intermediate memory Gaussian process},
    author = {Rousseau, Judith and Chopin, Nicolas and Liseo, Brunero and others},
    journal = {The Annals of Statistics},
    volume = {40},
    number = {2},
    pages = {964--995},
    year = {2012},
    publisher = {Institute of Mathematical Statistics}
  }
  

• O. Lieberman , R. Rosemarin , J. Rousseau , Asymptotic theory for maximum likelihood estimation of the memory parameter in stationary Gaussian processes, Econometric Theory, vol. 28, no. 2, 457–470, 2012.
  

  @article{lieberman2012asymptotic,
    title = {Asymptotic theory for maximum likelihood estimation of the memory parameter in stationary Gaussian processes},
    author = {Lieberman, Offer and Rosemarin, Roy and Rousseau, Judith},
    journal = {Econometric Theory},
    volume = {28},
    number = {2},
    pages = {457--470},
    year = {2012},
    publisher = {Cambridge University Press}
  }
  

• A. Gretton , B. K. Sriperumbudur , D. Sejdinovic , H. Strathmann , S. Balakrishnan , M. Pontil , K. Fukumizu , Optimal Kernel Choice for Large-Scale Two-Sample Tests, in Advances in Neural Information Processing Systems (NeurIPS), vol. 25, 2012, 1205–1213.
  
  Given samples from distributions p and q, a two-sample test determines whether to reject the null hypothesis that p=q, based on the value of a test statistic measuring the distance between the samples. One choice of test statistic is the maximum mean discrepancy (MMD), which is a distance between embeddings of the probability distributions in a reproducing kernel Hilbert space. The kernel used in obtaining these embeddings is thus critical in ensuring the test has high power, and correctly distinguishes unlike distributions with high probability. A means of parameter selection for the two-sample test based on the MMD is proposed. For a given test level (an upper bound on the probability of making a Type I error), the kernel is chosen so as to maximize the test power, and minimize the probability of making a Type II error. The test statistic, test threshold, and optimization over the kernel parameters are obtained with cost linear in the sample size. These properties make the kernel selection and test procedures suited to data streams, where the observations cannot all be stored in memory. In experiments, the new kernel selection approach yields a more powerful test than earlier kernel selection heuristics.

  @incollection{GreSriSejStrBalPonFuk2012,
    author = {Gretton, Arthur and Sriperumbudur, Bharath K. and Sejdinovic, Dino and Strathmann, Heiko and Balakrishnan, Sivaraman and Pontil, Massimiliano and Fukumizu, Kenji},
    booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
    code = {http://www.gatsby.ucl.ac.uk/%7Egretton/adaptMMD/adaptMMD.htm},
    pages = {1205--1213},
    title = {Optimal Kernel Choice for Large-Scale Two-Sample Tests},
    volume = {25},
    year = {2012},
    bdsk-url-1 = {http://papers.nips.cc/paper/4727-optimal-kernel-choice-for-large-scale-two-sample-tests.pdf}
  }
  

• D. Sejdinovic , A. Gretton , B. K. Sriperumbudur , K. Fukumizu , Hypothesis testing using pairwise distances and associated kernels, in International Conference on Machine Learning (ICML), 2012, 1111–1118.
  
  We provide a unifying framework linking two classes of statistics used in two-sample and independence testing: on the one hand, the energy distances and distance covariances from the statistics literature; on the other, distances between embeddings of distributions to reproducing kernel Hilbert spaces (RKHS), as established in machine learning. The equivalence holds when energy distances are computed with semimetrics of negative type, in which case a kernel may be defined such that the RKHS distance between distributions corresponds exactly to the energy distance. We determine the class of probability distributions for which kernels induced by semimetrics are characteristic (that is, for which embeddings of the distributions to an RKHS are injective). Finally, we investigate the performance of this family of kernels in two-sample and independence tests: we show in particular that the energy distance most commonly employed in statistics is just one member of a parametric family of kernels, and that other choices from this family can yield more powerful tests.

  @inproceedings{SejGreSriFuk12,
    author = {Sejdinovic, D. and Gretton, Arthur and Sriperumbudur, Bharath K. and Fukumizu, Kenji},
    booktitle = {International Conference on Machine Learning (ICML)},
    pages = {1111--1118},
    title = {{Hypothesis testing using pairwise distances and associated kernels}},
    year = {2012},
    bdsk-url-1 = {http://machinelearning.wustl.edu/mlpapers/paper_files/ICML2012Sejdinovic_575.pdf},
    bdsk-url-2 = {http://techtalks.tv/talks/57320/}
  }
  

• R. Piechocki , D. Sejdinovic , Combinatorial Channel Signature Modulation for Wireless ad-hoc Networks, in IEEE International Conference on Communications (ICC), 2012.
  
  In this paper we introduce a novel modulation and multiplexing method which facilitates highly efficient and simultaneous communication between multiple terminals in wireless ad-hoc networks. We term this method Combinatorial Channel Signature Modulation (CCSM). The CCSM method is particularly efficient in situations where communicating nodes operate in highly time dispersive environments. This is all achieved with a minimal MAC layer overhead, since all users are allowed to transmit and receive at the same time/frequency (full simultaneous duplex). The CCSM method has its roots in sparse modelling and the receiver is based on compressive sampling techniques. Towards this end, we develop a new low complexity algorithm termed Group Subspace Pursuit. Our analysis suggests that CCSM at least doubles the throughput when compared to the state-of-the art.

  @inproceedings{PieSej2012,
    author = {Piechocki, R. and Sejdinovic, D.},
    booktitle = {IEEE International Conference on Communications (ICC)},
    doi = {10.1109/ICC.2012.6363956},
    file = {pdf/2012ICC.pdf},
    title = {{Combinatorial Channel Signature Modulation for Wireless ad-hoc Networks}},
    year = {2012},
    bdsk-url-1 = {http://dx.doi.org/10.1109/ICC.2012.6363956}
  }
  

• A. Muller , D. Sejdinovic , R. Piechocki , Approximate Message Passing under Finite Alphabet Constraints, in IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2012.
  
  In this paper we consider Basis Pursuit De-Noising (BPDN) problems in which the sparse original signal is drawn from a finite alphabet. To solve this problem we propose an iterative message passing algorithm, which capitalises not only on the sparsity but by means of a prior distribution also on the discrete nature of the original signal. In our numerical experiments we test this algorithm in combination with a Rademacher measurement matrix and a measurement matrix derived from the random demodulator, which enables compressive sampling of analogue signals. Our results show in both cases significant performance gains over a linear programming based approach to the considered BPDN problem. We also compare the proposed algorithm to a similar message passing based algorithm without prior knowledge and observe an even larger performance improvement.

  @inproceedings{MulSejPie2012,
    author = {Muller, A. and Sejdinovic, D. and Piechocki, R.},
    booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)},
    doi = {10.1109/ICASSP.2012.6288590},
    file = {pdf/2012ICASSP.pdf},
    title = {{Approximate Message Passing under Finite Alphabet Constraints}},
    year = {2012},
    bdsk-url-1 = {http://dx.doi.org/10.1109/ICASSP.2012.6288590}
  }
  

• F. Caron , Y. W. Teh , Bayesian Nonparametric Models for Ranked Data, in Advances in Neural Information Processing Systems (NeurIPS), 2012.
  
  We develop a Bayesian nonparametric extension of the popular Plackett-Luce choice model that can handle an infinite number of choice items. Our framework is based on the theory of random atomic measures, with the prior specified by a gamma process. We derive a posterior characterization and a simple and effective Gibbs sampler for posterior simulation. We then develop a time-varying extension of our model, and apply our model to the New York Times lists of weekly bestselling books.

  @inproceedings{CarTeh2012a,
    author = {Caron, F. and Teh, Y. W.},
    booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
    title = {Bayesian Nonparametric Models for Ranked Data},
    year = {2012},
    bdsk-url-1 = {http://papers.nips.cc/paper/4624-bayesian-nonparametric-models-for-ranked-data},
    bdsk-url-2 = {http://papers.nips.cc/paper/4624-bayesian-nonparametric-models-for-ranked-data.pdf},
    bdsk-url-3 = {http://papers.nips.cc/paper/4624-bayesian-nonparametric-models-for-ranked-data-supplemental.zip}
  }
  

• B. Alexe , N. Heess , Y. W. Teh , V. Ferrari , Searching for Objects Driven by Context, in Advances in Neural Information Processing Systems (NeurIPS), 2012.
  
  The dominant visual search paradigm for object class detection is sliding windows. Although simple and effective, it is also wasteful, unnatural and rigidly hardwired. We propose strategies to search for objects which intelligently explore the space of windows by making sequential observations at locations decided based on previous observations. Our strategies adapt to the class being searched and to the content of a particular test image. Their driving force is exploiting context as the statistical relation between the appearance of a window and its location relative to the object, as observed in the training set. In addition to being more elegant than sliding windows, we demonstrate experimentally on the PASCAL VOC 2010 dataset that our strategies evaluate two orders of magnitude fewer windows while at the same time achieving higher detection accuracy.

  @inproceedings{AleHeeTeh2012a,
    author = {Alexe, Bogdan and Heess, Nicolas and Teh, Yee Whye and Ferrari, Vittorio},
    booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
    title = {Searching for Objects Driven by Context},
    year = {2012},
    bdsk-url-1 = {https://papers.nips.cc/paper/4717-searching-for-objects-driven-by-context},
    bdsk-url-2 = {https://papers.nips.cc/paper/4717-searching-for-objects-driven-by-context.pdf}
  }
  

• V. Rao , Y. W. Teh , MCMC for Continuous-Time Discrete-State Systems, in Advances in Neural Information Processing Systems (NeurIPS), 2012.
  
  We propose a simple and novel framework for MCMC inference in continuous-time discrete-state systems with pure jump trajectories. We construct an exact MCMC sampler for such systems by alternately sampling a random discretization of time given a trajectory of the system, and then a new trajectory given the discretization. The first step can be performed efficiently using properties of the Poisson process, while the second step can avail of discrete-time MCMC techniques based on the forward-backward algorithm. We compare our approach to particle MCMC and a uniformization-based sampler, and show its advantages.

  @inproceedings{RaoTeh2012a,
    author = {Rao, V. and Teh, Y. W.},
    booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
    title = {{MCMC} for Continuous-Time Discrete-State Systems},
    year = {2012},
    bdsk-url-1 = {https://papers.nips.cc/paper/4746-mcmc-for-continuous-time-discrete-state-systems},
    bdsk-url-2 = {https://papers.nips.cc/paper/4746-mcmc-for-continuous-time-discrete-state-systems.pdf},
    bdsk-url-3 = {https://papers.nips.cc/paper/4746-mcmc-for-continuous-time-discrete-state-systems-supplemental.zip}
  }
  

• A. Mnih , Y. W. Teh , A Fast and Simple Algorithm for Training Neural Probabilistic Language Models, in International Conference on Machine Learning (ICML), 2012.
  
  Neural probabilistic language models (NPLMs) have recently superseded smoothed n-gram models as the best-performing model class for language modelling. Unfortunately, the adoption of NPLMs is held back by their notoriously long training times, which can be measured in weeks even for moderately-sized datasets. These are a consequence of the models being explicitly normalized, which leads to having to consider all words in the vocabulary when computing the log-likelihood gradients. We propose a fast and simple algorithm for training NPLMs based on noise-contrastive estimation, a newly introduced procedure for estimating unnormalized continuous distributions. We investigate the behaviour of the algorithm on the Penn Treebank corpus and show that it reduces the training times by more than an order of magnitude without affecting the quality of the resulting models. The algorithm is also more efficient and much more stable than importance sampling because it requires far fewer noise samples to perform well. We demonstrate the scalability of the proposed approach by training several neural language models on a 47M-word corpus with a 80K-word vocabulary, obtaining state-of-the-art results in the Microsoft Research Sentence Completion Challenge.

  @inproceedings{MniTeh2012a,
    author = {Mnih, A. and Teh, Y. W.},
    booktitle = {International Conference on Machine Learning (ICML)},
    title = {A Fast and Simple Algorithm for Training Neural Probabilistic Language Models},
    year = {2012},
    bdsk-url-1 = {http://icml.cc/2012/papers/855.pdf}
  }
  

• N. Heess , D. Silver , Y. W. Teh , Actor-Critic Reinforcement Learning with Energy-Based Policies, in JMLR Workshop and Conference Proceedings: EWRL 2012, 2012.
  
  We consider reinforcement learning in Markov decision processes with high dimensional state and action spaces. We parametrize policies using energy-based models (particularly restricted Boltzmann machines), and train them using policy gradient learning. Our approach builds upon Sallans and Hinton (2004), who parameterized value functions using energy-based models, trained using a non-linear variant of temporal-difference (TD) learning. Unfortunately, non-linear TD is known to diverge in theory and practice. We introduce the first sound and efficient algorithm for training energy-based policies, based on an actor-critic architecture. Our algorithm is computationally efficient, converges close to a local optimum, and outperforms Sallans and Hinton (2004) in several high dimensional domains.

  @inproceedings{HeeSilTeh2012a,
    author = {Heess, N. and Silver, D. and Teh, Y. W.},
    booktitle = {JMLR Workshop and Conference Proceedings: EWRL 2012},
    title = {Actor-Critic Reinforcement Learning with Energy-Based Policies},
    year = {2012},
    bdsk-url-1 = {http://www.jmlr.org/proceedings/papers/v24/heess12a.html},
    bdsk-url-2 = {http://www.jmlr.org/proceedings/papers/v24/heess12a/heess12a.pdf}
  }
  

• A. Mnih , Y. W. Teh , Learning Label Trees for Probabilistic Modelling of Implicit Feedback, in Advances in Neural Information Processing Systems (NeurIPS), 2012.
  
  @inproceedings{MniTeh2012b,
    author = {Mnih, A. and Teh, Y. W.},
    booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
    title = {Learning Label Trees for Probabilistic Modelling of Implicit Feedback},
    year = {2012},
    bdsk-url-1 = {https://papers.nips.cc/paper/4620-learning-label-trees-for-probabilistic-modelling-of-implicit-feedback},
    bdsk-url-2 = {https://papers.nips.cc/paper/4620-learning-label-trees-for-probabilistic-modelling-of-implicit-feedback.pdf},
    bdsk-url-3 = {https://www.cs.toronto.edu/\\~{}amnih/posters/cftree_poster.pdf}
  }
  

• L. Elliott , Y. W. Teh , Scalable Imputation of Genetic Data with a Discrete Fragmentation-Coagulation Process, in Advances in Neural Information Processing Systems (NeurIPS), 2012.
  
  We present a Bayesian nonparametric model for genetic sequence data in which a set of genetic sequences is modelled using a Markov model of partitions. The partitions at consecutive locations in the genome are related by their clusters first splitting and then merging. Our model can be thought of as a discrete time analogue of continuous time fragmentation-coagulation processes [Teh et al 2011], preserving the important properties of projectivity, exchangeability and reversibility, while being more scalable. We apply this model to the problem of genotype imputation, showing improved computational efficiency while maintaining the same accuracies as in [Teh et al 2011].

  @inproceedings{EllTeh2012a,
    author = {Elliott, L. and Teh, Y. W.},
    booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
    title = {Scalable Imputation of Genetic Data with a Discrete Fragmentation-Coagulation Process},
    year = {2012},
    bdsk-url-1 = {http://papers.nips.cc/paper/4782-scalable-imputation-of-genetic-data-with-a-discrete-fragmentation-coagulation-process},
    bdsk-url-2 = {http://papers.nips.cc/paper/4782-scalable-imputation-of-genetic-data-with-a-discrete-fragmentation-coagulation-process.pdf}
  }
  

• C. Wu , M. A. Suchard , A. J. Drummond , Bayesian selection of nucleotide substitution models and their site assignments, Molecular biology and evolution, vol. 30, no. 3, 669–688, 2012.
  

  @article{wu2012bayesian,
    title = {Bayesian selection of nucleotide substitution models and their site assignments},
    author = {Wu, C.-H. and Suchard, M. A. and Drummond, A. J.},
    journal = {Molecular biology and evolution},
    volume = {30},
    number = {3},
    pages = {669--688},
    year = {2012},
    publisher = {Oxford University Press}
  }
  

2011

• G. Deligiannidis , S. Utev , Asymptotic variance of the self-intersections of stable random walks using Darboux-Wiener theory, Siberian Mathematical Journal, vol. 52, no. 4, 639–650, 2011.
  

  @article{deligiannidis2011asymptotic,
    author = {Deligiannidis, G. and Utev, S.A.},
    title = {Asymptotic variance of the self-intersections of stable random walks using Darboux-Wiener theory},
    journal = {Siberian Mathematical Journal},
    year = {2011},
    volume = {52},
    number = {4},
    pages = {639-650}
  }
  

• T. S. Richardson , R. J. Evans , J. M. Robins , Transparent parameterizations of models for potential outcomes, Bayesian Statistics, vol. 9, 569–610, 2011.
  

  @article{richardson:11,
    title = {Transparent parameterizations of models for potential outcomes},
    author = {Richardson, T. S. and Evans, R. J. and Robins, J. M.},
    journal = {Bayesian Statistics},
    volume = {9},
    pages = {569--610},
    year = {2011},
    publisher = {Oxford University Press New York}
  }
  

• S. Filippi , O. Cappe , A. Garivier , Optimally Sensing a Single Channel Without Prior Information: The Tiling Algorithm and Regret Bounds, IEEE Journal of Selected Topics in Signal Processing, 2011.
  

  @article{Filippi:2011bd,
    author = {Filippi, Sarah and Cappe, Olivier and Garivier, Aurelien},
    title = {{Optimally Sensing a Single Channel Without Prior Information: The Tiling Algorithm and Regret Bounds}},
    journal = {IEEE Journal of Selected Topics in Signal Processing},
    year = {2011}
  }
  

• G. Nicholson , M. Rantalainen , J. V. Li , A. D. Maher , D. Malmodin , K. R. Ahmadi , J. H. Faber , A. Barrett , J. L. Min , N. W. Rayner , . others , A genome-wide metabolic QTL analysis in Europeans implicates two loci shaped by recent positive selection, PLoS genetics, vol. 7, no. 9, e1002270, 2011.
  

  @article{nicholson2011genome,
    title = {A genome-wide metabolic QTL analysis in Europeans implicates two loci shaped by recent positive selection},
    author = {Nicholson, George and Rantalainen, Mattias and Li, Jia V and Maher, Anthony D and Malmodin, Daniel and Ahmadi, Kourosh R and Faber, Johan H and Barrett, Amy and Min, Josine L and Rayner, N William and others},
    journal = {PLoS genetics},
    volume = {7},
    number = {9},
    pages = {e1002270},
    year = {2011},
    publisher = {Public Library of Science}
  }
  

• C. Yau , C. C. Holmes , Hierarchical Bayesian nonparametric mixture models for clustering with variable relevance determination, Bayesian analysis (Online), vol. 6, no. 2, 329, 2011.
  

  @article{yau2011hierarchical,
    title = {Hierarchical Bayesian nonparametric mixture models for clustering with variable relevance determination},
    author = {Yau, Christopher and Holmes, Chris C.},
    journal = {Bayesian analysis (Online)},
    volume = {6},
    number = {2},
    pages = {329},
    year = {2011},
    publisher = {Europe PMC Funders}
  }
  

• B. S. Kato , G. Nicholson , M. Neiman , M. Rantalainen , C. C. Holmes , A. Barrett , M. Uhlén , P. Nilsson , T. D. Spector , J. M. Schwenk , Variance decomposition of protein profiles from antibody arrays using a longitudinal twin model, Proteome science, vol. 9, no. 1, 73, 2011.
  

  @article{kato2011variance,
    title = {Variance decomposition of protein profiles from antibody arrays using a longitudinal twin model},
    author = {Kato, Bernet S and Nicholson, George and Neiman, Maja and Rantalainen, Mattias and Holmes, Chris C and Barrett, Amy and Uhl{\'e}n, Mathias and Nilsson, Peter and Spector, Tim D and Schwenk, Jochen M},
    journal = {Proteome science},
    volume = {9},
    number = {1},
    pages = {73},
    year = {2011},
    publisher = {BioMed Central}
  }
  

• M. Rantalainen , B. M. Herrera , G. Nicholson , R. Bowden , Q. F. Wills , J. L. Min , M. J. Neville , A. Barrett , M. Allen , N. W. Rayner , . others , MicroRNA expression in abdominal and gluteal adipose tissue is associated with mRNA expression levels and partly genetically driven, PloS one, vol. 6, no. 11, e27338, 2011.
  

  @article{rantalainen2011microrna,
    title = {MicroRNA expression in abdominal and gluteal adipose tissue is associated with mRNA expression levels and partly genetically driven},
    author = {Rantalainen, Mattias and Herrera, Blanca M and Nicholson, George and Bowden, Rory and Wills, Quin F and Min, Josine L and Neville, Matt J and Barrett, Amy and Allen, Maxine and Rayner, Nigel W and others},
    journal = {PloS one},
    volume = {6},
    number = {11},
    pages = {e27338},
    year = {2011},
    publisher = {Public Library of Science}
  }
  

• M. Rantalainen , C. C. Holmes , Accounting for control mislabeling in case–control biomarker studies, Journal of proteome research, vol. 10, no. 12, 5562–5567, 2011.
  

  @article{rantalainen2011accounting,
    title = {Accounting for control mislabeling in case--control biomarker studies},
    author = {Rantalainen, Mattias and Holmes, Chris C},
    journal = {Journal of proteome research},
    volume = {10},
    number = {12},
    pages = {5562--5567},
    year = {2011},
    publisher = {American Chemical Society}
  }
  

• J. Ciampa , M. Yeager , K. Jacobs , M. J. Thun , S. Gapstur , D. Albanes , J. Virtamo , S. J. Weinstein , E. Giovannucci , W. C. Willett , . others , Application of a novel score test for genetic association incorporating gene-gene interaction suggests functionality for prostate cancer susceptibility regions, Human heredity, vol. 72, no. 3, 182–193, 2011.
  

  @article{ciampa2011application,
    title = {Application of a novel score test for genetic association incorporating gene-gene interaction suggests functionality for prostate cancer susceptibility regions},
    author = {Ciampa, Julia and Yeager, Meredith and Jacobs, Kevin and Thun, Michael J and Gapstur, Susan and Albanes, Demetrius and Virtamo, Jarmo and Weinstein, Stephanie J and Giovannucci, Edward and Willett, Walter C and others},
    journal = {Human heredity},
    volume = {72},
    number = {3},
    pages = {182--193},
    year = {2011},
    publisher = {Karger Publishers}
  }
  

• N. Cardin , C. C. Holmes , P. Donnelly , J. Marchini , Bayesian hierarchical mixture modeling to assign copy number from a targeted CNV array, Genetic epidemiology, vol. 35, no. 6, 536–548, 2011.
  

  @article{cardin2011bayesian,
    title = {Bayesian hierarchical mixture modeling to assign copy number from a targeted CNV array},
    author = {Cardin, Niall and Holmes, Chris C. and Donnelly, Peter and Marchini, Jonathan},
    journal = {Genetic epidemiology},
    volume = {35},
    number = {6},
    pages = {536--548},
    year = {2011},
    publisher = {Wiley Subscription Services, Inc., A Wiley Company}
  }
  

• A. Jasra , C. C. Holmes , Stochastic boosting algorithms, Statistics and Computing, vol. 21, no. 3, 335–347, 2011.
  

  @article{jasra2011stochastic,
    title = {Stochastic boosting algorithms},
    author = {Jasra, Ajay and Holmes, Chris C},
    journal = {Statistics and Computing},
    volume = {21},
    number = {3},
    pages = {335--347},
    year = {2011},
    publisher = {Springer}
  }
  

• G. Nicholson , M. Rantalainen , A. D. Maher , J. V. Li , D. Malmodin , K. R. Ahmadi , J. H. Faber , I. B. Hallgrı́msdóttir , A. Barrett , H. Toft , . others , Human metabolic profiles are stably controlled by genetic and environmental variation, Molecular systems biology, vol. 7, no. 1, 525, 2011.
  

  @article{nicholson2011human,
    title = {Human metabolic profiles are stably controlled by genetic and environmental variation},
    author = {Nicholson, George and Rantalainen, Mattias and Maher, Anthony D and Li, Jia V and Malmodin, Daniel and Ahmadi, Kourosh R and Faber, Johan H and Hallgr{\'\i}msd{\'o}ttir, Ingileif B and Barrett, Amy and Toft, Henrik and others},
    journal = {Molecular systems biology},
    volume = {7},
    number = {1},
    pages = {525},
    year = {2011},
    publisher = {EMBO Press}
  }
  

• C. Yau , O. Papaspiliopoulos , G. O. Roberts , C. C. Holmes , Bayesian non-parametric hidden Markov models with applications in genomics, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol. 73, no. 1, 37–57, 2011.
  

  @article{yau2011bayesian,
    title = {Bayesian non-parametric hidden Markov models with applications in genomics},
    author = {Yau, Christopher and Papaspiliopoulos, Omiros and Roberts, Gareth O and Holmes, Chris C},
    journal = {Journal of the Royal Statistical Society: Series B (Statistical Methodology)},
    volume = {73},
    number = {1},
    pages = {37--57},
    year = {2011},
    publisher = {Blackwell Publishing Ltd}
  }
  

• C. C. Holmes , L. Held , . others , Response to van der Lans, Bayesian Analysis, vol. 6, no. 2, 357–358, 2011.
  

  @article{holmes2011response,
    title = {Response to van der Lans},
    author = {Holmes, Chris C. and Held, Leonhard and others},
    journal = {Bayesian Analysis},
    volume = {6},
    number = {2},
    pages = {357--358},
    year = {2011},
    publisher = {International Society for Bayesian Analysis}
  }
  

• A. Drong , G. Nicholson , M. Schuster , F. Karpe , M. McCarthy , C. Holmes , M. Rantalainen , C. Lindgren , M. Consortia , The presence of methylation quantitative trait loci indicate a direct genetic influence on the level of methylation in adipose tissue, 2011.
  

  @inproceedings{drong2011presence,
    title = {The presence of methylation quantitative trait loci indicate a direct genetic influence on the level of methylation in adipose tissue},
    author = {Drong, A and Nicholson, G and Schuster, M and Karpe, F and McCarthy, MI and Holmes, CC and Rantalainen, M and Lindgren, CM and Consortia, M},
    year = {2011}
  }
  

• J. G. Ciampa , C. C. Holmes , N. Chatterjee , Application of a novel multi-locus test for genetic association incorporating gene-gene interaction suggests functionality for multiple susceptibility loci for prostate cancer. American Association for Cancer Research, 2011.
  

  @misc{ciampa2011applicatioo,
    title = {Application of a novel multi-locus test for genetic association incorporating gene-gene interaction suggests functionality for multiple susceptibility loci for prostate cancer},
    author = {Ciampa, Julia G and Holmes, Chris C. and Chatterjee, Nilanjan},
    year = {2011},
    publisher = {American Association for Cancer Research}
  }
  

• M. Krnjajić , N. Hjort , C. Holmes , P. Müller , S. Walker , Bayesian Nonparametrics. American Statistical Association, Taylor & Francis, Ltd., 2011.
  

  @misc{krnjajic2011bayesian,
    title = {Bayesian Nonparametrics},
    author = {Krnjaji{\'c}, Milovan and Hjort, NL and Holmes, C and M{\"u}ller, P and Walker, S},
    year = {2011},
    publisher = {American Statistical Association, Taylor \& Francis, Ltd.}
  }
  

• G. K. Nicholls , A. Muir Watt , Partial Order Models for Episcopal Social Status in 12th Century England, in Proceedings of the 26th International Workshop on Statistical Modelling. Valencia (Spain), July 5-11, 2011, 2011, 437–440.
  

  @inproceedings{nicholls122011partial,
    author = {Nicholls, G. K. and Muir Watt, A.},
    title = {Partial Order Models for Episcopal Social Status in 12th Century England},
    booktitle = {Proceedings of the 26th {I}nternational {W}orkshop on {S}tatistical {M}odelling. {V}alencia ({S}pain), July 5-11, 2011},
    pages = {437-440},
    year = {2011},
    editor = {Conesa, D. and Forte, A. and López-Quílez, A. and Muñoz, F.},
    organization = {Statistical Modelling Society}
  }
  

• R. Ryder , G. K. Nicholls , Missing data in a stochastic Dollo model for binary trait data, and its application to the dating of Proto-Indo-European, Journal of the Royal Statistical Society. Series C: Applied Statistics, vol. 60, no. 1, 71–92, 2011.
  

  @article{Ryder201171,
    author = {Ryder, R.J. and Nicholls, G. K.},
    title = {Missing data in a stochastic Dollo model for binary trait data, and its application to the dating of Proto-Indo-European},
    journal = {Journal of the Royal Statistical Society. Series C: Applied Statistics},
    year = {2011},
    volume = {60},
    number = {1},
    pages = {71-92},
    doi = {10.1111/j.1467-9876.2010.00743.x},
    note = {cited By 7},
    document_type = {Article},
    source = {Scopus}
  }
  

• J. Rousseau , K. Mengersen , Asymptotic behaviour of the posterior distribution in overfitted mixture models, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol. 73, no. 5, 689–710, 2011.
  

  @article{rousseau2011asymptotic,
    title = {Asymptotic behaviour of the posterior distribution in overfitted mixture models},
    author = {Rousseau, Judith and Mengersen, Kerrie},
    journal = {Journal of the Royal Statistical Society: Series B (Statistical Methodology)},
    volume = {73},
    number = {5},
    pages = {689--710},
    year = {2011},
    publisher = {Wiley Online Library}
  }
  

• W. Dai , D. Sejdinovic , O. Milenkovic , Gaussian Dynamic Compressive Sensing, in International Conference on Sampling Theory and Applications (SampTA), 2011.
  
  We consider the problem of estimating a discrete-time sequence of sparse signals with Gaussian innovations. Instances of such problems arise in networking and imaging, in particular, dynamic and interventional MRI imaging. Our approach combines Kalman filtering and compressive sensing (CS) techniques by introducing a sparse MAP estimator for Gaussian signals, and then developing a CS-type algorithm for solving the sparse MAP problem. Despite the underlying assumption that the sequence of sparse signals is Gaussian, our approach also allows for efficient tracking of sparse non-Gaussian signals obtained via non-linear mappings, using only one sample/observation per time instance.

  @inproceedings{DaiSejMil2011,
    author = {Dai, W. and Sejdinovic, D. and Milenkovic, O.},
    booktitle = {International Conference on Sampling Theory and Applications (SampTA)},
    title = {{Gaussian Dynamic Compressive Sensing}},
    year = {2011},
    bdsk-url-1 = {http://sampta2011.ntu.edu.sg/SampTA2011Proceedings/papers/Mo5S02.2-P0186.pdf}
  }
  

• V. Rao , Y. W. Teh , Gaussian Process Modulated Renewal Processes, in Advances in Neural Information Processing Systems (NeurIPS), 2011.
  
  Renewal processes are generalizations of the Poisson process on the real line, whose intervals are drawn i.i.d. from some distribution. Modulated renewal processes allow these distributions to vary with time, allowing the introduction nonstationarity. In this work, we take a nonparametric Bayesian approach, modeling this nonstationarity with a Gaussian process. Our approach is based on the idea of uniformization, allowing us to draw exact samples from an otherwise intractable distribution. We develop a novel and efficient MCMC sampler for posterior inference. In our experiments, we test these on a number of synthetic and real datasets.

  @inproceedings{RaoTeh2011b,
    author = {Rao, V. and Teh, Y. W.},
    booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
    title = {Gaussian Process Modulated Renewal Processes},
    year = {2011},
    bdsk-url-1 = {https://papers.nips.cc/paper/4358-gaussian-process-modulated-renewal-processes},
    bdsk-url-2 = {https://papers.nips.cc/paper/4358-gaussian-process-modulated-renewal-processes.pdf},
    bdsk-url-3 = {https://papers.nips.cc/paper/4358-gaussian-process-modulated-renewal-processes-supplemental.zip}
  }
  

• V. Rao , Y. W. Teh , Fast MCMC sampling for Markov jump processes and continuous time Bayesian networks, in Uncertainty in Artificial Intelligence (UAI), 2011.
  
  Markov jump processes and continuous time Bayesian networks are important classes of continuous time dynamical systems. In this paper, we tackle the problem of inferring unobserved paths in these models by introducing a fast auxiliary variable Gibbs sampler. Our approach is based on the idea of uniformization, and sets up a Markov chain over paths by sampling a finite set of virtual jump times and then running a standard hidden Markov model forward filtering-backward sampling algorithm over states at the set of extant and virtual jump times. We demonstrate significant computational benefits over a state-of-the-art Gibbs sampler on a number of continuous time Bayesian networks.

  @inproceedings{RaoTeh2011a,
    author = {Rao, V. and Teh, Y. W.},
    booktitle = {Uncertainty in Artificial Intelligence (UAI)},
    title = {Fast {MCMC} sampling for {M}arkov jump processes and continuous time {B}ayesian networks},
    year = {2011},
    bdsk-url-1 = {https://arxiv.org/pdf/1202.3760v1.pdf}
  }
  

• D. Görür , Y. W. Teh , Concave-Convex Adaptive Rejection Sampling, Journal of Computational and Graphical Statistics, 2011.
  
  We describe a method for generating independent samples from univariate density functions using adaptive rejection sampling without the log-concavity requirement. The method makes use of the fact that many functions can be expressed as a sum of concave and convex functions. Using a concave-convex decomposition, we bound the log-density by separately bounding the concave and convex parts using piecewise linear functions. The upper bound can then be used as the proposal distribution in rejection sampling. We demonstrate the applicability of the concave-convex approach on a number of standard distributions and describe an application to the efficient construction of sequential Monte Carlo proposal distributions for inference over genealogical trees. Computer code for the proposed algorithms is available online.

  @article{GorTeh2011a,
    author = {{G\"or\"ur}, D. and Teh, Y. W.},
    doi = {10.1198/jcgs.2011.09058},
    journal = {Journal of Computational and Graphical Statistics},
    title = {Concave-Convex Adaptive Rejection Sampling},
    year = {2011},
    bdsk-url-1 = {http://amstat.tandfonline.com/doi/abs/10.1198/jcgs.2011.09058},
    bdsk-url-2 = {http://dx.doi.org/10.1198/jcgs.2011.09058}
  }
  

• C. Blundell , Y. W. Teh , K. A. Heller , Discovering Non-binary Hierarchical Structures with Bayesian Rose Trees, in Mixture Estimation and Applications, C. P. Robert, K. Mengersen, and M. Titterington, Eds. John Wiley & Sons, 2011.
  
  @incollection{BluTehHel2011a,
    author = {Blundell, C. and Teh, Y. W. and Heller, K. A.},
    booktitle = {Mixture Estimation and Applications},
    editor = {Robert, C. P. and Mengersen, K. and Titterington, M.},
    publisher = {John Wiley \& Sons},
    title = {Discovering Non-binary Hierarchical Structures with {B}ayesian Rose Trees},
    year = {2011},
    bdsk-url-1 = {http://eu.wiley.com/WileyCDA/WileyTitle/productCd-111999389X.html},
    bdsk-url-2 = {http://www.stats.ox.ac.uk/\\~{}teh/research/npbayes/BluTehHel2011a.pdf},
    bdsk-url-3 = {http://www.stats.ox.ac.uk/\\~{}teh/research/npbayes/brt.pdf}
  }
  

• R. Silva , C. Blundell , Y. W. Teh , Mixed Cumulative Distribution Networks, in Artificial Intelligence and Statistics (AISTATS), 2011.
  
  Directed acyclic graphs (DAGs) are a popular framework to express multivariate probability distributions. Acyclic directed mixed graphs (ADMGs) are generalizations of DAGs that can succinctly capture much richer sets of conditional independencies, and are especially useful in modeling the effects of latent variables implicitly. Unfortunately, there are currently no parameterizations of general ADMGs. In this paper, we apply recent work on cumulative distribution networks and copulas to propose one general construction for ADMG models. We consider a simple parameter estimation approach, and report some encouraging experimental results.

  @inproceedings{SilBluTeh2011a,
    author = {Silva, R. and Blundell, C. and Teh, Y. W.},
    booktitle = {Artificial Intelligence and Statistics (AISTATS)},
    title = {Mixed Cumulative Distribution Networks},
    year = {2011},
    bdsk-url-1 = {http://www.stats.ox.ac.uk/\\~{}teh/research/compstats/SilBluTeh2011a.pdf},
    bdsk-url-2 = {http://www.stats.ox.ac.uk/\\~{}teh/research/compstats/SilBluTeh2011a-supp.pdf}
  }
  

• Y. W. Teh , C. Blundell , L. T. Elliott , Modelling Genetic Variations with Fragmentation-Coagulation Processes, in Advances in Neural Information Processing Systems (NeurIPS), 2011.
  
  We propose a novel class of Bayesian nonparametric models for sequential data called fragmentation-coagulation processes (FCPs). FCPs model a set of sequences using a partition-valued Markov process which evolves by splitting and merging clusters. An FCP is exchangeable, projective, stationary and reversible, and its equilibrium distributions are given by the Chinese restaurant process. As opposed to hidden Markov models, FCPs allow for flexible modelling of the number of clusters, and they avoid label switching non-identifiability problems. We develop an efficient Gibbs sampler for FCPs which uses uniformization and the forward-backward algorithm. Our development of FCPs is motivated by applications in population genetics, and we demonstrate the utility of FCPs on problems of genotype imputation with phased and unphased SNP data.

  @inproceedings{TehBluEll2011a,
    author = {Teh, Y. W. and Blundell, C. and Elliott, L. T.},
    booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
    title = {Modelling Genetic Variations with Fragmentation-Coagulation Processes},
    year = {2011},
    bdsk-url-1 = {https://papers.nips.cc/paper/4211-modelling-genetic-variations-using-fragmentation-coagulation-processes},
    bdsk-url-2 = {https://papers.nips.cc/paper/4211-modelling-genetic-variations-using-fragmentation-coagulation-processes.pdf}
  }
  

• F. Wood , J. Gasthaus , C. Archambeau , L. James , Y. W. Teh , The Sequence Memoizer, Communications of the Association for Computing Machines, vol. 54, no. 2, 91–98, 2011.
  
  Probabilistic models of sequences play a central role in most machine translation, automated speech recognition, lossless compression, spell-checking, and gene identification applications to name but a few. Unfortunately, real-world sequence data often exhibit long range dependencies which can only be captured by computationally challenging, complex models. Sequence data arising from natural processes also often exhibits power-law properties, yet common sequence models do not capture such properties. The sequence memoizer is a new hierarchical Bayesian model for discrete sequence data that captures long range dependencies and power-law characteristics, while remaining computationally attractive. Its utility as a language model and general purpose lossless compressor is demonstrated.

  @article{WooGasArc2011a,
    author = {Wood, F. and Gasthaus, J. and Archambeau, C. and James, L. and Teh, Y. W.},
    journal = {Communications of the Association for Computing Machines},
    number = {2},
    pages = {91-98},
    title = {The Sequence Memoizer},
    volume = {54},
    year = {2011},
    bdsk-url-1 = {http://cacm.acm.org/magazines/2011/2/104391-the-sequence-memoizer/fulltext},
    bdsk-url-2 = {http://cacm.acm.org/magazines/2011/2/104391-the-sequence-memoizer/pdf},
    bdsk-url-3 = {http://www.stats.ox.ac.uk/\\~{}teh/research/compling/WooGasArc2011a.pdf},
    bdsk-url-4 = {http://www.stats.ox.ac.uk/\\~{}teh/research/compling/bayeslm.pdf}
  }
  

• M. Welling , Y. W. Teh , Bayesian Learning via Stochastic Gradient Langevin Dynamics, in International Conference on Machine Learning (ICML), 2011.
  
  In this paper we propose a new framework for learning from large scale datasets based on iterative learning from small mini-batches. By adding the right amount of noise to a standard stochastic gradient optimization al- gorithm we show that the iterates will con- verge to samples from the true posterior dis- tribution as we anneal the stepsize. This seamless transition between optimization and Bayesian posterior sampling provides an in- built protection against overfitting. We also propose a practical method for Monte Carlo estimates of posterior statistics which moni- tors a “sampling threshold” and collects sam- ples after it has been surpassed. We apply the method to three models: a mixture of Gaussians, logistic regression and ICA with natural gradients.

  @inproceedings{WelTeh2011a,
    author = {Welling, M. and Teh, Y. W.},
    booktitle = {International Conference on Machine Learning (ICML)},
    title = {{B}ayesian Learning via Stochastic Gradient {L}angevin Dynamics},
    year = {2011},
    bdsk-url-1 = {http://www.stats.ox.ac.uk/\\~{}teh/research/compstats/WelTeh2011a.pdf}
  }
  

• C. Wu , A. J. Drummond , Joint inference of microsatellite mutation models, population history and genealogies using transdimensional Markov Chain Monte Carlo, Genetics, vol. 188, no. 1, 151–164, 2011.
  

  @article{wu2011joint,
    title = {Joint inference of microsatellite mutation models, population history and genealogies using transdimensional Markov Chain Monte Carlo},
    author = {Wu, C.-H. and Drummond, A. J.},
    journal = {Genetics},
    volume = {188},
    number = {1},
    pages = {151--164},
    year = {2011},
    publisher = {Genetics Soc America}
  }
  

2010

• A. P. Hegle , H. Nazzari , A. Roth , D. Angoli , E. A. Accili , Evolutionary emergence of N-glycosylation as a variable promoter of HCN channel surface expression., Am J Physiol Cell Physiol, vol. 298, no. 5, C1066–C1076, May 2010.
  

  @article{HegleNazzariRothEtAl2010,
    author = {Hegle, Andrew P. and Nazzari, Hamed and Roth, Andrew and Angoli, Damiano and Accili, Eric A.},
    doi = {10.1152/ajpcell.00389.2009},
    institution = {Department of Cellular and Physiological Sciences, University of British Columbia, Vancouver, Canada.},
    journal = {Am J Physiol Cell Physiol},
    month = may,
    number = {5},
    pages = {C1066--C1076},
    title = {{Evolutionary emergence of N-glycosylation as a variable promoter of HCN channel surface expression.}},
    volume = {298},
    year = {2010}
  }
  

• R. J. Evans , T. S. Richardson , Maximum likelihood fitting of acyclic directed mixed graphs to binary data, in Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence (UAI-10), 2010, 177–184.
  

  @inproceedings{evans:10,
    author = {Evans, R. J. and Richardson, T. S.},
    booktitle = {Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence (UAI-10)},
    pages = {177-184},
    title = {Maximum likelihood fitting of acyclic directed mixed graphs to binary data},
    year = {2010}
  }
  

• S. Filippi , O. Cappe , A. Garivier , C. Szepesvari , Parametric bandits: The generalized linear case, in Neural Information Processing Systems (NIPS’2010), 2010.
  

  @inproceedings{Filippi:2010un,
    author = {Filippi, S and Cappe, O and Garivier, A and Szepesvari, C},
    title = {{Parametric bandits: The generalized linear case}},
    booktitle = {Neural Information Processing Systems (NIPS'2010)},
    year = {2010}
  }
  

• S. Filippi , O. Cappe , A. Garivier , Optimism in reinforcement learning and Kullback-Leibler divergence, in 2010 48th Annual Allerton Conference on Communication, Control, and Computing (Allerton), 2010.
  

  @inproceedings{Filippi:2010gj,
    author = {Filippi, Sarah and Cappe, Olivier and Garivier, Aurelien},
    title = {{Optimism in reinforcement learning and Kullback-Leibler divergence}},
    booktitle = {2010 48th Annual Allerton Conference on Communication, Control, and Computing (Allerton)},
    year = {2010}
  }
  

• N. Craddock , M. E. Hurles , N. Cardin , R. D. Pearson , V. Plagnol , S. Robson , D. Vukcevic , C. Barnes , D. F. Conrad , E. Giannoulatou , . others , Genome-wide association study of CNVs in 16,000 cases of eight common diseases and 3,000 shared controls, Nature, vol. 464, no. 7289, 713–720, 2010.
  

  @article{craddock2010genome,
    title = {Genome-wide association study of CNVs in 16,000 cases of eight common diseases and 3,000 shared controls},
    author = {Craddock, Nick and Hurles, Matthew E and Cardin, Niall and Pearson, Richard D and Plagnol, Vincent and Robson, Samuel and Vukcevic, Damjan and Barnes, Chris and Conrad, Donald F and Giannoulatou, Eleni and others},
    journal = {Nature},
    volume = {464},
    number = {7289},
    pages = {713--720},
    year = {2010},
    publisher = {Nature Publishing Group}
  }
  

• N. L. Hjort , C. C. Holmes , P. Müller , S. G. Walker , Bayesian nonparametrics. Cambridge University Press, 2010.
  

  @misc{hjort2010bayesian,
    title = {Bayesian nonparametrics},
    author = {Hjort, Nils Lid and Holmes, Chris C. and M{\"u}ller, Peter and Walker, Stephen G},
    year = {2010},
    publisher = {Cambridge University Press}
  }
  

• M. A. Suchard , C. C. Holmes , M. West , Some of the what?, why?, how?, who? and where? of graphics processing unit computing for Bayesian analysis, Bulletin of the International Society for Bayesian Analysis, vol. 17, no. 1, 12–16, 2010.
  

  @article{suchard2010some,
    title = {Some of the what?, why?, how?, who? and where? of graphics processing unit computing for Bayesian analysis},
    author = {Suchard, Marc A and Holmes, Chris C. and West, Mike},
    journal = {Bulletin of the International Society for Bayesian Analysis},
    volume = {17},
    number = {1},
    pages = {12--16},
    year = {2010}
  }
  

• A. Lee , F. Caron , A. Doucet , C. C. Holmes , A hierarchical Bayesian framework for constructing sparsity-inducing priors, arXiv preprint arXiv:1009.1914, 2010.
  

  @article{lee2010hierarchical,
    title = {A hierarchical Bayesian framework for constructing sparsity-inducing priors},
    author = {Lee, Anthony and Caron, Francois and Doucet, Arnaud and Holmes, Chris C.},
    journal = {arXiv preprint arXiv:1009.1914},
    year = {2010}
  }
  

• N. L. Hjort , C. C. Holmes , P. Müller , S. G. Walker , An invitation to Bayesian nonparametrics, Bayesian Nonparametrics, vol. 28, 1, 2010.
  

  @article{hjort2010invitation,
    title = {An invitation to Bayesian nonparametrics},
    author = {Hjort, Nils Lid and Holmes, Chris C. and M{\"u}ller, Peter and Walker, Stephen G},
    journal = {Bayesian Nonparametrics},
    volume = {28},
    pages = {1},
    year = {2010},
    publisher = {Cambridge University Press}
  }
  

• J. L. Davies , J. Hein , C. C. Holmes , Detecting interacting genetic loci with effects on quantitative traits where the nature and order of the interaction are unknown, Genetic epidemiology, vol. 34, no. 4, 299–308, 2010.
  

  @article{davies2010detecting,
    title = {Detecting interacting genetic loci with effects on quantitative traits where the nature and order of the interaction are unknown},
    author = {Davies, Joanna L and Hein, Jotun and Holmes, Chris C},
    journal = {Genetic epidemiology},
    volume = {34},
    number = {4},
    pages = {299--308},
    year = {2010},
    publisher = {Wiley Subscription Services, Inc., A Wiley Company}
  }
  

• T. W. Chittenden , J. Pak , R. Rubio , H. Cheng , K. Holton , N. Prendergast , V. Glinskii , Y. Cai , A. Culhane , S. Bentink , . others , Therapeutic implications of GIPC1 silencing in cancer, PloS one, vol. 5, no. 12, e15581, 2010.
  

  @article{chittenden2010therapeutic,
    title = {Therapeutic implications of GIPC1 silencing in cancer},
    author = {Chittenden, Thomas W and Pak, Jane and Rubio, Renee and Cheng, Hailing and Holton, Kristina and Prendergast, Niall and Glinskii, Vladimir and Cai, Yi and Culhane, Aedin and Bentink, Stefan and others},
    journal = {PloS one},
    volume = {5},
    number = {12},
    pages = {e15581},
    year = {2010},
    publisher = {Public Library of Science}
  }
  

• J. Griffin , C. C. Holmes , Computational issues arising in Bayesian nonparametric hierarchical models, Bayesian Nonparametrics, vol. 28, 208, 2010.
  

  @article{griffin2010computational,
    title = {Computational issues arising in Bayesian nonparametric hierarchical models},
    author = {Griffin, Jim and Holmes, Chris C.},
    journal = {Bayesian Nonparametrics},
    volume = {28},
    pages = {208},
    year = {2010},
    publisher = {Cambridge University Press}
  }
  

• I. M. Heid , A. U. Jackson , J. C. Randall , T. W. Winkler , L. Qi , V. Steinthorsdottir , G. Thorleifsson , M. C. Zillikens , E. K. Speliotes , R. Mägi , . others , Meta-analysis identifies 13 new loci associated with waist-hip ratio and reveals sexual dimorphism in the genetic basis of fat distribution, Nature genetics, vol. 42, no. 11, 949–960, 2010.
  

  @article{heid2010meta,
    title = {Meta-analysis identifies 13 new loci associated with waist-hip ratio and reveals sexual dimorphism in the genetic basis of fat distribution},
    author = {Heid, Iris M and Jackson, Anne U and Randall, Joshua C and Winkler, Thomas W and Qi, Lu and Steinthorsdottir, Valgerdur and Thorleifsson, Gudmar and Zillikens, M Carola and Speliotes, Elizabeth K and M{\"a}gi, Reedik and others},
    journal = {Nature genetics},
    volume = {42},
    number = {11},
    pages = {949--960},
    year = {2010},
    publisher = {Nature Publishing Group}
  }
  

• C. Yau , D. Mouradov , R. N. Jorissen , S. Colella , G. Mirza , G. Steers , A. Harris , J. Ragoussis , O. Sieber , C. C. Holmes , . others , A statistical approach for detecting genomic aberrations in heterogeneous tumor samples from single nucleotide polymorphism genotyping data, Genome biology, vol. 11, no. 9, R92, 2010.
  

  @article{yau2010statistical,
    title = {A statistical approach for detecting genomic aberrations in heterogeneous tumor samples from single nucleotide polymorphism genotyping data},
    author = {Yau, Christopher and Mouradov, Dmitri and Jorissen, Robert N and Colella, Stefano and Mirza, Ghazala and Steers, Graham and Harris, Adrian and Ragoussis, Jiannis and Sieber, Oliver and Holmes, Chris C and others},
    journal = {Genome biology},
    volume = {11},
    number = {9},
    pages = {R92},
    year = {2010},
    publisher = {BioMed Central Ltd}
  }
  

• C. Yau , C. C. Holmes , A decision theoretic approach for segmental classification using Hidden Markov models, Arxiv preprint arXiv:1007.4532, 2010.
  

  @article{yau2010decision,
    title = {A decision theoretic approach for segmental classification using Hidden Markov models},
    author = {Yau, Christopher and Holmes, Chris C},
    journal = {Arxiv preprint arXiv:1007.4532},
    year = {2010}
  }
  

• A. Lee , C. Yau , M. B. Giles , A. Doucet , C. C. Holmes , On the utility of graphics cards to perform massively parallel simulation of advanced Monte Carlo methods, Journal of Computational and Graphical Statistics, vol. 19, no. 4, 769–789, 2010.
  

  @article{lee2010utility,
    title = {On the utility of graphics cards to perform massively parallel simulation of advanced Monte Carlo methods},
    author = {Lee, Anthony and Yau, Christopher and Giles, Michael B and Doucet, Arnaud and Holmes, Chris C},
    journal = {Journal of Computational and Graphical Statistics},
    volume = {19},
    number = {4},
    pages = {769--789},
    year = {2010},
    publisher = {ASA}
  }
  

• T. G. Clark , S. G. Campino , E. Anastasi , S. Auburn , Y. Y. Teo , K. Small , K. A. Rockett , D. P. Kwiatkowski , C. C. Holmes , A Bayesian approach using covariance of SNP data to detect differences in linkage disequilibrium patterns between groups of individuals, Bioinformatics, 2010.
  

  @article{clark2010bayesian,
    title = {A Bayesian approach using covariance of SNP data to detect differences in linkage disequilibrium patterns between groups of individuals},
    author = {Clark, Taane G and Campino, Susana G and Anastasi, Elisa and Auburn, Sarah and Teo, Yik Y and Small, Kerrin and Rockett, Kirk A and Kwiatkowski, Dominic P and Holmes, Chris C},
    journal = {Bioinformatics},
    year = {2010},
    publisher = {Oxford Univ Press}
  }
  

• A. Agam , B. Yalcin , A. Bhomra , M. Cubin , C. Webber , C. C. Holmes , J. Flint , R. Mott , Elusive copy number variation in the mouse genome, PLoS One, vol. 5, no. 9, e12839, 2010.
  

  @article{agam2010elusive,
    title = {Elusive copy number variation in the mouse genome},
    author = {Agam, Avigail and Yalcin, Binnaz and Bhomra, Amarjit and Cubin, Matthew and Webber, Caleb and Holmes, Chris C and Flint, Jonathan and Mott, Richard},
    journal = {PLoS One},
    volume = {5},
    number = {9},
    pages = {e12839},
    year = {2010},
    publisher = {Public Library of Science}
  }
  

• N. L. Hjort , C. C. Holmes , P. Müller , S. G. Walker , Bayesian nonparametrics. Cambridge series in statistical and probabilistic mathematics, Cambridge: Cambridge Univ. Press. Mathematical Reviews (MathSciNet): MR2722987, 2010.
  

  @article{hjort2010bayesiao,
    title = {Bayesian nonparametrics. Cambridge series in statistical and probabilistic mathematics},
    author = {Hjort, Nils Lid and Holmes, Chris C. and M{\"u}ller, Peter and Walker, Stephen G},
    journal = {Cambridge: Cambridge Univ. Press. Mathematical Reviews (MathSciNet): MR2722987},
    year = {2010}
  }
  

• M. Rantalainen , B. Herrera , G. Nicholson , Q. Wills , R. Bowden , M. Neville , J. Randall , A. Barrett , M. Allen , M. McCarthy , . others , Micro-ribonucleic acid expression profiling and expression quantitative trait loci analysis in human gluteal and abdominal adipose tissue, 2010.
  

  @inproceedings{rantalainen2010micro,
    title = {Micro-ribonucleic acid expression profiling and expression quantitative trait loci analysis in human gluteal and abdominal adipose tissue},
    author = {Rantalainen, M and Herrera, BM and Nicholson, G and Wills, QF and Bowden, R and Neville, MJ and Randall, JC and Barrett, A and Allen, M and McCarthy, MI and others},
    year = {2010}
  }
  

• A. Timbs , S. Knight , E. SadighiAkha , A. Burns , H. Dreau , A. Hewitt , C. Hatton , C. Yau , C. C. Holmes , J. Taylor , . others , Quantitative Whole Genome Analysis of Sequential Samples From Patients with B-CLL Identifies Novel Recurrent Copy Number Alterations Involving Critical B-Cell Transcription Factors, Blood, vol. 116, no. 21, 3590–3590, 2010.
  

  @article{timbs2010quantitative,
    title = {Quantitative Whole Genome Analysis of Sequential Samples From Patients with B-CLL Identifies Novel Recurrent Copy Number Alterations Involving Critical B-Cell Transcription Factors},
    author = {Timbs, Adele and Knight, Sam and SadighiAkha, Elham and Burns, Adam and Dreau, Helene and Hewitt, Anne-Marie and Hatton, Chris and Yau, Chris and Holmes, Chris C. and Taylor, Jenny and others},
    journal = {Blood},
    volume = {116},
    number = {21},
    pages = {3590--3590},
    year = {2010},
    publisher = {American Society of Hematology}
  }
  

• Q. Zhou , A. K. Ching , W. K. Leung , C. Szeto , S. Ho , C. C. Holmes , Y. Yuan , P. B. Lai , W. Yeo , N. Wong , Novel therapeutic potential in targeting the microtubules by nanoparticle albumin-bound paclitaxel in hepatocellular carcinoma. American Association for Cancer Research, 2010.
  

  @misc{zhou2010novel,
    title = {Novel therapeutic potential in targeting the microtubules by nanoparticle albumin-bound paclitaxel in hepatocellular carcinoma},
    author = {Zhou, Qian and Ching, Arthur KK and Leung, Wilson KC and Szeto, Carol and Ho, Shuk-Mei and Holmes, Chris C and Yuan, Yun-fei and Lai, Paul BS and Yeo, Winnie and Wong, Nathalie},
    year = {2010},
    publisher = {American Association for Cancer Research}
  }
  

• C. Holmes , P. Müller , S. Walker , Bayesian nonparametrics, Cambridge Univ Pr, 2010.
  

  @article{holmes2010bayesian,
    title = {Bayesian nonparametrics},
    author = {Holmes, C and M{\"u}ller, P and Walker, S},
    journal = {Cambridge Univ Pr},
    year = {2010}
  }
  

• G. K. Nicholls , P. Nunn , On building and fitting a spatio-temporal change-point model for settlement and growth at Bourewa, Fiji Islands, arXiv preprint arXiv:1006.5575, 2010.
  

  @article{nicholls2010building,
    title = {On building and fitting a spatio-temporal change-point model for settlement and growth at {B}ourewa, {F}iji {I}slands},
    author = {Nicholls, G. K. and Nunn, P.D.},
    journal = {arXiv preprint arXiv:1006.5575},
    year = {2010}
  }
  

• W. Kruijer , J. Rousseau , A. Van Der Vaart , . others , Adaptive Bayesian density estimation with location-scale mixtures, Electronic Journal of Statistics, vol. 4, 1225–1257, 2010.
  

  @article{kruijer2010adaptive,
    title = {Adaptive Bayesian density estimation with location-scale mixtures},
    author = {Kruijer, Willem and Rousseau, Judith and Van Der Vaart, Aad and others},
    journal = {Electronic Journal of Statistics},
    volume = {4},
    pages = {1225--1257},
    year = {2010},
    publisher = {The Institute of Mathematical Statistics and the Bernoulli Society}
  }
  

• J. Rousseau , . others , Rates of convergence for the posterior distributions of mixtures of betas and adaptive nonparametric estimation of the density, The Annals of Statistics, vol. 38, no. 1, 146–180, 2010.
  

  @article{rousseau2010rates,
    title = {Rates of convergence for the posterior distributions of mixtures of betas and adaptive nonparametric estimation of the density},
    author = {Rousseau, Judith and others},
    journal = {The Annals of Statistics},
    volume = {38},
    number = {1},
    pages = {146--180},
    year = {2010},
    publisher = {Institute of Mathematical Statistics}
  }
  

• D. Gajda , C. Guihenneuc-Jouyaux , J. Rousseau , K. Mengersen , D. Nur , . others , Use in practice of importance sampling for repeated MCMC for Poisson models, Electronic journal of statistics, vol. 4, 361–383, 2010.
  

  @article{gajda2010use,
    title = {Use in practice of importance sampling for repeated MCMC for Poisson models},
    author = {Gajda, Dorota and Guihenneuc-Jouyaux, Chantal and Rousseau, Judith and Mengersen, Kerry and Nur, Darfiana and others},
    journal = {Electronic journal of statistics},
    volume = {4},
    pages = {361--383},
    year = {2010},
    publisher = {The Institute of Mathematical Statistics and the Bernoulli Society}
  }
  

• D. Sejdinovic , O. Johnson , Note on noisy group testing: asymptotic bounds and belief propagation reconstruction, in 48th Annual Allerton Conference on Communication, Control, and Computing, 2010, 998–1003.
  
  An information theoretic perspective on group testing problems has recently been proposed by Atia and Saligrama, in order to characterise the optimal number of tests. Their results hold in the noiseless case, where only false positives occur, and where only false negatives occur. We extend their results to a model containing both false positives and false negatives, developing simple information theoretic bounds on the number of tests required. Based on these bounds, we obtain an improved order of convergence in the case of false negatives only. Since these results are based on (computationally infeasible) joint typicality decoding, we propose a belief propagation algorithm for the detection of defective items and compare its actual performance to the theoretical bounds.

  @inproceedings{SejJoh2010,
    author = {Sejdinovic, D. and Johnson, O.},
    booktitle = {48th Annual Allerton Conference on Communication, Control, and Computing},
    doi = {10.1109/ALLERTON.2010.5707018},
    pages = {998--1003},
    title = {Note on noisy group testing: asymptotic bounds and belief propagation reconstruction},
    year = {2010},
    bdsk-url-1 = {http://dx.doi.org/10.1109/ALLERTON.2010.5707018}
  }
  

• D. Sejdinovic , R. Piechocki , A. Doufexi , M. Ismail , Decentralised distributed fountain coding: asymptotic analysis and design, IEEE Communications Letters, vol. 14, no. 1, 42–44, 2010.
  
  A class of generic decentralised distributed fountain coding schemes is introduced and the tools of analysis of the performance of such schemes are presented. It is demonstrated that the developed approach can be used to formulate a robust code design methodology in a number of instances. We show that two non-standard applications of fountain codes, fountain codes for distributed source coding and fountain codes for unequal error protection lie within this decentralised distributed fountain coding framework.

  @article{SejPieDouIsm2010,
    author = {Sejdinovic, D. and Piechocki, R. and Doufexi, A. and Ismail, M.},
    doi = {10.1109/LCOMM.2010.01.091541},
    file = {pdf/2010CommLetter.pdf},
    journal = {IEEE Communications Letters},
    number = {1},
    pages = {42--44},
    title = {Decentralised distributed fountain coding: asymptotic analysis and design},
    volume = {14},
    year = {2010},
    bdsk-url-1 = {http://dx.doi.org/10.1109/LCOMM.2010.01.091541}
  }
  

• D. Sejdinovic , C. Andrieu , R. Piechocki , Bayesian sequential compressed sensing in sparse dynamical systems, in 48th Annual Allerton Conference on Communication, Control, and Computing, 2010, 1730–1736.
  
  While the theory of compressed sensing provides means to reliably and efficiently acquire a sparse high-dimensional signal from a small number of its linear projections, sensing of dynamically changing sparse signals is still not well understood. We pursue a Bayesian approach to the problem of sequential compressed sensing and develop methods to recursively estimate the full posterior distribution of the signal.

  @inproceedings{SejAndPie2010,
    author = {Sejdinovic, D. and Andrieu, C. and Piechocki, R.},
    booktitle = {48th Annual Allerton Conference on Communication, Control, and Computing},
    doi = {10.1109/ALLERTON.2010.5707125},
    pages = {1730--1736},
    title = {Bayesian sequential compressed sensing in sparse dynamical systems},
    year = {2010},
    bdsk-url-1 = {http://dx.doi.org/10.1109/ALLERTON.2010.5707125}
  }
  

• C. Blundell , Y. W. Teh , K. A. Heller , Bayesian Rose Trees, in Uncertainty in Artificial Intelligence (UAI), 2010.
  
  Hierarchical structure is ubiquitous in data across many domains. There are many hierarchical clustering methods, frequently used by domain experts, which strive to discover this structure. However, most of these methods limit discoverable hierarchies to those with binary branching structure. This limitation, while computationally convenient, is often undesirable. In this paper we explore a Bayesian hierarchical clustering algorithm that can produce trees with arbitrary branching structure at each node, known as rose trees. We interpret these trees as mixtures over partitions of a data set, and use a computationally efficient, greedy agglomerative algorithm to find the rose trees which have high marginal likelihood given the data. Lastly, we perform experiments which demonstrate that rose trees are better models of data than the typical binary trees returned by other hierarchical clustering algorithms.

  @inproceedings{BluTehHel2010a,
    author = {Blundell, C. and Teh, Y. W. and Heller, K. A.},
    booktitle = {Uncertainty in Artificial Intelligence (UAI)},
    title = {{B}ayesian Rose Trees},
    year = {2010},
    bdsk-url-1 = {https://arxiv.org/pdf/1203.3468v1.pdf}
  }
  

• J. Gasthaus , Y. W. Teh , Improvements to the Sequence Memoizer, in Advances in Neural Information Processing Systems (NeurIPS), 2010.
  
  The sequence memoizer is a model for sequence data with state-of-the-art performance on language modeling and compression. We propose a number of improvements to the model and inference algorithm, including an enlarged range of hyperparameters, a memory-efficient representation, and inference algorithms operating on the new representation. Our derivations are based on precise definitions of the various processes that will also allow us to provide an elementary proof of the mysterious coagulation and fragmentation properties used in the original paper on the sequence memoizer by Wood et al. (2009). We present some experimental results supporting our improvements.

  @inproceedings{GasTeh2010a,
    author = {Gasthaus, J. and Teh, Y. W.},
    booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
    title = {Improvements to the Sequence Memoizer},
    year = {2010},
    bdsk-url-1 = {https://papers.nips.cc/paper/3938-improvements-to-the-sequence-memoizer},
    bdsk-url-2 = {https://papers.nips.cc/paper/3938-improvements-to-the-sequence-memoizer.pdf},
    bdsk-url-3 = {https://papers.nips.cc/paper/3938-improvements-to-the-sequence-memoizer-supplemental.zip}
  }
  

• Y. W. Teh , M. I. Jordan , Hierarchical Bayesian Nonparametric Models with Applications, in Bayesian Nonparametrics, N. Hjort, C. Holmes, P. Müller, and S. Walker, Eds. Cambridge University Press, 2010.
  
  Hierarchical modeling is a fundamental concept in Bayesian statistics. The basic idea is that parameters are endowed with distributions which may themselves introduce new parameters, and this construction recurses. In this review we discuss the role of hierarchical modeling in Bayesian nonparametrics, focusing on models in which the infinite-dimensional parameters are treated hierarchically. For example, we consider a model in which the base measure for a Dirichlet process is itself treated as a draw from another Dirichlet process. This yields a natural recursion that we refer to as a hierarchical Dirichlet process. We also discuss hierarchies based on the Pitman-Yor process and on completely random processes. We demonstrate the value of these hierarchical constructions in a wide range of practical applications, in problems in computational biology, computer vision and natural language processing.

  @incollection{TehJor2010a,
    annote = {Technical Report 770. Department of Statistics, University of California
      at Berkeley.
      
      },
    author = {Teh, Y. W. and Jordan, M. I.},
    booktitle = {Bayesian Nonparametrics},
    editor = {Hjort, N. and Holmes, C. and M{\"u}ller, P. and Walker, S.},
    publisher = {Cambridge University Press},
    title = {Hierarchical {B}ayesian Nonparametric Models with Applications},
    year = {2010},
    bdsk-url-1 = {http://www.stats.ox.ac.uk/\\~{}teh/research/npbayes/TehJor2010a.pdf}
  }
  

• Y. W. Teh , Dirichlet Processes, in Encyclopedia of Machine Learning, Springer, 2010.
  
  @incollection{Teh2010a,
    author = {Teh, Y. W.},
    booktitle = {Encyclopedia of Machine Learning},
    publisher = {Springer},
    title = {{D}irichlet Processes},
    year = {2010},
    bdsk-url-1 = {http://www.stats.ox.ac.uk/\\~{}teh/research/npbayes/Teh2010a.pdf}
  }
  

• P. Orbanz , Y. W. Teh , Bayesian Nonparametric Models, in Encyclopedia of Machine Learning, Springer, 2010.
  
  @incollection{OrbTeh2010a,
    author = {Orbanz, P. and Teh, Y. W.},
    booktitle = {Encyclopedia of Machine Learning},
    publisher = {Springer},
    title = {{B}ayesian Nonparametric Models},
    year = {2010},
    bdsk-url-1 = {http://www.stats.ox.ac.uk/\\~{}teh/research/npbayes/OrbTeh2010a.pdf}
  }
  

• J. Gasthaus , F. Wood , Y. W. Teh , Lossless compression based on the Sequence Memoizer, in Data Compression Conference, 2010.
  
  In this work we describe a sequence compression method based on combining a Bayesian nonparametric sequence model with entropy encoding. The model, a hierarchy of Pitman-Yor processes of unbounded depth previously proposed by Wood et al. [2009] in the context of language modelling, allows modelling of long-range dependencies by allowing conditioning contexts of unbounded length. We show that incremental approximate inference can be performed in this model, thereby allowing it to be used in a text compression setting. The resulting compressor reliably outperforms several PPM variants on many types of data, but is particularly effective in compressing data that exhibits power law properties.

  @inproceedings{GasWooTeh2010a,
    author = {Gasthaus, J. and Wood, F. and Teh, Y. W.},
    booktitle = {Data Compression Conference},
    title = {Lossless compression based on the Sequence Memoizer},
    year = {2010},
    bdsk-url-1 = {http://www.stats.ox.ac.uk/\\~{}teh/research/compling/GasWooTeh2010a.pdf}
  }
  

2009

• F. Caron , A. Doucet , Bayesian Nonparametric Models on Decomposable Graphs, in Advances in Neural Information Processing Systems (NeurIPS), 2009.
  

  @inproceedings{Caron2009,
    title = {Bayesian Nonparametric Models on Decomposable Graphs},
    author = {Caron, F. and Doucet, A.},
    booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
    year = {2009},
    owner = {caron},
    timestamp = {2016.10.24}
  }
  

• G. Deligiannidis , H. Le , S. Utev , Optimal stopping for processes with independent increments , and applications, Journal of Applied Probability, vol. 46, no. 4, 1130–1145, 2009.
  

  @article{deligiannidis2009optimal,
    author = {Deligiannidis, G. and Le, H. and Utev, S.},
    title = {Optimal stopping for processes with independent increments , and applications},
    journal = {Journal of Applied Probability},
    year = {2009},
    volume = {46},
    number = {4},
    pages = {1130-1145}
  }
  

• J. E. Lemieux , N. Gomez-Escobar , A. Feller , C. Carret , A. Amambua-Ngwa , R. Pinches , F. Day , S. A. Kyes , D. J. Conway , C. C. Holmes , . others , Statistical estimation of cell-cycle progression and lineage commitment in Plasmodium falciparum reveals a homogeneous pattern of transcription in ex vivo culture, Proceedings of the National Academy of Sciences, vol. 106, no. 18, 7559–7564, 2009.
  

  @article{lemieux2009statistical,
    title = {Statistical estimation of cell-cycle progression and lineage commitment in Plasmodium falciparum reveals a homogeneous pattern of transcription in ex vivo culture},
    author = {Lemieux, Jacob E and Gomez-Escobar, Natalia and Feller, Avi and Carret, Celine and Amambua-Ngwa, Alfred and Pinches, Robert and Day, Felix and Kyes, Sue A and Conway, David J and Holmes, Chris C and others},
    journal = {Proceedings of the National Academy of Sciences},
    volume = {106},
    number = {18},
    pages = {7559--7564},
    year = {2009},
    publisher = {National Acad Sciences}
  }
  

• J. W. Klingelhoefer , L. Moutsianas , C. C. Holmes , Approximate Bayesian feature selection on a large meta-dataset offers novel insights on factors that effect siRNA potency, Bioinformatics, vol. 25, no. 13, 1594–1601, 2009.
  

  @article{klingelhoefer2009approximate,
    title = {Approximate Bayesian feature selection on a large meta-dataset offers novel insights on factors that effect siRNA potency},
    author = {Klingelhoefer, Jochen W and Moutsianas, Loukas and Holmes, Chris C.},
    journal = {Bioinformatics},
    volume = {25},
    number = {13},
    pages = {1594--1601},
    year = {2009},
    publisher = {Oxford Univ Press}
  }
  

• S. Anjum , A. Doucet , C. C. Holmes , A boosting approach to structure learning of graphs with and without prior knowledge, Bioinformatics, vol. 25, no. 22, 2929–2936, 2009.
  

  @article{anjum2009boosting,
    title = {A boosting approach to structure learning of graphs with and without prior knowledge},
    author = {Anjum, Shahzia and Doucet, Arnaud and Holmes, Chris C},
    journal = {Bioinformatics},
    volume = {25},
    number = {22},
    pages = {2929--2936},
    year = {2009},
    publisher = {Oxford Univ Press}
  }
  

• A. Feller , C. C. Holmes , Beyond toplines: Heterogeneous treatment effects in randomized experiments, Unpublished manuscript, Oxford University, 2009.
  

  @article{feller2009beyond,
    title = {Beyond toplines: Heterogeneous treatment effects in randomized experiments},
    author = {Feller, Avi and Holmes, Chris C},
    journal = {Unpublished manuscript, Oxford University},
    year = {2009}
  }
  

• F. Mackenzie , A. Parker , N. Parkinson , P. Oliver , D. Brooker , P. Underhill , V. Lukashkina , A. Lukashkin , C. Holmes , S. Brown , Analysis of the mouse mutant Cloth-ears shows a role for the voltage-gated sodium channel Scn8a in peripheral neural hearing loss, Genes, Brain and Behavior, vol. 8, no. 7, 699–713, 2009.
  

  @article{mackenzie2009analysis,
    title = {Analysis of the mouse mutant Cloth-ears shows a role for the voltage-gated sodium channel Scn8a in peripheral neural hearing loss},
    author = {Mackenzie, FE and Parker, A and Parkinson, NJ and Oliver, PL and Brooker, D and Underhill, P and Lukashkina, VA and Lukashkin, AN and Holmes, C and Brown, SDM},
    journal = {Genes, Brain and Behavior},
    volume = {8},
    number = {7},
    pages = {699--713},
    year = {2009},
    publisher = {Wiley Online Library}
  }
  

• C. C. Holmes , A. Jasra , Antithetic methods for gibbs samplers, Journal of Computational and Graphical Statistics, vol. 18, no. 2, 401–414, 2009.
  

  @article{holmes2009antithetic,
    title = {Antithetic methods for gibbs samplers},
    author = {Holmes, Chris C. and Jasra, Ajay},
    journal = {Journal of Computational and Graphical Statistics},
    volume = {18},
    number = {2},
    pages = {401--414},
    year = {2009},
    publisher = {Taylor \& Francis}
  }
  

• W. Valdar , C. C. Holmes , R. Mott , J. Flint , Mapping in structured populations by resample model averaging, Genetics, vol. 182, no. 4, 1263–1277, 2009.
  

  @article{valdar2009mapping,
    title = {Mapping in structured populations by resample model averaging},
    author = {Valdar, William and Holmes, Chris C and Mott, Richard and Flint, Jonathan},
    journal = {Genetics},
    volume = {182},
    number = {4},
    pages = {1263--1277},
    year = {2009},
    publisher = {Genetics}
  }
  

• A. Antonyuk , C. Holmes , On testing for genetic association in case-control studies when population allele frequencies are known, Genetic epidemiology, vol. 33, no. 5, 371–378, 2009.
  

  @article{antonyuk2009testing,
    title = {On testing for genetic association in case-control studies when population allele frequencies are known},
    author = {Antonyuk, A and Holmes, C},
    journal = {Genetic epidemiology},
    volume = {33},
    number = {5},
    pages = {371--378},
    year = {2009},
    publisher = {Wiley Online Library}
  }
  

• J. E. Lemieux , A. Feller , C. C. Holmes , C. I. Newbold , Reply to Wirth et al.: In vivo profiles show continuous variation between 2 cellular populations, Proceedings of the National Academy of Sciences, vol. 106, no. 27, E71–E72, 2009.
  

  @article{lemieux2009reply,
    title = {Reply to Wirth et al.: In vivo profiles show continuous variation between 2 cellular populations},
    author = {Lemieux, Jacob E and Feller, Avi and Holmes, Chris C and Newbold, Chris I},
    journal = {Proceedings of the National Academy of Sciences},
    volume = {106},
    number = {27},
    pages = {E71--E72},
    year = {2009},
    publisher = {National Acad Sciences}
  }
  

• C. C. Holmes , Increasing statistical power and generalizability in genomics microarray research, PhD thesis, University of Oxford, 2009.
  

  @phdthesis{holmes2009increasing,
    title = {Increasing statistical power and generalizability in genomics microarray research},
    author = {Holmes, Chris C.},
    year = {2009},
    school = {University of Oxford}
  }
  

• D. Roy , G. K. Nicholls , C. Fox , Imaging convex quadrilateral inclusions in uniform conductors from electrical boundary measurements, Statistics and Computing, vol. 19, no. 1, 17–26, 2009.
  

  @article{Roy200917,
    author = {Roy, D. and Nicholls, G. K. and Fox, C.},
    title = {Imaging convex quadrilateral inclusions in uniform conductors from electrical boundary measurements},
    journal = {Statistics and Computing},
    year = {2009},
    volume = {19},
    number = {1},
    pages = {17-26},
    doi = {10.1007/s11222-008-9064-0},
    note = {cited By 2},
    document_type = {Article},
    source = {Scopus}
  }
  

• C. P. Robert , N. Chopin , J. Rousseau , . others , Harold Jeffreys’s theory of probability revisited, Statistical Science, vol. 24, no. 2, 141–172, 2009.
  

  @article{robert2009harold,
    title = {Harold Jeffreys’s theory of probability revisited},
    author = {Robert, Christian P and Chopin, Nicolas and Rousseau, Judith and others},
    journal = {Statistical Science},
    volume = {24},
    number = {2},
    pages = {141--172},
    year = {2009},
    publisher = {Institute of Mathematical Statistics}
  }
  

• R. Mcvinish , J. Rousseau , K. Mengersen , Bayesian goodness of fit testing with mixtures of triangular distributions, Scandinavian Journal of Statistics, vol. 36, no. 2, 337–354, 2009.
  

  @article{mcvinish2009bayesian,
    title = {Bayesian goodness of fit testing with mixtures of triangular distributions},
    author = {Mcvinish, Ross and Rousseau, Judith and Mengersen, Kerrie},
    journal = {Scandinavian Journal of Statistics},
    volume = {36},
    number = {2},
    pages = {337--354},
    year = {2009},
    publisher = {Wiley Online Library}
  }
  

• D. Nur , D. Allingham , J. Rousseau , K. L. Mengersen , R. McVinish , Bayesian hidden Markov model for DNA sequence segmentation: A prior sensitivity analysis, Computational Statistics & Data Analysis, vol. 53, no. 5, 1873–1882, 2009.
  

  @article{nur2009bayesian,
    title = {Bayesian hidden Markov model for DNA sequence segmentation: A prior sensitivity analysis},
    author = {Nur, Darfiana and Allingham, David and Rousseau, Judith and Mengersen, Kerrie L and McVinish, Ross},
    journal = {Computational Statistics \& Data Analysis},
    volume = {53},
    number = {5},
    pages = {1873--1882},
    year = {2009},
    publisher = {Elsevier}
  }
  

• D. Sejdinovic , D. Vukobratovic , A. Doufexi , V. Senk , R. Piechocki , Expanding window fountain codes for unequal error protection, IEEE Transactions on Communications, vol. 57, no. 9, 2510–2516, 2009.
  
  A novel approach to provide unequal error protection (UEP) using rateless codes over erasure channels, named Expanding Window Fountain (EWF) codes, is developed and discussed. EWF codes use a windowing technique rather than a weighted (non-uniform) selection of input symbols to achieve UEP property. The windowing approach introduces additional parameters in the UEP rateless code design, making it more general and flexible than the weighted approach. Furthermore, the windowing approach provides better performance of UEP scheme, which is confirmed both theoretically and experimentally.

  @article{SejVukDouSenPie2009,
    author = {Sejdinovic, D. and Vukobratovic, D. and Doufexi, A. and Senk, V. and Piechocki, R.},
    doi = {10.1109/TCOMM.2009.09.070616},
    file = {pdf/2009TransComm.pdf},
    journal = {IEEE Transactions on Communications},
    number = {9},
    pages = {2510--2516},
    title = {Expanding window fountain codes for unequal error protection},
    volume = {57},
    year = {2009},
    bdsk-url-1 = {http://dx.doi.org/10.1109/TCOMM.2009.09.070616}
  }
  

• D. Vukobratovic , V. Stankovic , D. Sejdinovic , L. Stankovic , Z. Xiong , Scalable video multicast using expanding window fountain codes, IEEE Transactions on Multimedia, vol. 11, no. 6, 1094–1104, 2009.
  
  Fountain codes were introduced as an efficient and universal forward error correction (FEC) solution for data multicast over lossy packet networks. They have recently been proposed for large scale multimedia content delivery in practical multimedia distribution systems. However, standard fountain codes, such as LT or Raptor codes, are not designed to meet unequal error protection (UEP) requirements typical in real-time scalable video multicast applications. In this paper, we propose recently introduced UEP expanding window fountain (EWF) codes as a flexible and efficient solution for real-time scalable video multicast. We demonstrate that the design flexibility and UEP performance make EWF codes ideally suited for this scenario, i.e., EWF codes offer a number of design parameters to be tuned at the server side to meet the different reception criteria of heterogeneous receivers. The performance analysis using both analytical results and simulation experiments of H.264 scalable video coding (SVC) multicast to heterogeneous receiver classes confirms the flexibility and efficiency of the proposed EWF-based FEC solution.

  @article{VukStaSejStaXio2009,
    author = {Vukobratovic, D. and Stankovic, V. and Sejdinovic, D. and Stankovic, L. and Xiong, Z.},
    doi = {10.1109/TMM.2009.2026087},
    file = {pdf/2009TransMultimedia.pdf},
    journal = {IEEE Transactions on Multimedia},
    number = {6},
    pages = {1094--1104},
    title = {Scalable video multicast using expanding window fountain codes},
    volume = {11},
    year = {2009},
    bdsk-url-1 = {http://dx.doi.org/10.1109/TMM.2009.2026087}
  }
  

• D. Sejdinovic , R. Piechocki , A. Doufexi , M. Ismail , Fountain code design for data multicast with side information, IEEE Transactions on Wireless Communications, vol. 8, no. 10, 5155–5165, 2009.
  
  Fountain codes are a robust solution for data multicasting to a large number of receivers which experience variable channel conditions and different packet loss rates. However, the standard fountain code design becomes inefficient if all receivers have access to some side information correlated with the source information. We focus our attention on the cases where the correlation of the source and side information can be modelled by a binary erasure channel (BEC) or by a binary input additive white Gaussian noise channel (BIAWGNC). We analyse the performance of fountain codes in data multicasting with side information for these cases, derive bounds on their performance and provide a fast and robust linear programming optimization framework for code parameters. We demonstrate that systematic Raptor code design can be employed as a possible solution to the problem at the cost of higher encoding/decoding complexity, as it reduces the side information scenario to a channel coding problem. However, our results also indicate that a simpler solution, non-systematic LT and Raptor codes, can be designed to perform close to the information theoretic bounds.

  @article{SejPieDouIsm2009,
    author = {Sejdinovic, D. and Piechocki, R. and Doufexi, A. and Ismail, M.},
    doi = {10.1109/TWC.2009.081076},
    file = {pdf/2009TransWirelessComm.pdf},
    journal = {IEEE Transactions on Wireless Communications},
    number = {10},
    pages = {5155--5165},
    publisher = {IEEE},
    title = {Fountain code design for data multicast with side information},
    volume = {8},
    year = {2009},
    bdsk-url-1 = {http://dx.doi.org/10.1109/TWC.2009.081076}
  }
  

• D. Sejdinovic , R. Piechocki , A. Doufexi , AND-OR tree analysis of distributed LT codes, in IEEE Information Theory Workshop (ITW), 2009, 261–265.
  
  In this contribution, we consider design of distributed LT codes, i.e., independent rateless encodings of multiple sources which communicate to a common relay, where relay is able to combine incoming packets from the sources and forwards them to receivers. We provide density evolution formulae for distributed LT codes, which allow us to formulate distributed LT code design problem and prove the equivalence of performance of distributed LT codes and LT codes with related parameters in the asymptotic regime. Furthermore, we demonstrate that allowing LT coding apparatus at both the sources and the relay may prove advantageous to coding only at the sources and coding only at the relay.

  @inproceedings{SejPieDou2009ITW,
    author = {Sejdinovic, D. and Piechocki, R.J. and Doufexi, A.},
    booktitle = {IEEE Information Theory Workshop (ITW)},
    doi = {10.1109/ITWNIT.2009.5158583},
    file = {pdf/2009ITW.pdf},
    pages = {261--265},
    title = {{AND-OR tree analysis of distributed LT codes}},
    year = {2009},
    bdsk-url-1 = {http://dx.doi.org/10.1109/ITWNIT.2009.5158583}
  }
  

• D. Vukobratovic , V. Stankovic , L. Stankovic , D. Sejdinovic , Precoded EWF codes for unequal error protection of scalable video, in International ICST Mobile Multimedia Communications Conference (MOBIMEDIA), 2009.
  
  Rateless codes are forward error correcting (FEC) codes of linear encoding-decoding complexity and asymptotically capacity-approaching performance over erasure channels with any erasure statistics. They have been recently recognized as a simple and efficient solution for packetized video transmission over networks with packet erasures. However, to adapt the error correcting capabilities of rateless codes to the unequal importance of scalable video, unequal error protection (UEP) rateless codes are proposed as an alternative to standard rateless codes. In this paper, we extend our recent work on UEP rateless codes called Expanding Window Fountain (EWF) codes in order to improve their UEP performance. We investigate the design of precoded EWF codes, where precoding is done using high-rate Low-Density Parity-Check (LDPC) codes, following the similar reasoning applied in the design of Raptor codes. The obtained results are presented in the context of UEP error correcting performance of EWF codes and applied on scalable video coded (SVC) transmission over erasure networks.

  @inproceedings{VukStaStaSej2009,
    author = {Vukobratovic, D. and Stankovic, V. and Stankovic, L. and Sejdinovic, D.},
    booktitle = {International ICST Mobile Multimedia Communications Conference (MOBIMEDIA)},
    file = {pdf/2009MobimediaB.pdf},
    title = {{Precoded EWF codes for unequal error protection of scalable video}},
    year = {2009},
    bdsk-url-1 = {http://portal.acm.org/citation.cfm?id=1653559}
  }
  

• D. Sejdinovic , R. Piechocki , A. Doufexi , Rateless distributed source code design, in International ICST Mobile Multimedia Communications Conference (MOBIMEDIA), 2009.
  
  Over the past decade, rateless codes, i.e., digital fountain codes, have emerged as an efficient and robust solution for reliable data transmission over packet erasure networks and a particularly suitable one for multicasting and broadcasting applications where users may experience variable channel conditions and packet loss rates, such as mobile environments. Luby Transform (LT) and Raptor codes are practical fountain codes with a capacity approaching performance and a low computational cost. In addition to their channel coding applications, the use of fountain codes for various kinds of distributed source compression and distributed joint-source channel coding has been extensively studied lately, and with promising results. However, a systematic treatise of the code design and optimization considerations for such non-standard applications of fountain codes is still absent. In this contribution, we overview the main results concerned with rateless codes for distributed source coding and outline several examples of data dissemination protocols where carefully designed fountain codes can provide strikingly simple, yet robust solutions yielding both distributed source coding and channel coding gains.

  @inproceedings{SejPieDou2009,
    author = {Sejdinovic, D. and Piechocki, R.J. and Doufexi, A.},
    booktitle = {International ICST Mobile Multimedia Communications Conference (MOBIMEDIA)},
    file = {pdf/2009MobimediaA.pdf},
    title = {Rateless distributed source code design},
    year = {2009},
    bdsk-url-1 = {http://portal.acm.org/citation.cfm?id=1653578}
  }
  

• D. Sejdinovic , Topics in Fountain Coding, PhD thesis, University of Bristol, 2009.
  
  The invention of the sparse graph codes, error correction codes with low complexity and rates close to capacity, has had an unrivaled impact on digital communication systems. A recent advance in the sparse graph codes, fountain coding, due to its natural rate adaptivity, is becoming an error correction coding scheme of choice for many multicasting and broadcasting systems. This thesis studies the use of fountain codes for several non-standard coding problems commonly occuring in communications. Generic decentralised distributed fountain coding schemes for networked communications are developed, discussed and analysed, where many non-cooperating source nodes communicate possibly correlated data to a large number of receivers. Several results concerning the generalised asymptotic analysis of the fountain decoder in this decentralised and distributed coding setting are presented. The problem of fountain codes with unequal error protection property is explored, where a novel class of fountain codes, Expanding Window Fountain (EWF) codes, is proposed, analysed and shown to offer competitive performance applicable to scalable video multicasting. Further, asymptotic analysis, code design and optimisation are derived for both symmetric and asymmetric Slepian-Wolf coding with fountain codes. It is shown how one can obtain both channel coding and distributed source coding gains with the same fountain coding scheme, by a judicious choice of the code parameters. The developed methods of asymptotic analysis are extended to the problem of independent fountain encodings at multiple source nodes which communicate to a common relay. It is shown that the re-encoding of the multiple fountain encoded bitstreams at the relay node with another fountain code may reduce the number of required transmissions, and the overall code optimisation methods of such schemes are derived. Finally, dual fountain codes are introduced and equipped with a low complexity quantisation algorithm for a lossy source coding problem dual to binary erasure channel coding.

  @phdthesis{Sej2009,
    author = {Sejdinovic, D.},
    file = {pdf/PhD_TopicsInFountainCoding.pdf},
    school = {University of Bristol},
    title = {Topics in Fountain Coding},
    year = {2009}
  }
  

• V. Rao , Y. W. Teh , Spatial Normalized Gamma Processes, in Advances in Neural Information Processing Systems (NeurIPS), 2009, vol. 22.
  
  Dependent Dirichlet processes (DPs) are dependent sets of random measures, each being marginally Dirichlet process distributed. They are used in Bayesian nonparametric models when the usual exchangebility assumption does not hold. We propose a simple and general framework to construct dependent DPs by marginalizing and normalizing a single gamma process over an extended space. The result is a set of DPs, each located at a point in a space such that neighboring DPs are more dependent. We describe Markov chain Monte Carlo inference, involving the typical Gibbs sampling and three different Metropolis-Hastings proposals to speed up convergence. We report an empirical study of convergence speeds on a synthetic dataset and demonstrate an application of the model to topic modeling through time.

  @inproceedings{RaoTeh2009a,
    author = {Rao, V. and Teh, Y. W.},
    booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
    title = {Spatial Normalized Gamma Processes},
    volume = {22},
    year = {2009},
    bdsk-url-1 = {https://papers.nips.cc/paper/3630-spatial-normalized-gamma-processes},
    bdsk-url-2 = {https://papers.nips.cc/paper/3630-spatial-normalized-gamma-processes.pdf}
  }
  

• F. Wood , Y. W. Teh , A Hierarchical Nonparametric Bayesian Approach to Statistical Language Model Domain Adaptation, in Artificial Intelligence and Statistics (AISTATS), 2009.
  
  In this paper we present a doubly hierarchical Pitman-Yor process language model. Its bottom layer of hierarchy consists of multiple hierarchical Pitman-Yor process language models, one each for some number of domains. The novel top layer of hierarchy consists of a mechanism to couple together multiple language models such that they share statistical strength. Intuitively this sharing results in the ?adaptation? of a latent shared language model to each domain. We introduce a general formalism capable of describing the overall model which we call the graphical Pitman-Yor process and explain how to perform Bayesian inference in it. We present encouraging language model domain adaptation results that both illustrate the potential benefits of our new model and suggest new avenues of inquiry.

  @inproceedings{WooTeh2009a,
    author = {Wood, F. and Teh, Y. W.},
    booktitle = {Artificial Intelligence and Statistics (AISTATS)},
    title = {A Hierarchical Nonparametric {B}ayesian Approach to Statistical Language Model Domain Adaptation},
    year = {2009},
    bdsk-url-1 = {http://www.jmlr.org/proceedings/papers/v5/wood09a.html},
    bdsk-url-2 = {http://www.jmlr.org/proceedings/papers/v5/wood09a/wood09a.pdf}
  }
  

• D. M. Roy , Y. W. Teh , The Mondrian Process, in Advances in Neural Information Processing Systems (NeurIPS), 2009, vol. 21.
  
  We describe a novel stochastic process that can be used to construct a multidimensional generalization of the stick-breaking process and which is related to the classic stick breaking process described by [Sethuraman 1994] in one dimension. We describe how the process can be applied to relational data modeling using the de Finetti representation for infinitely and partially exchangeable arrays.

  @inproceedings{RoyTeh2009a,
    author = {Roy, D. M. and Teh, Y. W.},
    booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
    title = {The {M}ondrian Process},
    volume = {21},
    year = {2009},
    bdsk-url-1 = {https://papers.nips.cc/paper/3622-the-mondrian-process},
    bdsk-url-2 = {https://papers.nips.cc/paper/3622-the-mondrian-process.pdf}
  }
  

• D. Görür , Y. W. Teh , An Efficient Sequential Monte-Carlo Algorithm for Coalescent Clustering, in Advances in Neural Information Processing Systems (NeurIPS), 2009, vol. 21.
  
  We propose an efficient sequential Monte Carlo inference scheme for the recently proposed coalescent clustering model (Teh et al, 2008). Our algorithm has a quadratic runtime while those in (Teh et al, 2008) is cubic. In experiments, we were surprised to find that in addition to being more efficient, it is also a better sequential Monte Carlo sampler than the best in (Teh et al, 2008), when measured in terms of variance of estimated likelihood and effective sample size.

  @inproceedings{GorTeh2009a,
    author = {{G\"or\"ur}, D. and Teh, Y. W.},
    booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
    title = {An Efficient Sequential {M}onte-{C}arlo Algorithm for Coalescent Clustering},
    volume = {21},
    year = {2009},
    bdsk-url-1 = {https://papers.nips.cc/paper/3426-an-efficient-sequential-monte-carlo-algorithm-for-coalescent-clustering},
    bdsk-url-2 = {https://papers.nips.cc/paper/3426-an-efficient-sequential-monte-carlo-algorithm-for-coalescent-clustering.pdf}
  }
  

• Y. W. Teh , D. Görür , Indian Buffet Processes with Power-law Behavior, in Advances in Neural Information Processing Systems (NeurIPS), 2009, vol. 22.
  
  The Indian buffet process (IBP) is an exchangeable distribution over binary matrices used in Bayesian nonparametric featural models. In this paper we propose a three-parameter generalization of the IBP exhibiting power-law behavior. We achieve this by generalizing the beta process (the de Finetti measure of the IBP) to the stable-beta process and deriving the IBP corresponding to it. We find interesting relationships between the stable-beta process and the Pitman-Yor process (another stochastic process used in Bayesian nonparametric models with interesting power-law properties). We show that our power-law IBP is a good model for word occurrences in documents with improved performance over the normal IBP.

  @inproceedings{TehGor2009a,
    author = {Teh, Y. W. and {G\"or\"ur}, D.},
    booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
    title = {{I}ndian Buffet Processes with Power-law Behavior},
    volume = {22},
    year = {2009},
    bdsk-url-1 = {https://papers.nips.cc/paper/3638-indian-buffet-processes-with-power-law-behavior},
    bdsk-url-2 = {https://papers.nips.cc/paper/3638-indian-buffet-processes-with-power-law-behavior.pdf}
  }
  

• K. A. Heller , Y. W. Teh , D. Görür , Infinite Hierarchical Hidden Markov Models, in Artificial Intelligence and Statistics (AISTATS), 2009, vol. 5.
  
  In this paper we present the Infinite Hierarchical Hidden Markov Model (IHHMM), a nonparametric generalization of Hierarchical Hidden Markov Models (HHMMs). HHMMs have been used for modeling sequential data in applications such as speech recognition, detecting topic transitions in video and extracting information from text. The IHHMM provides more flexible modeling of sequential data by allowing a potentially unbounded number of levels in the hierarchy, instead of requiring the specification of a fixed hierarchy depth. Inference and learning are performed efficiently using Gibbs sampling and a modified forward-backtrack algorithm. We show encouraging demonstrations of the workings of the IHHMM.

  @inproceedings{HelTehGor2009a,
    author = {Heller, K. A. and Teh, Y. W. and {G\"or\"ur}, D.},
    booktitle = {Artificial Intelligence and Statistics (AISTATS)},
    title = {Infinite Hierarchical Hidden {M}arkov Models},
    volume = {5},
    year = {2009},
    bdsk-url-1 = {http://www.jmlr.org/proceedings/papers/v5/heller09a.html},
    bdsk-url-2 = {http://www.jmlr.org/proceedings/papers/v5/heller09a/heller09a.pdf}
  }
  

• F. Doshi , K. T. Miller , J. Van Gael , Y. W. Teh , Variational Inference for the Indian Buffet Process, in Artificial Intelligence and Statistics (AISTATS), 2009, vol. 5.
  
  The Indian Buffet Process (IBP) is a nonparametric prior for latent feature models in which observations are influenced by a combination of several hidden features. For example, images may be composed of several objects or sounds may consist of several notes. Latent feature models seek to infer what these latent features from a set of observations. Current inference methods for the IBP have all relied on sampling. While these methods are guaranteed to be accurate in the limit, in practice, samplers for the IBP tend to mix slow. We develop a deterministic variational method for the IBP. We provide theoretical guarantees on its truncation bounds and demonstrate its superior performance for high dimensional data sets.

  @inproceedings{DosMilVan2009a,
    author = {Doshi, F. and Miller, K. T. and {Van Gael}, J. and Teh, Y. W.},
    booktitle = {Artificial Intelligence and Statistics (AISTATS)},
    title = {Variational Inference for the {I}ndian Buffet Process},
    volume = {5},
    year = {2009},
    bdsk-url-1 = {http://www.jmlr.org/proceedings/papers/v5/doshi09a.html},
    bdsk-url-2 = {http://www.jmlr.org/proceedings/papers/v5/doshi09a/doshi09a.pdf}
  }
  

• J. Van Gael , Y. W. Teh , Z. Ghahramani , The Infinite Factorial Hidden Markov Model, in Advances in Neural Information Processing Systems (NeurIPS), 2009, vol. 21.
  
  We introduces a new probability distribution over a potentially infinite number of binary Markov chains which we call the Markov Indian buffet process. This process extends the IBP to allow temporal dependencies in the hidden variables. We use this stochastic process to build a nonparametric extension of the factorial hidden Markov model. After working out an inference scheme which combines slice sampling and dynamic programming we demonstrate how the infinite factorial hidden Markov model can be used for blind source separation.

  @inproceedings{VanTehGha2009a,
    author = {{Van Gael}, J. and Teh, Y. W. and Ghahramani, Z.},
    booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
    title = {The Infinite Factorial Hidden {M}arkov Model},
    volume = {21},
    year = {2009},
    bdsk-url-1 = {https://papers.nips.cc/paper/3518-the-infinite-factorial-hidden-markov-model},
    bdsk-url-2 = {https://papers.nips.cc/paper/3518-the-infinite-factorial-hidden-markov-model.pdf}
  }
  

• J. Gasthaus , F. Wood , D. Görür , Y. W. Teh , Dependent Dirichlet Process Spike Sorting, in Advances in Neural Information Processing Systems (NeurIPS), 2009, vol. 21, 497–504.
  
  In this paper we propose a new incremental spike sorting model that automatically eliminates refractory period violations, accounts for action potential waveform drift, and can handle “appearance” and “disappearance” of neurons. Our approach is to augment a known time-varying Dirichlet process that ties together a sequence of infinite Gaussian mixture models, one per action potential waveform observation, with an interspike-interval-dependent likelihood that prohibits refractory period violations. We demonstrate this model by showing results from sorting two publicly available neural data recordings for which the a partial ground truth labeling is known.

  @inproceedings{GasWooGor2009a,
    author = {Gasthaus, J. and Wood, F. and {G\"or\"ur}, D. and Teh, Y. W.},
    booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
    pages = {497-504},
    title = {Dependent {D}irichlet Process Spike Sorting},
    volume = {21},
    year = {2009},
    bdsk-url-1 = {https://papers.nips.cc/paper/3606-dependent-dirichlet-process-spike-sorting},
    bdsk-url-2 = {https://papers.nips.cc/paper/3606-dependent-dirichlet-process-spike-sorting.pdf}
  }
  

• F. Wood , C. Archambeau , J. Gasthaus , L. F. James , Y. W. Teh , A Stochastic Memoizer for Sequence Data, in International Conference on Machine Learning (ICML), 2009, vol. 26, 1129–1136.
  
  We propose an unbounded-depth, hierarchical, Bayesian nonparametric model for discrete sequence data. This model can be estimated from a single training sequence, yet shares statistical strength between subsequent symbol predictive distributions in such a way that predictive performance generalizes well. The model builds on a specific parameterization of an unbounded-depth hierarchical Pitman-Yor process. We introduce analytic marginalization steps (using coagulation operators) to reduce this model to one that can be represented in time and space linear in the length of the training sequence. We show how to perform inference in such a model without truncation approximation and introduce fragmentation operators necessary to do predictive inference. We demonstrate the sequence memoizer by using it as a language model, achieving state-of-the-art results.

  @inproceedings{WooArcGas2009a,
    author = {Wood, F. and Archambeau, C. and Gasthaus, J. and James, L. F. and Teh, Y. W.},
    booktitle = {International Conference on Machine Learning (ICML)},
    pages = {1129-1136},
    title = {A Stochastic Memoizer for Sequence Data},
    volume = {26},
    year = {2009},
    bdsk-url-1 = {http://www.stats.ox.ac.uk/\\~{}teh/research/compling/WooArcGas2009a.pdf}
  }
  

• G. R. Haffari , Y. W. Teh , Hierarchical Dirichlet Trees for Information Retrieval, in Proceedings of the Annual Meeting of the North American Association for Computational Linguistics and the Human Language Technology Conference, 2009.
  
  We propose a principled probabilisitc framework which uses trees over the vocabulary to capture similarities among terms in an information retrieval setting. This allows the retrieval of documents based not just on occurrences of specific query terms, but also on similarities between terms (an effect similar to query expansion). Additionally our principled generative model exhibits an effect similar to inverse document frequency. We give encouraging experimental evidence of the superiority of the hierarchical Dirichlet tree compared to standard baselines.

  @inproceedings{HafTeh2009a,
    author = {Haffari, G. R. and Teh, Y. W.},
    booktitle = {Proceedings of the Annual Meeting of the North American Association for Computational Linguistics and the Human Language Technology Conference},
    title = {Hierarchical {D}irichlet Trees for Information Retrieval},
    year = {2009},
    bdsk-url-1 = {http://www.stats.ox.ac.uk/\\~{}teh/research/compling/HafTeh2009a.pdf}
  }
  

• G. Quon , Y. W. Teh , E. Chan , T. Hughes , M. Brudno , Q. Morris , A Mixture Model for the Evolution of Gene Expression in Non-homogeneous Datasets, in Advances in Neural Information Processing Systems (NeurIPS), 2009, vol. 21.
  
  We address the challenge of assessing conservation of gene expression in complex, non-homogeneous datasets. Recent studies have demonstrated the success of probabilistic models in studying the evolution of gene expression in simple eukaryotic organisms such as yeast, for which measurements are typically scalar and independent. Models capable of studying expression evolution in much more complex organisms such as vertebrates are particularly important given the medical and scientific interest in species such as human and mouse. We present a statistical model that makes a number of significant extensions to previous models to enable characterization of changes in expression among highly complex organisms. We demonstrate the efficacy of our method on a microarray dataset containing diverse tissues from multiple vertebrate species. We anticipate that the model will be invaluable in the study of gene expression patterns in other diverse organisms as well, such as worms and insects.

  @inproceedings{QuoTehCha2009a,
    author = {Quon, G. and Teh, Y. W. and Chan, E. and Hughes, T. and Brudno, M. and Morris, Q.},
    booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
    title = {A Mixture Model for the Evolution of Gene Expression in Non-homogeneous Datasets},
    volume = {21},
    year = {2009},
    bdsk-url-1 = {https://papers.nips.cc/paper/3384-a-mixture-model-for-the-evolution-of-gene-expression-in-non-homogeneous-datasets},
    bdsk-url-2 = {https://papers.nips.cc/paper/3384-a-mixture-model-for-the-evolution-of-gene-expression-in-non-homogeneous-datasets.pdf}
  }
  

• A. Asuncion , M. Welling , P. Smyth , Y. W. Teh , On Smoothing and Inference for Topic Models, in Uncertainty in Artificial Intelligence (UAI), 2009.
  
  Latent Dirichlet analysis, or topic modeling, is a flexible latent variable framework for modeling high-dimensional sparse count data. Various learning algorithms have been developed in recent years, including collapsed Gibbs sampling, variational inference, and maximum a posteriori estimation, and this variety motivates the need for careful empirical comparisons. In this paper, we highlight the close connections between these approaches. We find that the main differences are attributable to the amount of smoothing applied to the counts. When the hyperparameters are optimized, the differences in performance among the algorithms diminish significantly. The ability of these algorithms to achieve solutions of comparable accuracy gives us the freedom to select computationally efficient approaches. Using the insights gained from this comparative study, we show how accurate topic models can be learned in several seconds on text corpora with thousands of documents.

  @inproceedings{AsuWelSmy2009a,
    author = {Asuncion, A. and Welling, M. and Smyth, P. and Teh, Y. W.},
    booktitle = {Uncertainty in Artificial Intelligence (UAI)},
    title = {On Smoothing and Inference for Topic Models},
    year = {2009},
    bdsk-url-1 = {https://arxiv.org/pdf/1205.2662.pdf}
  }
  

2008

• F. Caron , M. Davy , A. Doucet , E. Duflos , P. Vanheeghe , Bayesian inference for linear dynamic models with Dirichlet process mixtures, IEEE Transactions on Signal Processing, vol. 56, no. 1, 71–84, 2008.
  

  @article{Caron2008,
    title = {Bayesian inference for linear dynamic models with {D}irichlet process mixtures},
    author = {Caron, F. and Davy, M. and Doucet, A. and Duflos, E. and Vanheeghe, P.},
    journal = {IEEE Transactions on Signal Processing},
    year = {2008},
    number = {1},
    pages = {71--84},
    volume = {56},
    owner = {caron},
    publisher = {IEEE},
    timestamp = {2016.10.24}
  }
  

• S. Filippi , O. Cappe , F. Clerot , E. Moulines , A Near Optimal Policy for Channel Allocation in Cognitive Radio, in Lecture Notes in Computer Science, Recent Advances in Reinforcement Learning, Springer, 2008.
  

  @inproceedings{Filippi:2008em,
    author = {Filippi, Sarah and Cappe, Olivier and Clerot, Fabrice and Moulines, Eric},
    title = {{A Near Optimal Policy for Channel Allocation in Cognitive Radio}},
    booktitle = {Lecture Notes in Computer Science, Recent Advances in Reinforcement Learning, Springer},
    year = {2008}
  }
  

• A. Ramasamy , A. Mondry , C. C. Holmes , D. G. Altman , Key issues in conducting a meta-analysis of gene expression microarray datasets, PLoS Med, vol. 5, no. 9, e184, 2008.
  

  @article{ramasamy2008key,
    title = {Key issues in conducting a meta-analysis of gene expression microarray datasets},
    author = {Ramasamy, Adaikalavan and Mondry, Adrian and Holmes, Chris C and Altman, Douglas G},
    journal = {PLoS Med},
    volume = {5},
    number = {9},
    pages = {e184},
    year = {2008},
    publisher = {Public Library of Science}
  }
  

• T. W. Chittenden , E. A. Howe , A. C. Culhane , R. Sultana , J. M. Taylor , C. C. Holmes , J. Quackenbush , Functional classification analysis of somatically mutated genes in human breast and colorectal cancers, Genomics, vol. 91, no. 6, 508–511, 2008.
  

  @article{chittenden2008functional,
    title = {Functional classification analysis of somatically mutated genes in human breast and colorectal cancers},
    author = {Chittenden, Thomas W and Howe, Eleanor A and Culhane, Aedin C and Sultana, Razvan and Taylor, Jennifer M and Holmes, Chris C. and Quackenbush, John},
    journal = {Genomics},
    volume = {91},
    number = {6},
    pages = {508--511},
    year = {2008},
    publisher = {Elsevier}
  }
  

• A. Webb , J. M. Hancock , C. C. Holmes , Phylogenetic inference under recombination using Bayesian stochastic topology selection, Bioinformatics, vol. 25, no. 2, 197–203, 2008.
  

  @article{webb2008phylogenetic,
    title = {Phylogenetic inference under recombination using Bayesian stochastic topology selection},
    author = {Webb, Alex and Hancock, John M and Holmes, Chris C},
    journal = {Bioinformatics},
    volume = {25},
    number = {2},
    pages = {197--203},
    year = {2008},
    publisher = {Oxford University Press}
  }
  

• C. Yau , C. Holmes , CNV discovery using SNP genotyping arrays, Cytogenetic and genome research, vol. 123, no. 1-4, 307–312, 2008.
  

  @article{yau2008cnv,
    title = {CNV discovery using SNP genotyping arrays},
    author = {Yau, C and Holmes, CC},
    journal = {Cytogenetic and genome research},
    volume = {123},
    number = {1-4},
    pages = {307--312},
    year = {2008},
    publisher = {Karger Publishers}
  }
  

• E. Giannoulatou , C. Yau , S. Colella , J. Ragoussis , C. C. Holmes , GenoSNP: a variational Bayes within-sample SNP genotyping algorithm that does not require a reference population, Bioinformatics, vol. 24, no. 19, 2209–2214, 2008.
  

  @article{giannoulatou2008genosnp,
    title = {GenoSNP: a variational Bayes within-sample SNP genotyping algorithm that does not require a reference population},
    author = {Giannoulatou, Eleni and Yau, Christopher and Colella, Stefano and Ragoussis, Jiannis and Holmes, Chris C},
    journal = {Bioinformatics},
    volume = {24},
    number = {19},
    pages = {2209--2214},
    year = {2008},
    publisher = {Oxford Univ Press}
  }
  

• A. Jasra , A. Doucet , D. A. Stephens , C. C. Holmes , Interacting sequential Monte Carlo samplers for trans-dimensional simulation, Computational Statistics & Data Analysis, vol. 52, no. 4, 1765–1791, 2008.
  

  @article{jasra2008interacting,
    title = {Interacting sequential Monte Carlo samplers for trans-dimensional simulation},
    author = {Jasra, Ajay and Doucet, Arnaud and Stephens, David A and Holmes, Chris C},
    journal = {Computational Statistics \& Data Analysis},
    volume = {52},
    number = {4},
    pages = {1765--1791},
    year = {2008},
    publisher = {Elsevier}
  }
  

• G. K. Nicholls , Horses or farmers? The tower of Babel and confidence in trees, Significance, vol. 5, no. 3, 112–117, 2008.
  

  @article{Nicholls2008112,
    author = {Nicholls, G. K.},
    title = {Horses or farmers? The tower of Babel and confidence in trees},
    journal = {Significance},
    year = {2008},
    volume = {5},
    number = {3},
    pages = {112-117},
    doi = {10.1111/j.1740-9713.2008.00305.x},
    note = {cited By 1},
    document_type = {Article},
    source = {Scopus}
  }
  

• G. K. Nicholls , R. Gray , Dated ancestral trees from binary trait data and their application to the diversification of languages, Journal of the Royal Statistical Society. Series B: Statistical Methodology, vol. 70, no. 3, 545–566, 2008.
  

  @article{Nicholls2008545,
    author = {Nicholls, G. K. and Gray, R.D.},
    title = {Dated ancestral trees from binary trait data and their application to the diversification of languages},
    journal = {Journal of the Royal Statistical Society. Series B: Statistical Methodology},
    year = {2008},
    volume = {70},
    number = {3},
    pages = {545-566},
    doi = {10.1111/j.1467-9868.2007.00648.x},
    note = {cited By 21},
    document_type = {Article},
    source = {Scopus}
  }
  

• I. Albert , E. Grenier , J. Denis , J. Rousseau , Quantitative Risk Assessment from Farm to Fork and Beyond: A Global Bayesian Approach Concerning Food-Borne Diseases, Risk Analysis, vol. 28, no. 2, 557–571, 2008.
  

  @article{albert2008quantitative,
    title = {Quantitative Risk Assessment from Farm to Fork and Beyond: A Global Bayesian Approach Concerning Food-Borne Diseases},
    author = {Albert, Isabelle and Grenier, Emmanuel and Denis, Jean-Baptiste and Rousseau, Judith},
    journal = {Risk Analysis},
    volume = {28},
    number = {2},
    pages = {557--571},
    year = {2008},
    publisher = {Wiley Online Library}
  }
  

• D. Fraser , J. Rousseau , Studentization and deriving accurate p-values, Biometrika, vol. 95, no. 1, 1–16, 2008.
  

  @article{fraser2008studentization,
    title = {Studentization and deriving accurate p-values},
    author = {Fraser, DAS and Rousseau, Judith},
    journal = {Biometrika},
    volume = {95},
    number = {1},
    pages = {1--16},
    year = {2008},
    publisher = {Oxford University Press}
  }
  

• A. Chambaz , J. Rousseau , Bounds for Bayesian order identification with application to mixtures, The Annals of Statistics, 938–962, 2008.
  

  @article{chambaz2008bounds,
    title = {Bounds for Bayesian order identification with application to mixtures},
    author = {Chambaz, Antoine and Rousseau, Judith},
    journal = {The Annals of Statistics},
    pages = {938--962},
    year = {2008},
    publisher = {JSTOR}
  }
  

• S. J. Low Choy , K. L. Mengersen , J. Rousseau , Encoding expert opinion on skewed non-negative distributions, Journal of Applied Probability and Statistics, vol. 3, no. 1, 1–21, 2008.
  

  @article{low2008encoding,
    title = {Encoding expert opinion on skewed non-negative distributions},
    author = {Low Choy, Samantha J and Mengersen, Kerrie L and Rousseau, Judith},
    journal = {Journal of Applied Probability and Statistics},
    volume = {3},
    number = {1},
    pages = {1--21},
    year = {2008},
    publisher = {Dixie W Publishing Corporation}
  }
  

• D. Vukobratovic , V. Stankovic , D. Sejdinovic , L. Stankovic , Z. Xiong , Expanding window fountain codes for scalable video multicast, in IEEE International Conference on Multimedia and Expo (ICME), 2008, 77–80.
  
  Digital Fountain (DF) codes have recently been suggested as an efficient forward error correction (FEC) solution for video multicast to heterogeneous receiver classes over lossy packet networks. However, to adapt DF codes to low-delay constraints and varying importance of scalable multimedia content, unequal error protection (UEP) DF schemes are needed. Thus, in this paper, Expanding Window Fountain (EWF) codes are proposed as a FEC solution for scalable video multicast. We demonstrate that the design flexibility and UEP performance make EWF codes ideally suited for this scenario, i.e., EWF codes offer a number of design parameters to be ldquotunedrdquo at the server side to meet the different reception conditions of heterogeneous receivers. Performance analysis of H.264 Scalable Video Coding (SVC) multicast to heterogeneous receiver classes confirms the flexibility and efficiency of the proposed EWF-based FEC solution.

  @inproceedings{VukStaSejStaXio2008,
    author = {Vukobratovic, D. and Stankovic, V. and Sejdinovic, D. and Stankovic, L. and Xiong, Z.},
    booktitle = {IEEE International Conference on Multimedia and Expo (ICME)},
    doi = {10.1109/ICME.2008.4607375},
    pages = {77--80},
    title = {Expanding window fountain codes for scalable video multicast},
    year = {2008},
    bdsk-url-1 = {http://dx.doi.org/10.1109/ICME.2008.4607375}
  }
  

• D. Sejdinovic , R. Piechocki , A. Doufexi , M. Ismail , Fountain coding with decoder side information, in IEEE International Conference on Communications (ICC), 2008, 4477–4482.
  
  In this contribution, we consider the application of digital fountain (DF) codes to the problem of data transmission when side information is available at the decoder. The side information is modelled as a "virtual" channel output when original information sequence is the input. For two cases of the system model, which model both the virtual and the actual transmission channel either as a binary erasure channel or as a binary input additive white Gaussian noise (BIAWGN) channel, we propose methods of enhancing the design of standard non-systematic DF codes by optimizing their output degree distribution based on the side information assumption. In addition, a systematic Raptor design has been employed as a possible solution to the problem.

  @inproceedings{SejPieDouIsm2008ICC,
    author = {Sejdinovic, D. and Piechocki, R.J. and Doufexi, A. and Ismail, M.},
    booktitle = {IEEE International Conference on Communications (ICC)},
    doi = {10.1109/ICC.2008.840},
    pages = {4477--4482},
    title = {Fountain coding with decoder side information},
    year = {2008},
    bdsk-url-1 = {http://dx.doi.org/10.1109/ICC.2008.840}
  }
  

• D. Sejdinovic , V. Ponnampalam , R. Piechocki , A. Doufexi , The throughput analysis of different IR-HARQ schemes based on fountain codes, in IEEE Wireless Communications and Networking Conference (WCNC), 2008, 267–272.
  
  In this contribution, we construct two novel IR-HARQ (automatic repeat request) schemes based on fountain codes, which combine the punctured and rateless IR-HARQ schemes, in order to attain the advantageous properties of both: nearly optimal performance of the former at the high signal-to-noise ratio (SNR) region and ratelessness of the latter. The preliminary simulation results indicate that these schemes are particularly suitable for scenarios where the transmission is originally assumed to occur at the very high SNR region, but resilience to severe deterioration of channel conditions is required.

  @inproceedings{SejPonPieDou2008,
    author = {Sejdinovic, D. and Ponnampalam, V. and Piechocki, R.J. and Doufexi, A.},
    booktitle = {IEEE Wireless Communications and Networking Conference (WCNC)},
    doi = {10.1109/WCNC.2008.52},
    file = {pdf/2008WCNC.pdf},
    pages = {267--272},
    title = {The throughput analysis of different IR-HARQ schemes based on fountain codes},
    year = {2008},
    bdsk-url-1 = {http://dx.doi.org/10.1109/WCNC.2008.52}
  }
  

• D. Sejdinovic , R. Piechocki , A. Doufexi , M. Ismail , Rate adaptive binary erasure quantization with dual fountain codes, in IEEE Global Telecommunications Conference (GLOBECOM), 2008.
  
  In this contribution, duals of fountain codes are introduced and their use for lossy source compression is investigated. It is shown both theoretically and experimentally that the source coding dual of the binary erasure channel coding problem, binary erasure quantization, is solved at a nearly optimal rate with application of duals of LT and raptor codes by a belief propagation-like algorithm which amounts to a graph pruning procedure. Furthermore, this quantizing scheme is rate adaptive, i.e., its rate can be modified on-the-fly in order to adapt to the source distribution, very much like LT and raptor codes are able to adapt their rate to the erasure probability of a channel.

  @inproceedings{SejPieDouIsm2008,
    author = {Sejdinovic, D. and Piechocki, R.J. and Doufexi, A. and Ismail, M.},
    booktitle = {IEEE Global Telecommunications Conference (GLOBECOM)},
    doi = {10.1109/GLOCOM.2008.ECP.238},
    file = {pdf/2008Globecom.pdf},
    title = {Rate adaptive binary erasure quantization with dual fountain codes},
    year = {2008},
    bdsk-url-1 = {http://dx.doi.org/10.1109/GLOCOM.2008.ECP.238}
  }
  

• H. L. Chieu , W. S. Lee , Y. W. Teh , Cooled and Relaxed Survey Propagation for MRFs, in Advances in Neural Information Processing Systems (NeurIPS), 2008, vol. 20.
  
  We describe a new algorithm, Relaxed Survey Propagation (RSP), for finding MAP configurations in Markov random fields. We compare its performance with state-of-the-art algorithms including the max-product belief propagation, its sequential tree-reweighted variant, residual (sum-product) belief propagation, and tree-structured expectation propagation. We show that it outperforms all approaches for Ising models with mixed couplings, as well as on a web person disambiguation task formulated as a supervised clustering problem.

  @inproceedings{ChiLeeTeh2008a,
    author = {Chieu, H. L. and Lee, W. S. and Teh, Y. W.},
    booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
    title = {Cooled and Relaxed Survey Propagation for {MRFs}},
    volume = {20},
    year = {2008},
    bdsk-url-1 = {https://papers.nips.cc/paper/3308-cooled-and-relaxed-survey-propagation-for-mrfs},
    bdsk-url-2 = {https://papers.nips.cc/paper/3308-cooled-and-relaxed-survey-propagation-for-mrfs.pdf}
  }
  

• Y. W. Teh , H. Daume III , D. M. Roy , Bayesian Agglomerative Clustering with Coalescents, in Advances in Neural Information Processing Systems (NeurIPS), 2008, vol. 20.
  
  We introduce a new Bayesian model for hierarchical clustering based on a prior over trees called Kingman’s coalescent. We develop novel greedy and sequential Monte Carlo inferences which operate in a bottom-up agglomerative fashion. We show experimentally the superiority of our algorithms over the state-of-the-art, and demonstrate our approach in document clustering and phylolinguistics.

  @inproceedings{TehDauRoy2008a,
    author = {Teh, Y. W. and {Daume III}, H. and Roy, D. M.},
    booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
    title = {{B}ayesian Agglomerative Clustering with Coalescents},
    volume = {20},
    year = {2008},
    bdsk-url-1 = {http://papers.nips.cc/paper/3266-bayesian-agglomerative-clustering-with-coalescents},
    bdsk-url-2 = {http://papers.nips.cc/paper/3266-bayesian-agglomerative-clustering-with-coalescents.pdf}
  }
  

• J. Van Gael , Y. Saatci , Y. W. Teh , Z. Ghahramani , Beam Sampling for the Infinite Hidden Markov Model, in International Conference on Machine Learning (ICML), 2008, vol. 25.
  
  The infinite hidden Markov model is a nonparametric extension of the widely used hidden Markov model. Our paper introduces a new inference algorithm for the infinite Hidden Markov model called beam sampling. Beam sampling combines slice sampling, which limits the number of states considered at each time step to a finite number, with dynamic programming, which samples whole state trajectories efficiently. Our algorithm typically outperforms the Gibbs sampler and is more robust. We present applications of iHMM inference using the beam sampler on changepoint detection and text prediction problems.

  @inproceedings{VanSaaTeh2008a,
    author = {{Van Gael}, J. and Saatci, Y. and Teh, Y. W. and Ghahramani, Z.},
    booktitle = {International Conference on Machine Learning (ICML)},
    title = {Beam Sampling for the Infinite Hidden {M}arkov Model},
    volume = {25},
    year = {2008},
    bdsk-url-1 = {http://www.stats.ox.ac.uk/\\~{}teh/research/npbayes/VanSaaTeh2008a.pdf}
  }
  

• M. Welling , Y. W. Teh , H. J. Kappen , Hybrid Variational/Gibbs Collapsed Inference in Topic Models, in Uncertainty in Artificial Intelligence (UAI), 2008, vol. 24.
  
  Variational Bayesian inference and (collapsed) Gibbs sampling are the two important classes of inference algorithms for Bayesian networks. Both have their advantages and disadvantages: collapsed Gibbs sampling is unbiased but is also inefficient for large count values and requires averaging over many samples to reduce variance. On the other hand, variational Bayesian inference is efficient and accurate for large count values but suffers from bias for small counts. We propose a hybrid algorithm that combines the best of both worlds: it samples very small counts and applies variational updates to large counts. This hybridization is shown to significantly improve testset perplexity relative to variational inference at no computational cost.

  @inproceedings{WelTehKap2008a,
    author = {Welling, M. and Teh, Y. W. and Kappen, H. J.},
    booktitle = {Uncertainty in Artificial Intelligence (UAI)},
    title = {Hybrid {Variational/Gibbs} Collapsed Inference in Topic Models},
    volume = {24},
    year = {2008},
    bdsk-url-1 = {https://arxiv.org/pdf/1206.3297v1.pdf}
  }
  

• Y. W. Teh , K. Kurihara , M. Welling , Collapsed Variational Inference for HDP, in Advances in Neural Information Processing Systems (NeurIPS), 2008, vol. 20.
  
  A wide variety of Dirichlet-multinomial ‘topic’ models have found interesting applications in recent years. While Gibbs sampling remains an important method of inference in such models, variational techniques have certain advantages such as easy assessment of convergence, easy optimization without the need to maintain detailed balance, a bound on the marginal likelihood, and side-stepping of issues with topic-identifiability. The most accurate variational technique thus far, namely collapsed variational latent Dirichlet allocation, did not deal with model selection nor did it include inference for hyperparameters. We address both issues by generalizing the technique, obtaining the first variational algorithm to deal with the hierarchical Dirichlet process and to deal with hyperparameters of Dirichlet variables. Experiments show a significant improvement in accuracy.

  @inproceedings{TehKurWel2008a,
    author = {Teh, Y. W. and Kurihara, K. and Welling, M.},
    booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
    title = {Collapsed Variational Inference for {HDP}},
    volume = {20},
    year = {2008},
    bdsk-url-1 = {https://papers.nips.cc/paper/3342-collapsed-variational-inference-for-hdp},
    bdsk-url-2 = {https://papers.nips.cc/paper/3342-collapsed-variational-inference-for-hdp.pdf}
  }
  

2007

• F. Caron , M. Davy , A. Doucet , Generalized Polya urn for time-varying Dirichlet process mixtures, in Uncertainty in Artificial Intelligence (UAI), 2007.
  

  @inproceedings{Caron2007,
    title = {{Generalized Polya urn for time-varying Dirichlet process mixtures}},
    author = {Caron, F. and Davy, M. and Doucet, A.},
    booktitle = {Uncertainty in Artificial Intelligence (UAI)},
    year = {2007},
    owner = {caron},
    timestamp = {2016.10.24}
  }
  

• P. Dellaportas , D. G. Denison , C. C. Holmes , Flexible threshold models for modelling interest rate volatility, Econometric reviews, vol. 26, no. 2-4, 419–437, 2007.
  

  @article{dellaportas2007flexible,
    title = {Flexible threshold models for modelling interest rate volatility},
    author = {Dellaportas, Petros and Denison, David GT and Holmes, Chris C.},
    journal = {Econometric reviews},
    volume = {26},
    number = {2-4},
    pages = {419--437},
    year = {2007},
    publisher = {Taylor \& Francis}
  }
  

• C. C. Holmes , A. Pintore , BAYESIAN STATISTICS 8, pp. 253-282. JM Bernardo, MJ Bayarri, JO Berger, AP Dawid, D. Heckerman, AFM Smith and M. West (Eds.)\copyright Oxford University Press, 2007, in Bayesian statistics 8: proceedings of the eighth Valencia International Meeting, June 2-6, 2006, 2007, vol. 8, 253.
  

  @inproceedings{holmes2007bayesian,
    title = {BAYESIAN STATISTICS 8, pp. 253-282. JM Bernardo, MJ Bayarri, JO Berger, AP Dawid, D. Heckerman, AFM Smith and M. West (Eds.){\copyright} Oxford University Press, 2007},
    author = {Holmes, Chris C and Pintore, Alexandre},
    booktitle = {Bayesian statistics 8: proceedings of the eighth Valencia International Meeting, June 2-6, 2006},
    volume = {8},
    pages = {253},
    year = {2007},
    organization = {Oxford University Press, USA}
  }
  

• L. Astle , C. Holmes , D. Balding , Turbo Genomic Control, 2007.
  

  @inproceedings{astle2007turbo,
    title = {Turbo Genomic Control},
    author = {Astle, LJ and Holmes, CC and Balding, DJ},
    year = {2007},
    organization = {WILEY-LISS}
  }
  

• S. Colella , C. Yau , J. M. Taylor , G. Mirza , H. Butler , P. Clouston , A. S. Bassett , A. Seller , C. C. Holmes , J. Ragoussis , QuantiSNP: an Objective Bayes Hidden-Markov Model to detect and accurately map copy number variation using SNP genotyping data, Nucleic Acids Research, vol. 35, no. 6, 2013–2025, 2007.
  

  @article{colella2007quantisnp,
    title = {QuantiSNP: an Objective Bayes Hidden-Markov Model to detect and accurately map copy number variation using SNP genotyping data},
    author = {Colella, Stefano and Yau, Christopher and Taylor, Jennifer M and Mirza, Ghazala and Butler, Helen and Clouston, Penny and Bassett, Anne S and Seller, Anneke and Holmes, Chris C and Ragoussis, Jiannis},
    journal = {Nucleic Acids Research},
    volume = {35},
    number = {6},
    pages = {2013--2025},
    year = {2007},
    publisher = {Oxford Univ Press}
  }
  

• A. Jasra , D. A. Stephens , C. C. Holmes , On population-based simulation for static inference, Statistics and Computing, vol. 17, no. 3, 263–279, 2007.
  

  @article{jasra2007population,
    title = {On population-based simulation for static inference},
    author = {Jasra, Ajay and Stephens, David A and Holmes, Chris C},
    journal = {Statistics and Computing},
    volume = {17},
    number = {3},
    pages = {263--279},
    year = {2007},
    publisher = {Springer}
  }
  

• A. Jasra , D. A. Stephens , C. C. Holmes , Population-based reversible jump Markov chain Monte Carlo, Biometrika, 787–807, 2007.
  

  @article{jasra2007populatioo,
    title = {Population-based reversible jump Markov chain Monte Carlo},
    author = {Jasra, Ajay and Stephens, David A and Holmes, Chris C},
    journal = {Biometrika},
    pages = {787--807},
    year = {2007},
    publisher = {Biometrika Trust, University College London}
  }
  

• K. Gallagher , J. Stephenson , R. Brown , C. Holmes , Integrating 3D information from thermochronological data over unknown spatial scales, in Geophysical Research Abstracts, 2007, vol. 9, 09015.
  

  @inproceedings{gallagher2007integrating,
    title = {Integrating 3D information from thermochronological data over unknown spatial scales},
    author = {Gallagher, K and Stephenson, J and Brown, R and Holmes, C},
    booktitle = {Geophysical Research Abstracts},
    volume = {9},
    pages = {09015},
    year = {2007}
  }
  

• J. Griffin , C. Holmes , Bayesian nonparametric calibration with applications in spatial epidemiology, Technical Report, Institute of Mathematics, Statistics and Actuarial Science, University of Kent, 2007.
  

  @techreport{griffin2007bayesian,
    title = {Bayesian nonparametric calibration with applications in spatial epidemiology},
    author = {Griffin, JE and Holmes, CC},
    year = {2007},
    institution = {Technical Report, Institute of Mathematics, Statistics and Actuarial Science, University of Kent}
  }
  

• M. Zucknick , C. C. Holmes , S. Richardson , Mcmc Methods for Bayesian Variable Selection in Large-scale Genomic Applications, Annals of Human Genetics, vol. 71, no. 4, 558–559, 2007.
  

  @article{zucknick2007mcmc,
    title = {Mcmc Methods for Bayesian Variable Selection in Large-scale Genomic Applications},
    author = {Zucknick, Manuela and Holmes, Chris C. and Richardson, Sylvia},
    journal = {Annals of Human Genetics},
    volume = {71},
    number = {4},
    pages = {558--559},
    year = {2007}
  }
  

• J. Rousseau , Approximating interval hypothesis: p-values and Bayes factors, Bayesian statistics, vol. 8, 417–452, 2007.
  

  @article{rousseau2007approximating,
    title = {Approximating interval hypothesis: p-values and Bayes factors},
    author = {Rousseau, Judith},
    journal = {Bayesian statistics},
    volume = {8},
    pages = {417--452},
    year = {2007},
    publisher = {Oxford University Press Oxford}
  }
  

• D. Vukobratovic , V. Stankovic , D. Sejdinovic , L. Stankovic , Z. Xiong , Scalable data multicast using expanding window fountain codes, in 45th Annual Allerton Conference on Communication, Control, and Computing, 2007.
  
  Digital Fountain (DF) codes were introduced as an efficient and universal Forward Error Correction (FEC) solution for data multicast over lossy packet networks. However, in real-time applications, the DF encoder cannot make use of the “rateless” property as it was proposed in the DF framework, due to its delay constraints. In this scenario, many receivers might not be able to collect enough encoded symbols (packets) to perform succesful decoding of the source data block (e.g., they are connected as a low bit-rate receivers to a high bit-rate source stream, or they are affected by severe channel conditions). This paper proposes an application of recently introduced Expanding Window Fountain (EWF) codes as a scalable and efficient solution for real-time multicast data transmission. We show that, by carefully optimizing EWF code design parameters, it is possible to design a flexible DF solution that is capable of satisfying multicast data receivers over a wide range of data rates and/or erasure channel conditions.

  @inproceedings{VukStaSejStaXio2007,
    author = {Vukobratovic, D. and Stankovic, V. and Sejdinovic, D. and Stankovic, L. and Xiong, Z.},
    booktitle = {45th Annual Allerton Conference on Communication, Control, and Computing},
    file = {pdf/2007Allerton.pdf},
    title = {Scalable data multicast using expanding window fountain codes},
    year = {2007}
  }
  

• D. Sejdinovic , D. Vukobratovic , A. Doufexi , V. Senk , R. Piechocki , Expanding window fountain codes for unequal error protection, in Asilomar Conference on Signals, Systems and Computers, 2007, 1020–1024.
  
  A novel approach to provide unequal error protection (UEP) using rateless codes over erasure channels, named Expanding Window Fountain (EWF) codes, is developed and discussed. EWF codes use a windowing technique rather than a weighted (non-uniform) selection of input symbols to achieve UEP property. The windowing approach introduces additional parameters in the UEP rateless code design, making it more general and flexible than the weighted approach. Furthermore, the windowing approach provides better performance of UEP scheme, which is confirmed both theoretically and experimentally.

  @inproceedings{SejVukDouSenPie2007,
    author = {Sejdinovic, D. and Vukobratovic, D. and Doufexi, A. and Senk, V. and Piechocki, R.},
    booktitle = {Asilomar Conference on Signals, Systems and Computers},
    doi = {10.1109/ACSSC.2007.4487375},
    file = {pdf/2007Asilomar.pdf},
    pages = {1020--1024},
    title = {Expanding window fountain codes for unequal error protection},
    year = {2007},
    bdsk-url-1 = {http://dx.doi.org/10.1109/ACSSC.2007.4487375}
  }
  

• K. Kurihara , M. Welling , Y. W. Teh , Collapsed Variational Dirichlet Process Mixture Models, in Proceedings of the International Joint Conference on Artificial Intelligence, 2007, vol. 20.
  
  Nonparametric Bayesian mixture models, in particular Dirichlet process (DP) mixture models, have shown great promise for density estimation and data clustering. Given the size of today’s datasets, computational efficiency becomes an essential ingredient in the applicability of these techniques to real world data. We study and experimentally compare a number of variational Bayesian (VB) approximations to the DP mixture model. In particular we consider the standard VB approximation where parameters are assumed to be independent from cluster assignment variables, and a novel collapsed VB approximation where mixture weights are marginalized out. For both VB approximations we consider two different ways to approximate the DP, by truncating the stick-breaking construction, and by using a finite mixture model with a symmetric Dirichlet prior.

  @inproceedings{KurWelTeh2007a,
    author = {Kurihara, K. and Welling, M. and Teh, Y. W.},
    booktitle = {Proceedings of the International Joint Conference on Artificial Intelligence},
    title = {Collapsed Variational {D}irichlet Process Mixture Models},
    volume = {20},
    year = {2007},
    bdsk-url-1 = {http://www.stats.ox.ac.uk/\\~{}teh/research/papers/KurWelTeh2007a.pdf}
  }
  

• Y. W. Teh , D. Görür , Z. Ghahramani , Stick-breaking Construction for the Indian Buffet Process, in Artificial Intelligence and Statistics (AISTATS), 2007, vol. 11.
  
  The Indian buffet process (IBP) is a Bayesian nonparametric distribution whereby objects are modelled using an unbounded number of latent features. In this paper we derive a stick-breaking representation for the IBP. Based on this new representation, we develop slice samplers for the IBP that are efficient, easy to implement and are more generally applicable than the currently available Gibbs sampler. This representation, along with the work of Thibaux and Jordan, also illuminates interesting theoretical connections between the IBP, Chinese restaurant processes, Beta processes and Dirichlet processes.

  @inproceedings{TehGorGha2007a,
    author = {Teh, Y. W. and {G\"or\"ur}, D. and Ghahramani, Z.},
    booktitle = {Artificial Intelligence and Statistics (AISTATS)},
    title = {Stick-breaking Construction for the {I}ndian Buffet Process},
    volume = {11},
    year = {2007},
    bdsk-url-1 = {http://www.stats.ox.ac.uk/\\~{}teh/research/npbayes/aistats2007.pdf}
  }
  

• J. F. Cai , W. S. Lee , Y. W. Teh , NUS-ML: Improving Word Sense Disambiguation Using Topic Features, in Proceedings of the International Workshop on Semantic Evaluations, 2007, vol. 4.
  
  We participated in SemEval-1 English coarse-grained all-words task (task 7), English fine-grained all-words task (task 17, subtask 3) and English coarse-grained lexical sample task (task 17, subtask 1). The same method with different labeled data is used for the tasks; SemCor is the labeled corpus used to train our system for the all words tasks while the labeled corpus that is provided is used for the lexical sample task. The knowledge sources include part-of-speech of neighboring words, single words in the surrounding context, local collocations, and syntactic patterns. In addition, we constructed a topic feature, targeted to capture the global context information, using the latent dirichlet allocation (LDA) algorithm with unlabeled corpus. A modified naive Bayes classifier is constructed to incorporate all the features. We achieved 81.6%, 57.6%, 88.7% for coarse-grained all words task, fine-grained all-words task and coarse-grained lexical sample task respectively.

  @inproceedings{CaiLeeTeh2007a,
    author = {Cai, J. F. and Lee, W. S. and Teh, Y. W.},
    booktitle = {Proceedings of the International Workshop on Semantic Evaluations},
    title = {{NUS-ML}: Improving Word Sense Disambiguation Using Topic Features},
    volume = {4},
    year = {2007},
    bdsk-url-1 = {http://www.stats.ox.ac.uk/\\~{}teh/research/compling/semeval2007.pdf}
  }
  

• Y. J. Lim , Y. W. Teh , Variational Bayesian Approach to Movie Rating Prediction, in Proceedings of KDD Cup and Workshop, 2007.
  
  Singular value decomposition (SVD) is a matrix decomposition algorithm that returns the optimal (in the sense of squared error) low-rank decomposition of a matrix. SVD has found widespread use across a variety of machine learning applications, where its output is interpreted as compact and informative representations of data. The Netflix Prize challenge, and collaborative filtering in general, is an ideal application for SVD, since the data is a matrix of ratings given by users to movies. It is thus not surprising to observe that most currently successful teams use SVD, either with an extension, or to interpolate with results returned by other algorithms. Unfortunately SVD can easily overfit due to the extreme data sparsity of the matrix in the Netflix Prize challenge, and care must be taken to regularize properly. In this paper, we propose a Bayesian approach to alleviate overfitting in SVD, where priors are introduced and all parameters are integrated out using variational inference. We show experimentally that this gives significantly improved results over vanilla SVD. For truncated SVDs of rank 5, 10, 20, and 30, our proposed Bayesian approach achieves 2.2% improvement over a na¨ıve approach, 1.6% improvement over a gradient descent approach dealing with unobserved entries properly, and 0.9% improvement over a maximum a posteriori (MAP) approach.

  @inproceedings{LimTeh2007a,
    author = {Lim, Y. J. and Teh, Y. W.},
    booktitle = {Proceedings of KDD Cup and Workshop},
    title = {Variational {B}ayesian Approach to Movie Rating Prediction},
    year = {2007},
    bdsk-url-1 = {http://www.stats.ox.ac.uk/\\~{}teh/research/bayesml/kddcup2007.pdf}
  }
  

• J. F. Cai , W. S. Lee , Y. W. Teh , Improving Word Sense Disambiguation Using Topic Features, in Proceedings of the Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-coNLL), 2007.
  
  This paper presents a novel approach for exploiting the global context for the task of word sense disambiguation (WSD). This is done by using topic features constructed using the latent dirichlet allocation (LDA) algorithm on unlabeled data. The features are incorporated into a modified naive Bayes network alongside other features such as part-of-speech of neighboring words, single words in the surrounding context, local collocations, and syntactic patterns. In both the English all-words task and the English lexical sample task, the method achieved significant improvement over the simple naive Bayes classifier and higher accuracy than the best official scores on Senseval-3 for both task.

  @inproceedings{CaiLeeTeh2007b,
    author = {Cai, J. F. and Lee, W. S. and Teh, Y. W.},
    booktitle = {Proceedings of the Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-coNLL)},
    title = {Improving Word Sense Disambiguation Using Topic Features},
    year = {2007},
    bdsk-url-1 = {http://www.aclweb.org/anthology/D/D07/D07-1108},
    bdsk-url-2 = {http://www.aclweb.org/anthology/D/D07/D07-1108.pdf}
  }
  

• Y. W. Teh , D. Newman , M. Welling , A Collapsed Variational Bayesian Inference Algorithm for Latent Dirichlet Allocation, in Advances in Neural Information Processing Systems (NeurIPS), 2007, vol. 19, 1353–1360.
  
  Latent Dirichlet allocation (LDA) is a Bayesian network that has recently gained much popularity in applications ranging from document modeling to computer vision. Due to the large scale nature of these applications, current inference procedures like variational Bayes and Gibbs sampling have been found lacking. In this paper we propose the collapsed variational Bayesian inference algorithm for LDA, and show that it is computationally efficient, easy to implement and significantly more accurate than standard variational Bayesian inference for LDA.

  @inproceedings{TehNewWel2007a,
    author = {Teh, Y. W. and Newman, D. and Welling, M.},
    booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
    pages = {1353-1360},
    title = {A Collapsed Variational {B}ayesian Inference Algorithm for Latent {D}irichlet Allocation},
    volume = {19},
    year = {2007},
    bdsk-url-1 = {http://papers.nips.cc/paper/3113-a-collapsed-variational-bayesian-inference-algorithm-for-latent-dirichlet-allocation},
    bdsk-url-2 = {http://papers.nips.cc/paper/3113-a-collapsed-variational-bayesian-inference-algorithm-for-latent-dirichlet-allocation.pdf}
  }
  

2006

• C. C. Holmes , L. Held , . others , Bayesian auxiliary variable models for binary and multinomial regression, Bayesian analysis, vol. 1, no. 1, 145–168, 2006.
  

  @article{holmes2006bayesian,
    title = {Bayesian auxiliary variable models for binary and multinomial regression},
    author = {Holmes, Chris C and Held, Leonhard and others},
    journal = {Bayesian analysis},
    volume = {1},
    number = {1},
    pages = {145--168},
    year = {2006},
    publisher = {International Society for Bayesian Analysis}
  }
  

• A. Pintore , P. Speckman , C. C. Holmes , Spatially adaptive smoothing splines, Biometrika, 113–125, 2006.
  

  @article{pintore2006spatially,
    title = {Spatially adaptive smoothing splines},
    author = {Pintore, Alexandre and Speckman, Paul and Holmes, Chris C},
    journal = {Biometrika},
    pages = {113--125},
    year = {2006},
    publisher = {Biometrika Trust, University College London}
  }
  

• J. Stephenson , K. Gallagher , C. C. Holmes , A Bayesian approach to calibrating apatite fission track annealing models for laboratory and geological timescales, Geochimica et Cosmochimica Acta, vol. 70, no. 20, 5183–5200, 2006.
  

  @article{stephenson2006bayesian,
    title = {A Bayesian approach to calibrating apatite fission track annealing models for laboratory and geological timescales},
    author = {Stephenson, John and Gallagher, Kerry and Holmes, Chris C.},
    journal = {Geochimica et Cosmochimica Acta},
    volume = {70},
    number = {20},
    pages = {5183--5200},
    year = {2006},
    publisher = {Pergamon}
  }
  

• H. De Wet , M. Allen , C. C. Holmes , M. Stobbart , J. D. Lippiat , H. De Wet , M. Allen , C. C. Holmes , M. Stobbart , J. D. Lippiat , . others , Modulation of the BK channel by estrogens: examination at single channel level, Molecular membrane biology, vol. 23, no. 5, 420–429, 2006.
  

  @article{de2006modulation,
    title = {Modulation of the BK channel by estrogens: examination at single channel level},
    author = {De Wet, Heidi and Allen, Marcus and Holmes, Chris C. and Stobbart, Michelle and Lippiat, Jonathan D and De Wet, Heidi and Allen, Marcus and Holmes, Chris C. and Stobbart, Michelle and Lippiat, Jonathan D and others},
    journal = {Molecular membrane biology},
    volume = {23},
    number = {5},
    pages = {420--429},
    year = {2006},
    publisher = {Taylor \& Francis}
  }
  

• V. Baladandayuthapani , C. C. Holmes , B. Mallick , R. Carroll , Modeling nonlinear gene interactions using Bayesian MARS. Bayesian Inference for Gene Expression and Proteomics. Cambridge University Press, 2006.
  

  @misc{baladandayuthapani2006modeling,
    title = {Modeling nonlinear gene interactions using Bayesian MARS},
    author = {Baladandayuthapani, Veerabhadran and Holmes, Chris C and Mallick, BK and Carroll, RJ},
    year = {2006},
    publisher = {Bayesian Inference for Gene Expression and Proteomics. Cambridge University Press}
  }
  

• K. Gallagher , A. Jasra , D. Stephens , C. C. Holmes , A new approach to mixture modelling for geochronology, Geochimica et Cosmochimica Acta, vol. 70, no. 18, A190, 2006.
  

  @article{gallagher2006new,
    title = {A new approach to mixture modelling for geochronology},
    author = {Gallagher, Kerry and Jasra, Ajay and Stephens, David and Holmes, Chris C.},
    journal = {Geochimica et Cosmochimica Acta},
    volume = {70},
    number = {18},
    pages = {A190},
    year = {2006},
    publisher = {Pergamon}
  }
  

• A. Jasra , D. A. Stephens , K. Gallagher , C. C. Holmes , Bayesian mixture modelling in geochronology via Markov chain Monte Carlo, Mathematical geology, vol. 38, no. 3, 269–300, 2006.
  

  @article{jasra2006bayesian,
    title = {Bayesian mixture modelling in geochronology via Markov chain Monte Carlo},
    author = {Jasra, Ajay and Stephens, David A and Gallagher, Kerry and Holmes, Chris C},
    journal = {Mathematical geology},
    volume = {38},
    number = {3},
    pages = {269--300},
    year = {2006},
    publisher = {Springer}
  }
  

• A. Jasra , D. Stephens , K. Gallagher , C. Holmes , Analysis of geochronological data with measurement error using Bayesian mixtures, Mathematical Geology, vol. 38, 269–300, 2006.
  

  @article{jasra2006analysis,
    title = {Analysis of geochronological data with measurement error using Bayesian mixtures},
    author = {Jasra, A and Stephens, DA and Gallagher, K and Holmes, CC},
    journal = {Mathematical Geology},
    volume = {38},
    pages = {269--300},
    year = {2006}
  }
  

• N. A. Heard , C. C. Holmes , D. A. Stephens , A quantitative study of gene regulation involved in the immune response of anopheline mosquitoes, Journal of the American Statistical Association, vol. 101, no. 473, 18–29, 2006.
  

  @article{heard2006quantitative,
    title = {A quantitative study of gene regulation involved in the immune response of anopheline mosquitoes},
    author = {Heard, Nicholas A and Holmes, Chris C and Stephens, David A},
    journal = {Journal of the American Statistical Association},
    volume = {101},
    number = {473},
    pages = {18--29},
    year = {2006},
    publisher = {ASA}
  }
  

• K. Gallagher , J. Stephenson , C. Holmes , R. Brown , Putting the data to work—strategies for modelling multiple samples in multiple dimensions, Geochimica et Cosmochimica Acta, vol. 70, no. 18, A190, 2006.
  

  @article{gallagher2006putting,
    title = {Putting the data to work—strategies for modelling multiple samples in multiple dimensions},
    author = {Gallagher, K and Stephenson, J and Holmes, C and Brown, R},
    journal = {Geochimica et Cosmochimica Acta},
    volume = {70},
    number = {18},
    pages = {A190},
    year = {2006},
    publisher = {Pergamon}
  }
  

• J. Stephenson , K. Gallagher , C. Holmes , Low temperature thermochronology and strategies for multiple samples: 2: Partition modelling for 2d/3d distributions with discontinuities, Earth and Planetary Science Letters, vol. 241, no. 3, 557–570, 2006.
  

  @article{stephenson2006low,
    title = {Low temperature thermochronology and strategies for multiple samples: 2: Partition modelling for 2d/3d distributions with discontinuities},
    author = {Stephenson, John and Gallagher, K and Holmes, CC},
    journal = {Earth and Planetary Science Letters},
    volume = {241},
    number = {3},
    pages = {557--570},
    year = {2006},
    publisher = {Elsevier}
  }
  

• V. Baladandayuthapani , C. C. Holmes , B. K. Mallick , R. J. Carroll , Bayesian Inference for Gene Expression and Proteomics: Modeling Nonlinear Gene Interactions Using Bayesian MARS, 2006.
  

  @article{baladandayuthapani2006bayesian,
    title = {Bayesian Inference for Gene Expression and Proteomics: Modeling Nonlinear Gene Interactions Using Bayesian MARS},
    author = {Baladandayuthapani, Veerabhadran and Holmes, Chris C and Mallick, Bani K and Carroll, Raymond J},
    year = {2006}
  }
  

• Y. W. Teh , A Hierarchical Bayesian Language Model based on Pitman-Yor Processes, in Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics, 2006, 985–992.
  
  @inproceedings{Teh2006b,
    author = {Teh, Y. W.},
    booktitle = {Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics},
    pages = {985-992},
    title = {A Hierarchical {B}ayesian Language Model based on {P}itman-{Y}or Processes},
    year = {2006},
    bdsk-url-1 = {http://aclanthology.info/papers/a-hierarchical-bayesian-language-model-based-on-pitman-yor-processes},
    bdsk-url-2 = {http://aclweb.org/anthology/P06-1124}
  }
  

• Y. W. Teh , A Bayesian Interpretation of Interpolated Kneser-Ney, School of Computing, National University of Singapore, TRA2/06, 2006.
  
  Interpolated Kneser-Ney is one of the best smoothing methods for n-gram language models. Previous explanations for its superiority have been based on intuitive and empirical justifications of specific properties of the method. We propose a novel interpretation of interpolated Kneser-Ney as approximate inference in a hierarchical Bayesian model consisting of Pitman-Yor processes. As opposed to past explanations, our interpretation can recover exactly the formulation of interpolated Kneser-Ney, and performs better than interpolated Kneser-Ney when a better inference procedure is used.

  @techreport{Teh2006a,
    author = {Teh, Y. W.},
    institution = {School of Computing, National University of Singapore},
    number = {TRA2/06},
    title = {A {B}ayesian Interpretation of Interpolated {K}neser-{N}ey},
    year = {2006},
    bdsk-url-1 = {http://www.stats.ox.ac.uk/\\~{}teh/research/compling/hpylm.pdf}
  }
  

• E. P. Xing , K. Sohn , M. I. Jordan , Y. W. Teh , Bayesian Multi-population Haplotype Inference via a Hierarchical Dirichlet process mixture, in International Conference on Machine Learning (ICML), 2006, vol. 23.
  
  Uncovering the haplotypes of single nucleotide polymorphisms and their population demography is essential for many biological and medical applications. Methods for haplotype inference developed thus far—including methods based on coalescence, finite and infinite mixtures, and maximal parsimony— ignore the underlying population structure in the genotype data. As noted by Pritchard (2001), different populations can share certain portion of their genetic ancestors, as well as have their own genetic components through migration and diversification. In this paper, we address the problem of multipopulation haplotype inference. We capture cross-population structure using a nonparametric Bayesian prior known as the hierarchical Dirichlet process (HDP) (Teh et al., 2006), conjoining this prior with a recently developed Bayesian methodology for haplotype phasing known as DP-Haplotyper (Xing et al., 2004). We also develop an efficient sampling algorithm for the HDP based on a two-level nested Polya urn scheme. We show that our model outperforms extant algorithms on both simulated and real biological data.

  @inproceedings{XinSohJor2006a,
    author = {Xing, E. P. and Sohn, K. and Jordan, M. I. and Teh, Y. W.},
    booktitle = {International Conference on Machine Learning (ICML)},
    title = {{B}ayesian Multi-population Haplotype Inference via a Hierarchical {D}irichlet process mixture},
    volume = {23},
    year = {2006},
    bdsk-url-1 = {http://www.stats.ox.ac.uk/\\~{}teh/research/npbayes/icml2006.pdf}
  }
  

• Y. W. Teh , M. I. Jordan , M. J. Beal , D. M. Blei , Hierarchical Dirichlet Processes, Journal of the American Statistical Association, vol. 101, no. 476, 1566–1581, 2006.
  
  We consider problems involving groups of data, where each observation within a group is a draw from a mixture model, and where it is desirable to share mixture components between groups. We assume that the number of mixture components is unknown a priori and is to be inferred from the data. In this setting it is natural to consider sets of Dirichlet processes, one for each group, where the well-known clustering property of the Dirichlet process provides a nonparametric prior for the number of mixture components within each group. Given our desire to tie the mixture models in the various groups, we consider a hierarchical model, specifically one in which the base measure for the child Dirichlet processes is itself distributed according to a Dirichlet process. Such a base measure being discrete, the child Dirichlet processes necessarily share atoms. Thus, as desired, the mixture models in the different groups necessarily share mixture components. We discuss representations of hierarchical Dirichlet processes in terms of a stick-breaking process, and a generalization of the Chinese restaurant process that we refer to as the “Chinese restaurant franchise.” We present Markov chain Monte Carlo algorithms for posterior inference in hierarchical Dirichlet process mixtures, and describe applications to problems in information retrieval and text modelling.

  @article{TehJorBea2006a,
    author = {Teh, Y. W. and Jordan, M. I. and Beal, M. J. and Blei, D. M.},
    journal = {Journal of the American Statistical Association},
    number = {476},
    pages = {1566-1581},
    title = {Hierarchical {D}irichlet Processes},
    volume = {101},
    year = {2006},
    bdsk-url-1 = {http://www.jstor.org/stable/27639773},
    bdsk-url-2 = {http://www.stats.ox.ac.uk/\\~{}teh/research/npbayes/hdp2004.pdf}
  }
  

• G. E. Hinton , S. Osindero , Y. W. Teh , A Fast Learning Algorithm for Deep Belief Networks, Neural Computation, vol. 18, no. 7, 1527–1554, 2006.
  
  We show how to use “complementary priors” to eliminate the explaining-away effects that make inference difficult in densely connected belief nets that have many hidden layers. Using complementary priors, we derive a fast, greedy algorithm that can learn deep, directed belief networks one layer at a time, provided the top two layers form an undirected associative memory. The fast, greedy algorithm is used to initialize a slower learning procedure that fine-tunes the weights using a contrastive version of the wake-sleep algorithm. After fine-tuning, a network with three hidden layers forms a very good generative model of the joint distribution of handwritten digit images and their labels. This generative model gives better digit classification than the best discriminative learning algorithms. The low-dimensional manifolds on which the digits lie are modeled by long ravines in the free-energy landscape of the top-level associative memory, and it is easy to explore these ravines by using the directed connections to display what the associative memory has in mind.

  @article{HinOsiTeh2006a,
    author = {Hinton, G. E. and Osindero, S. and Teh, Y. W.},
    journal = {Neural Computation},
    number = {7},
    pages = {1527-1554},
    title = {A Fast Learning Algorithm for Deep Belief Networks},
    volume = {18},
    year = {2006},
    bdsk-url-1 = {http://www.mitpressjournals.org/doi/abs/10.1162/neco.2006.18.7.1527},
    bdsk-url-2 = {http://www.mitpressjournals.org/doi/pdf/10.1162/neco.2006.18.7.1527}
  }
  

• G. E. Hinton , S. Osindero , M. Welling , Y. W. Teh , Unsupervised Discovery of Non-linear Structure Using Contrastive Backpropagation, Cognitive Science, vol. 30, no. 4, 725–731, 2006.
  
  We describe a way of modeling high-dimensional data vectors by using an unsupervised, nonlinear, multilayer neural network in which the activity of each neuron-like unit makes an additive contribution to a global energy score that indicates how surprised the network is by the data vector. The connection weights that determine how the activity of each unit depends on the activities in earlier layers are learned by minimizing the energy assigned to data vectors that are actually observed and maximizing the energy assigned to “confabulations” that are generated by perturbing an observed data vector in a direction that decreases its energy under the current model.

  @article{HinOsiWel2006a,
    author = {Hinton, G. E. and Osindero, S. and Welling, M. and Teh, Y. W.},
    journal = {Cognitive Science},
    number = {4},
    pages = {725-731},
    title = {Unsupervised Discovery of Non-linear Structure Using Contrastive Backpropagation},
    volume = {30},
    year = {2006},
    bdsk-url-1 = {http://onlinelibrary.wiley.com/doi/10.1207/s15516709cog0000_76/abstract},
    bdsk-url-2 = {http://www.stats.ox.ac.uk/\\~{}teh/papers/HinOsiWel2006a.pdf}
  }
  

• W. S. Lee , X. Zhang , Y. W. Teh , Semi-supervised Learning in Reproducing Kernel Hilbert Spaces Using Local Invariances, School of Computing, National University of Singapore, TRB3/06, 2006.
  
  We propose a framework for semi-supervised learning in reproducing kernel Hilbert spaces using local invariances that explicitly characterize the behavior of the target function around both labeled and unlabeled data instances. Such invariances include: invariance to small changes to the data instances, invariance to averaging across a small neighbourhood around data instances, and invariance to local transformations such as translation and rotation. These invariances are approximated by minimizing loss functions on derivatives and local averages of the functions. We use a regularized cost function, consisting of the sum of loss functions penalized with the squared norm of the function, and give a representer theorem showing that an optimal function can be represented as a linear combination of a finite number of basis functions. For the representer theorem to hold, the derivatives and local averages are required to be bounded linear functionals in the reproducing kernel Hilbert space. We show that this is true in the reproducing kernel Hilbert spaces defined by Gaussian and polynomial kernels.

  @techreport{LeeZhaTeh2006a,
    author = {Lee, W. S. and Zhang, X. and Teh, Y. W.},
    institution = {School of Computing, National University of Singapore},
    number = {TRB3/06},
    title = {Semi-supervised Learning in Reproducing Kernel {H}ilbert Spaces Using Local Invariances},
    year = {2006},
    bdsk-url-1 = {http://www.stats.ox.ac.uk/\\~{}teh/research/invariances/semisup.pdf}
  }
  

2005

• H. Kim , B. K. Mallick , C. Holmes , Analyzing nonstationary spatial data using piecewise Gaussian processes, Journal of the American Statistical Association, vol. 100, no. 470, 653–668, 2005.
  

  @article{kim2005analyzing,
    title = {Analyzing nonstationary spatial data using piecewise Gaussian processes},
    author = {Kim, Hyoung-Moon and Mallick, Bani K and Holmes, CC},
    journal = {Journal of the American Statistical Association},
    volume = {100},
    number = {470},
    pages = {653--668},
    year = {2005},
    exclude_group_member = {Kim},
    publisher = {Taylor \& Francis}
  }
  

• K. Gallagher , J. Stephenson , R. Brown , C. C. Holmes , P. Ballester , Exploiting 3D spatial sampling in inverse modeling of thermochronological data, Reviews in mineralogy and geochemistry, vol. 58, no. 1, 375–387, 2005.
  

  @article{gallagher2005exploiting,
    title = {Exploiting 3D spatial sampling in inverse modeling of thermochronological data},
    author = {Gallagher, Kerry and Stephenson, John and Brown, Roderick and Holmes, Chris C. and Ballester, Pedro},
    journal = {Reviews in mineralogy and geochemistry},
    volume = {58},
    number = {1},
    pages = {375--387},
    year = {2005},
    publisher = {Mineral Soc America}
  }
  

• A. Pintore , C. C. Holmes , A dimension-reduction approach for spectral tempering using empirical orthogonal functions, in Geostatistics Banff 2004, Springer Netherlands, 2005, 1007–1015.
  

  @incollection{pintore2005dimension,
    title = {A dimension-reduction approach for spectral tempering using empirical orthogonal functions},
    author = {Pintore, Alexandre and Holmes, Chris C},
    booktitle = {Geostatistics Banff 2004},
    pages = {1007--1015},
    year = {2005},
    publisher = {Springer Netherlands}
  }
  

• J. Stephenson , C. C. Holmes , K. Gallagher , A. Pintore , A statistical technique for modelling non-stationary spatial processes, Geostatistics Banff 2004, 125–134, 2005.
  

  @article{stephenson2005statistical,
    title = {A statistical technique for modelling non-stationary spatial processes},
    author = {Stephenson, John and Holmes, Chris C. and Gallagher, Kerry and Pintore, Alexandre},
    journal = {Geostatistics Banff 2004},
    pages = {125--134},
    year = {2005},
    publisher = {Springer Netherlands}
  }
  

• K. Gallagher , J. Stephenson , R. Brown , C. C. Holmes , P. Fitzgerald , Low temperature thermochronology and modeling strategies for multiple samples 1: Vertical profiles, Earth and Planetary Science Letters, vol. 237, no. 1, 193–208, 2005.
  

  @article{gallagher2005low,
    title = {Low temperature thermochronology and modeling strategies for multiple samples 1: Vertical profiles},
    author = {Gallagher, Kerry and Stephenson, John and Brown, Roderick and Holmes, Chris C. and Fitzgerald, Paul},
    journal = {Earth and Planetary Science Letters},
    volume = {237},
    number = {1},
    pages = {193--208},
    year = {2005},
    publisher = {Elsevier}
  }
  

• A. Jasra , C. C. Holmes , D. A. Stephens , Markov chain Monte Carlo methods and the label switching problem in Bayesian mixture modeling, Statistical Science, 50–67, 2005.
  

  @article{jasra2005markov,
    title = {Markov chain Monte Carlo methods and the label switching problem in Bayesian mixture modeling},
    author = {Jasra, Ajay and Holmes, Chris C and Stephens, David A},
    journal = {Statistical Science},
    pages = {50--67},
    year = {2005},
    publisher = {Institute of Mathematical Statistics}
  }
  

• N. A. Heard , C. C. Holmes , D. A. Stephens , D. J. Hand , G. Dimopoulos , Bayesian coclustering of Anopheles gene expression time series: study of immune defense response to multiple experimental challenges, Proceedings of the National Academy of Sciences of the United States of America, vol. 102, no. 47, 16939, 2005.
  

  @article{heard2005bayesian,
    title = {Bayesian coclustering of Anopheles gene expression time series: study of immune defense response to multiple experimental challenges},
    author = {Heard, Nicholas A and Holmes, Chris C and Stephens, David A and Hand, David J and Dimopoulos, George},
    journal = {Proceedings of the National Academy of Sciences of the United States of America},
    volume = {102},
    number = {47},
    pages = {16939},
    year = {2005},
    publisher = {National Acad Sciences}
  }
  

• C. Holmes , D. T. Denison , S. Ray , B. Mallick , Bayesian prediction via partitioning, Journal of Computational and Graphical Statistics, vol. 14, no. 4, 811–830, 2005.
  

  @article{holmes2005bayesian,
    title = {Bayesian prediction via partitioning},
    author = {Holmes, CC and Denison, DG T and Ray, S and Mallick, BK},
    journal = {Journal of Computational and Graphical Statistics},
    volume = {14},
    number = {4},
    pages = {811--830},
    year = {2005},
    publisher = {Taylor \& Francis}
  }
  

• C. C. Holmes , S. D. Brown , All systems GO for understanding mouse gene function, The Scientist, vol. 19, no. 1, 20–1, 2005.
  

  @article{holmes2005all,
    title = {All systems GO for understanding mouse gene function},
    author = {Holmes, Chris C. and Brown, Steve DM},
    journal = {The Scientist},
    volume = {19},
    number = {1},
    pages = {20--1},
    year = {2005},
    publisher = {Scientist Inc.}
  }
  

• Q. Atkinson , G. K. Nicholls , D. Welch , R. Gray , From words to dates: Water into wine, mathemagic or phylogenetic inference?, Transactions of the Philological Society, vol. 103, no. 2, 193–219, 2005.
  

  @article{Atkinson2005193,
    author = {Atkinson, Q. and Nicholls, G. K. and Welch, D. and Gray, R.},
    title = {From words to dates: Water into wine, mathemagic or phylogenetic inference?},
    journal = {Transactions of the Philological Society},
    year = {2005},
    volume = {103},
    number = {2},
    pages = {193-219},
    doi = {10.1111/j.1467-968X.2005.00151.x},
    note = {cited By 40},
    document_type = {Article},
    source = {Scopus}
  }
  

• D. Welch , G. K. Nicholls , A. Rodrigo , W. Solomon , Integrating genealogy and epidemiology: The ancestral infection and selection graph as a model for reconstructing host virus histories, Theoretical Population Biology, vol. 68, no. 1, 65–75, 2005.
  

  @article{Welch200565,
    author = {Welch, D. and Nicholls, G. K. and Rodrigo, A. and Solomon, W.},
    title = {Integrating genealogy and epidemiology: The ancestral infection and selection graph as a model for reconstructing host virus histories},
    journal = {Theoretical Population Biology},
    year = {2005},
    volume = {68},
    number = {1},
    pages = {65-75},
    doi = {10.1016/j.tpb.2005.03.003},
    note = {cited By 8},
    document_type = {Article},
    source = {Scopus}
  }
  

• I. McKeague , G. K. Nicholls , K. Speer , R. Herbei , Statistical inversion of South Atlantic circulation in an abyssal neutral density layer, Journal of Marine Research, vol. 63, no. 4, 683–704, 2005.
  

  @article{McKeague2005683,
    author = {McKeague, I.W. and Nicholls, G. K. and Speer, K. and Herbei, R.},
    title = {Statistical inversion of South Atlantic circulation in an abyssal neutral density layer},
    journal = {Journal of Marine Research},
    year = {2005},
    volume = {63},
    number = {4},
    pages = {683-704},
    doi = {10.1357/0022240054663240},
    note = {cited By 21},
    document_type = {Article},
    source = {Scopus}
  }
  

• G. Gayraud , J. Rousseau , Rates of convergence for a Bayesian level set estimation, Scandinavian journal of statistics, vol. 32, no. 4, 639–660, 2005.
  

  @article{gayraud2005rates,
    title = {Rates of convergence for a Bayesian level set estimation},
    author = {Gayraud, Ghislaine and Rousseau, Judith},
    journal = {Scandinavian journal of statistics},
    volume = {32},
    number = {4},
    pages = {639--660},
    year = {2005},
    publisher = {Wiley Online Library}
  }
  

• C. Guihenneuc-Jouyaux , J. Rousseau , Laplace expansions in Markov chain Monte Carlo algorithms, Journal of Computational and Graphical Statistics, vol. 14, no. 1, 75–94, 2005.
  

  @article{guihenneuc2005laplace,
    title = {Laplace expansions in Markov chain Monte Carlo algorithms},
    author = {Guihenneuc-Jouyaux, Chantal and Rousseau, Judith},
    journal = {Journal of Computational and Graphical Statistics},
    volume = {14},
    number = {1},
    pages = {75--94},
    year = {2005},
    publisher = {Taylor \& Francis}
  }
  

• Y. W. Teh , M. I. Jordan , M. J. Beal , D. M. Blei , Sharing Clusters among Related Groups: Hierarchical Dirichlet Processes, in Advances in Neural Information Processing Systems (NeurIPS), 2005, vol. 17.
  
  We propose the hierarchical Dirichlet process (HDP), a nonparametric Bayesian model for clustering problems involving multiple groups of data. Each group of data is modeled with a mixture, with the number of components being open-ended and inferred automatically by the model. Further, components can be shared across groups, allowing dependencies across groups to be modeled effectively as well as conferring generalization to new groups. Such grouped clustering problems occur often in practice, e.g. in the problem of topic discovery in document corpora. We report experimental results on three text corpora showing the effective and superior performance of the HDP over previous models.

  @inproceedings{TehJorBea2005a,
    author = {Teh, Y. W. and Jordan, M. I. and Beal, M. J. and Blei, D. M.},
    booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
    title = {Sharing Clusters among Related Groups: Hierarchical {D}irichlet Processes},
    volume = {17},
    year = {2005},
    bdsk-url-1 = {https://papers.nips.cc/paper/2698-sharing-clusters-among-related-groups-hierarchical-dirichlet-processes},
    bdsk-url-2 = {https://papers.nips.cc/paper/2698-sharing-clusters-among-related-groups-hierarchical-dirichlet-processes.pdf}
  }
  

• J. Edwards , Y. W. Teh , D. A. Forsyth , R. Bock , M. Maire , G. Vesom , Making Latin Manuscripts Searchable using gHMM’s, in Advances in Neural Information Processing Systems (NeurIPS), 2005, vol. 17.
  
  We describe a method that can make a scanned, handwritten mediaeval latin manuscript accessible to full text search. A generalized HMM is fitted, using transcribed latin to obtain a transition model and one example each of 22 letters to obtain an emission model. We show results for unigram, bigram and trigram models. Our method transcribes 25 pages of a manuscript of Terence with fair accuracy (75% of letters correctly transcribed). Search results are very strong; we use examples of variant spellings to demonstrate that the search respects the ink of the document. Furthermore, our model produces fair searches on a document from which we obtained no training data.

  @inproceedings{EdwTehFor2005a,
    author = {Edwards, J. and Teh, Y. W. and Forsyth, D. A. and Bock, R. and Maire, M. and Vesom, G.},
    booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
    title = {Making Latin Manuscripts Searchable using {gHMM}'s},
    volume = {17},
    year = {2005},
    bdsk-url-1 = {http://papers.nips.cc/paper/2706-making-latin-manuscripts-searchable-using-ghmms},
    bdsk-url-2 = {http://papers.nips.cc/paper/2706-making-latin-manuscripts-searchable-using-ghmms.pdf}
  }
  

• Y. W. Teh , M. Seeger , M. I. Jordan , Semiparametric Latent Factor Models, in Artificial Intelligence and Statistics (AISTATS), 2005, vol. 10.
  
  We propose a semiparametric model for regression problems involving multiple response variables. The model makes use of a set of Gaussian processes that are linearly mixed to capture dependencies that may exist among the response variables. We propose an efficient approximate inference scheme for this semiparametric model whose complexity is linear in the number of training data points. We present experimental results in the domain of multi-joint robot arm dynamics.

  @inproceedings{TehSeeJor2005a,
    author = {Teh, Y. W. and Seeger, M. and Jordan, M. I.},
    booktitle = {Artificial Intelligence and Statistics (AISTATS)},
    title = {Semiparametric Latent Factor Models},
    volume = {10},
    year = {2005},
    bdsk-url-1 = {http://www.stats.ox.ac.uk/\\~{}teh/research/npbayes/aistats2005.pdf}
  }
  

• M. Seeger , Y. W. Teh , M. I. Jordan , Semiparametric Latent Factor Models, Division of Computer Science, University of California at Berkeley, 2005.
  
  We propose a semiparametric model for regression and classification problems involving multiple response variables. The model makes use of a set of Gaussian processes to model the relationship to the inputs in a nonparametric fashion. Conditional dependencies between the responses can be captured through a linear mixture of the driving processes. This feature becomes important if some of the responses of predictive interest are less densely supplied by observed data than related auxiliary ones. We propose an efficient approximate inference scheme for this semiparametric model whose complexity is linear in the number of training data points.

  @techreport{SeeTehJor2005a,
    author = {Seeger, M. and Teh, Y. W. and Jordan, M. I.},
    institution = {Division of Computer Science, University of California at Berkeley},
    title = {Semiparametric Latent Factor Models},
    year = {2005},
    bdsk-url-1 = {http://www.stats.ox.ac.uk/\\~{}teh/research/npbayes/slfm2005.pdf}
  }
  

• M. Welling , T. Minka , Y. W. Teh , Structured Region Graphs: Morphing EP into GBP, in Uncertainty in Artificial Intelligence (UAI), 2005, vol. 21.
  
  GBP and EP are two successful algorithms for approximate probabilistic inference, which are based on different approximation strategies. An open problem in both algorithms has been how to choose an appropriate approximation structure. We introduce ’structured region graphs’, a formalism which marries these two strategies, reveals a deep connection between them, and suggests how to choose good approximation structures. In this formalism, each region has an internal structure which defines an exponential family, whose sufficient statistics must be matched by the parent region. Reduction operators on these structures allow conversion between EP and GBP free energies. Thus it is revealed that all EP approximations on discrete variables are special cases of GBP, and conversely that some wellknown GBP approximations, such as overlapping squares, are special cases of EP. Furthermore, region graphs derived from EP have a number of good structural properties, including maxent-normality and overall counting number of one. The result is a convenient framework for producing high-quality approximations with a user-adjustable level of complexity.

  @inproceedings{WelMinTeh2005a,
    author = {Welling, M. and Minka, T. and Teh, Y. W.},
    booktitle = {Uncertainty in Artificial Intelligence (UAI)},
    title = {Structured Region Graphs: Morphing {EP} into {GBP}},
    volume = {21},
    year = {2005},
    bdsk-url-1 = {https://arxiv.org/pdf/1207.1426v1.pdf}
  }
  

2004

• A. Pintore , C. Holmes , Spatially adaptive non-stationary covariance functions via spatially adaptive spectra, http:\backslash\backslash www. stats. ox. ac. uk cholmes\backslash Reports\backslash spectral tempering. pdf, 2004.
  

  @article{pintore2004spatially,
    title = {Spatially adaptive non-stationary covariance functions via spatially adaptive spectra},
    author = {Pintore, A and Holmes, C},
    journal = {http:$\backslash$$\backslash$ www. stats. ox. ac. uk cholmes$\backslash$ Reports$\backslash$ spectral tempering. pdf},
    year = {2004}
  }
  

• C. C. Holmes , S. D. Brown , All systems GO for understanding mouse gene function, Journal of biology, vol. 3, no. 5, 20, 2004.
  

  @article{holmes2004all,
    title = {All systems GO for understanding mouse gene function},
    author = {Holmes, Chris C. and Brown, Steve DM},
    journal = {Journal of biology},
    volume = {3},
    number = {5},
    pages = {20},
    year = {2004},
    publisher = {BioMed Central}
  }
  

• J. Stephenson , K. Gallagher , C. Holmes , Beyond kriging: dealing with discontinuous spatial data fields using adaptive prior information and Bayesian partition modelling, Geological Society, London, Special Publications, vol. 239, no. 1, 195–209, 2004.
  

  @article{stephenson2004beyond,
    title = {Beyond kriging: dealing with discontinuous spatial data fields using adaptive prior information and Bayesian partition modelling},
    author = {Stephenson, John and Gallagher, K and Holmes, CC},
    journal = {Geological Society, London, Special Publications},
    volume = {239},
    number = {1},
    pages = {195--209},
    year = {2004},
    publisher = {Geological Society of London}
  }
  

• G. Ewing , G. K. Nicholls , A. Rodrigo , Using temporally spaced sequences to simultaneously estimate migration rates, mutation rate and population sizes in measurably evolving populations, Genetics, vol. 168, no. 4, 2407–2420, 2004.
  

  @article{Ewing20042407,
    author = {Ewing, G. and Nicholls, G. K. and Rodrigo, A.},
    title = {Using temporally spaced sequences to simultaneously estimate migration rates, mutation rate and population sizes in measurably evolving populations},
    journal = {Genetics},
    year = {2004},
    volume = {168},
    number = {4},
    pages = {2407-2420},
    doi = {10.1534/genetics.104.030411},
    note = {cited By 25},
    document_type = {Article},
    source = {Scopus}
  }
  

• A. Mira , G. K. Nicholls , Bridge estimation of the probability density at a point, Statistica Sinica, vol. 14, no. 2, 603–612, 2004.
  

  @article{Mira2004603,
    author = {Mira, A. and Nicholls, G. K.},
    title = {Bridge estimation of the probability density at a point},
    journal = {Statistica Sinica},
    year = {2004},
    volume = {14},
    number = {2},
    pages = {603-612},
    note = {cited By 16},
    document_type = {Article},
    source = {Scopus}
  }
  

• P. Müller , G. Parmigiani , C. Robert , J. Rousseau , Optimal sample size for multiple testing: the case of gene expression microarrays, Journal of the American Statistical Association, vol. 99, no. 468, 990–1001, 2004.
  

  @article{muller2004optimal,
    title = {Optimal sample size for multiple testing: the case of gene expression microarrays},
    author = {M{\"u}ller, Peter and Parmigiani, Giovanni and Robert, Christian and Rousseau, Judith},
    journal = {Journal of the American Statistical Association},
    volume = {99},
    number = {468},
    pages = {990--1001},
    year = {2004},
    publisher = {Taylor \& Francis}
  }
  

• M. Welling , M. Rosen-Zvi , Y. W. Teh , Approximate Inference by Markov Chains on Union Spaces, in International Conference on Machine Learning (ICML), 2004, vol. 21.
  
  @inproceedings{WelRosTeh2004a,
    author = {Welling, M. and Rosen-Zvi, M. and Teh, Y. W.},
    booktitle = {International Conference on Machine Learning (ICML)},
    title = {Approximate Inference by Markov Chains on Union Spaces},
    volume = {21},
    year = {2004},
    bdsk-url-1 = {http://www.stats.ox.ac.uk/\\~{}teh/research/papers/WelRosTeh2004a.pdf}
  }
  

• M. Welling , Y. W. Teh , Linear Response Algorithms for Approximate Inference in Graphical Models, Neural Computation, vol. 16, 197–221, 2004.
  
  Belief propagation (BP) on cyclic graphs is an efficient algorithm for computing approximate marginal probability distributions over single nodes and neighboring nodes in the graph. However, it does not prescribe a way to compute joint distributions over pairs of distant nodes in the graph. In this article, we propose two new algorithms for approximating these pairwise probabilities, based on the linear response theorem. The first is a propagation algorithm that is shown to converge if BP converges to a stable fixed point. The second algorithm is based on matrix inversion. Applying these ideas to gaussian random fields, we derive a propagation algorithm for computing the inverse of a matrix.

  @article{WelTeh2004a,
    author = {Welling, M. and Teh, Y. W.},
    journal = {Neural Computation},
    pages = {197-221},
    title = {Linear Response Algorithms for Approximate Inference in Graphical Models},
    volume = {16},
    year = {2004},
    bdsk-url-1 = {http://cognet.mit.edu/journal/10.1162/08997660460734056},
    bdsk-url-2 = {http://cognet.mit.edu/system/cogfiles/journalpdfs/08997660460734056.pdf}
  }
  

• T. Miller , A. C. Berg , J. Edwards , M. Maire , R. White , Y. W. Teh , E. Learned-Miller , D. A. Forsyth , Faces and Names in the News, in Proceedings of the Conference on Computer Vision and Pattern Recognition, 2004.
  
  We show quite good face clustering is possible for a dataset of inaccurately and ambiguously labelled face images. Our dataset is 44,773 face images, obtained by applying a face finder to approximately half a million captioned news images. This dataset is more realistic than usual face recognition datasets, because it contains faces captured “in the wild” in a variety of configurations with respect to the camera, taking a variety of expressions, and under illumination of widely varying color. Each face image is associated with a set of names, automatically extracted from the associated caption. Many, but not all such sets contain the correct name. We cluster face images in appropriate discriminant coordinates. We use a clustering procedure to break ambiguities in labelling and identify incorrectly labelled faces. A merging procedure then identifies variants of names that refer to the same individual. The resulting representation can be used to label faces in news images or to organize news pictures by individuals present. An alternative view of our procedure is as a process that cleans up noisy supervised data. We demonstrate how to use entropy measures to evaluate such procedures.

  @inproceedings{MilBerEdw2004a,
    author = {Miller, T. and Berg, A. C. and Edwards, J. and Maire, M. and White, R. and Teh, Y. W. and Learned-Miller, E. and Forsyth, D. A.},
    booktitle = {Proceedings of the Conference on Computer Vision and Pattern Recognition},
    title = {Faces and Names in the News},
    year = {2004},
    bdsk-url-1 = {http://www.stats.ox.ac.uk/\\~{}teh/research/compvision/cvpr2004.pdf}
  }
  

• Y. W. Teh , M. I. Jordan , M. J. Beal , D. M. Blei , Hierarchical Dirichlet Processes, Department of Statistics, University of California at Berkeley, 653, 2004.
  
  We consider problems involving groups of data, where each observation within a group is a draw from a mixture model, and where it is desirable to share mixture components between groups. We assume that the number of mixture components is unknown a priori and is to be inferred from the data. In this setting it is natural to consider sets of Dirichlet processes, one for each group, where the well-known clustering property of the Dirichlet process provides a nonparametric prior for the number of mixture components within each group. Given our desire to tie the mixture models in the various groups, we consider a hierarchical model, specifically one in which the base measure for the child Dirichlet processes is itself distributed according to a Dirichlet process. Such a base measure being discrete, the child Dirichlet processes necessarily share atoms. Thus, as desired, the mixture models in the different groups necessarily share mixture components. We discuss representations of hierarchical Dirichlet processes in terms of a stick-breaking process, and a generalization of the Chinese restaurant process that we refer to as the “Chinese restaurant franchise.” We present Markov chain Monte Carlo algorithms for posterior inference in hierarchical Dirichlet process mixtures, and describe applications to problems in information retrieval and text modelling.

  @techreport{TehJorBea2004a,
    author = {Teh, Y. W. and Jordan, M. I. and Beal, M. J. and Blei, D. M.},
    institution = {Department of Statistics, University of California at Berkeley},
    number = {653},
    title = {Hierarchical {D}irichlet Processes},
    year = {2004},
    bdsk-url-1 = {http://www.stats.ox.ac.uk/\\~{}teh/research/npbayes/hdp2004.pdf}
  }
  

2003

• C. Holmes , L. Held , On the simulation of Bayesian binary and polychotomous regression models using auxiliary variables, Technical report. Available at: http://www. stat. uni-muenchen. de/\\~ leo, 2003.
  

  @techreport{holmes2003simulation,
    title = {On the simulation of Bayesian binary and polychotomous regression models using auxiliary variables},
    author = {Holmes, CC and Held, L},
    year = {2003},
    institution = {Technical report. Available at: http://www. stat. uni-muenchen. de/\\\~{}{} leo}
  }
  

• C. Holmes , L. Knorr-Held , Efficient simulation of Bayesian logistic regression models, Discussion papers/Sonderforschungsbereich 386 der Ludwig-Maximilians-Universität München, 2003.
  

  @techreport{holmes2003efficient,
    title = {Efficient simulation of Bayesian logistic regression models},
    author = {Holmes, C and Knorr-Held, Leonhard},
    year = {2003},
    institution = {Discussion papers/Sonderforschungsbereich 386 der Ludwig-Maximilians-Universit{\"a}t M{\"u}nchen}
  }
  

• C. Holmes , N. Heard , Generalized monotonic regression using random change points, Statistics in Medicine, vol. 22, no. 4, 623–638, 2003.
  

  @article{holmes2003generalized,
    title = {Generalized monotonic regression using random change points},
    author = {Holmes, CC and Heard, NA},
    journal = {Statistics in Medicine},
    volume = {22},
    number = {4},
    pages = {623--638},
    year = {2003},
    publisher = {Wiley Online Library}
  }
  

• C. Holmes , B. Mallick , Generalized nonlinear modeling with multivariate free-knot regression splines, Journal of the American Statistical Association, vol. 98, no. 462, 352–368, 2003.
  

  @article{holmes2003generalizee,
    title = {Generalized nonlinear modeling with multivariate free-knot regression splines},
    author = {Holmes, CC and Mallick, BK},
    journal = {Journal of the American Statistical Association},
    volume = {98},
    number = {462},
    pages = {352--368},
    year = {2003},
    publisher = {Taylor \& Francis}
  }
  

• C. C. Holmes , N. M. Adams , Likelihood inference in nearest-neighbour classification models, Biometrika, 99–112, 2003.
  

  @article{holmes2003likelihood,
    title = {Likelihood inference in nearest-neighbour classification models},
    author = {Holmes, Chris C and Adams, Niall M},
    journal = {Biometrika},
    pages = {99--112},
    year = {2003},
    publisher = {Biometrika Trust, University College London}
  }
  

• C. Holmes , D. Denison , Classification with bayesian MARS, Machine Learning, vol. 50, no. 1, 159–173, 2003.
  

  @article{holmes2003classification,
    title = {Classification with bayesian MARS},
    author = {Holmes, CC and Denison, DGT},
    journal = {Machine Learning},
    volume = {50},
    number = {1},
    pages = {159--173},
    year = {2003},
    publisher = {Springer Netherlands}
  }
  

• C. Holmes , B. Mallick , Perfect simulation for Bayesian curve and surface fitting, Preprint from www. stat. tamu. edu/\\~ bmallick/papers/perf. ps, 2003.
  

  @article{holmes2003perfect,
    title = {Perfect simulation for Bayesian curve and surface fitting},
    author = {Holmes, CC and Mallick, BK},
    journal = {Preprint from www. stat. tamu. edu/\\\~{}{} bmallick/papers/perf. ps},
    year = {2003}
  }
  

• R. Graziani , C. C. Holmes , Bayesian free knot polynomial splines of random order. Università commerciale Luigi Bocconi, 2003.
  

  @misc{graziani2003bayesian,
    title = {Bayesian free knot polynomial splines of random order},
    author = {Graziani, Rebecca and Holmes, Chris C.},
    year = {2003},
    publisher = {Universit{\`a} commerciale Luigi Bocconi}
  }
  

• R. M. Gray , D. Denison , M. Hansen , C. Holmes , B. Mallick , B. Yu , Gauss mixture quantization: clustering Gauss mixtures, in Nonlinear Estimation and Classification, 2003, vol. 1003, 189–212.
  

  @inproceedings{gray2003gauss,
    title = {Gauss mixture quantization: clustering Gauss mixtures},
    author = {Gray, Robert M and Denison, DD and Hansen, MH and Holmes, CC and Mallick, B and Yu, B},
    booktitle = {Nonlinear Estimation and Classification},
    volume = {1003},
    pages = {189--212},
    year = {2003},
    organization = {Springer, New York}
  }
  

• C. Holmes , D. Denison , Stochastic search algorithms inspired by physical and biological systems are applied to the problem of learning directed graphical probability models in the presence of missing observations and hidden variables. For this class of problems, deterministic search algorithms tend to halt at local optima, requiring random restarts to obtain solutions of acceptable quality. We compare three stochastic search..., Machine Learning, vol. 50, no. 3, 279–301, 2003.
  

  @article{holmes2003stochastic,
    title = {Stochastic search algorithms inspired by physical and biological systems are applied to the problem of learning directed graphical probability models in the presence of missing observations and hidden variables. For this class of problems, deterministic search algorithms tend to halt at local optima, requiring random restarts to obtain solutions of acceptable quality. We compare three stochastic search...},
    author = {Holmes, CC and Denison, DGT},
    journal = {Machine Learning},
    volume = {50},
    number = {3},
    pages = {279--301},
    year = {2003}
  }
  

• O. Lieberman , J. Rousseau , D. M. Zucker , . others , Valid asymptotic expansions for the maximum likelihood estimator of the parameter of a stationary, Gaussian, strongly dependent process, The Annals of Statistics, vol. 31, no. 2, 586–612, 2003.
  

  @article{lieberman2003valid,
    title = {Valid asymptotic expansions for the maximum likelihood estimator of the parameter of a stationary, Gaussian, strongly dependent process},
    author = {Lieberman, Offer and Rousseau, Judith and Zucker, David M and others},
    journal = {The Annals of Statistics},
    volume = {31},
    number = {2},
    pages = {586--612},
    year = {2003},
    publisher = {Institute of Mathematical Statistics}
  }
  

• Y. W. Teh , M. Welling , S. Osindero , G. E. Hinton , Energy-Based Models for Sparse Overcomplete Representations, Journal of Machine Learning Research (JMLR), vol. 4, 1235–1260, 2003.
  
  @article{TehWelOsi2003a,
    author = {Teh, Y. W. and Welling, M. and Osindero, S. and Hinton, G. E.},
    journal = {Journal of Machine Learning Research (JMLR)},
    pages = {1235-1260},
    title = {Energy-Based Models for Sparse Overcomplete Representations},
    volume = {4},
    year = {2003},
    bdsk-url-1 = {http://www.jmlr.org/papers/v4/teh03a.html},
    bdsk-url-2 = {http://www.jmlr.org/papers/volume4/teh03a/teh03a.pdf}
  }
  

• Y. W. Teh , Bethe Free Energy and Contrastive Divergence Approximations for Undirected Graphical Models, PhD thesis, Department of Computer Science, University of Toronto, 2003.
  
  As the machine learning community tackles more complex and harder problems, the graphical models needed to solve such problems become larger and more complicated. As a result performing inference and learning exactly for such graphical models become ever more expensive, and approximate inference and learning techniques become ever more prominent. There are a variety of techniques for approximate inference and learning in the literature. This thesis contributes some new ideas in the products of experts (PoEs) class of models (Hinton, 2002), and the Bethe free energy approximations (Yedidia et al., 2001). For PoEs, our contribution is in developing new PoE models for continuous-valued domains. We developed RBMrate, a model for discretized continuous-valued data. We applied it to face recognition to demonstrate its abilities. We also developed energy-based models (EBMs) – flexible probabilistic models where the building blocks consist of energy terms computed using a feed-forward network. We show that standard square noiseless independent components analysis (ICA) (Bell and Sejnowski, 1995) can be viewed as a restricted form of EBMs. Extending this relationship with ICA, we describe sparse and over-complete representations of data where the inference process is trivial since it is simply an EBM. For Bethe free energy approximations, our contribution is a theory relating belief propagation and iterative scaling. We show that both belief propagation and iterative scaling updates can be derived as fixed point equations for constrained minimization of the Bethe free energy. This allows usto develop a new algorithm to directly minimize the Bethe free energy, and to apply the Bethe free energy to learning in addition to inference. We also describe improvements to the efficiency of standard learning algorithms for undirected graphical models (Jirousek and Preucil, 1995).

  @phdthesis{Teh2003a,
    author = {Teh, Y. W.},
    school = {Department of Computer Science, University of Toronto},
    title = {Bethe Free Energy and Contrastive Divergence Approximations for Undirected Graphical Models},
    year = {2003},
    bdsk-url-1 = {http://www.stats.ox.ac.uk/\\~{}teh/research/theses/phdthesis.pdf}
  }
  

• Y. W. Teh , S. Roweis , Automatic Alignment of Local Representations, in Advances in Neural Information Processing Systems (NeurIPS), 2003, vol. 15.
  
  We present an automatic alignment procedure which maps the disparate internal representations learned by several local dimensionality reduction experts into a single, coherent global coordinate system for the original data space. Our algorithm can be applied to any set of experts, each of which produces a low-dimensional local representation of a highdimensional input. Unlike recent efforts to coordinate such models by modifying their objective functions [1, 2], our algorithm is invoked after training and applies an efficient eigensolver to post-process the trained models. The post-processing has no local optima and the size of the system it must solve scales with the number of local models rather than the number of original data points, making it more efficient than model-free algorithms such as Isomap [3] or LLE [4].

  @inproceedings{TehRow2003a,
    author = {Teh, Y. W. and Roweis, S.},
    booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
    title = {Automatic Alignment of Local Representations},
    volume = {15},
    year = {2003},
    bdsk-url-1 = {http://papers.nips.cc/paper/2180-automatic-alignment-of-local-representations},
    bdsk-url-2 = {http://papers.nips.cc/paper/2180-automatic-alignment-of-local-representations.pdf}
  }
  

• Y. W. Teh , M. Welling , On Improving the Efficiency of the Iterative Proportional Fitting Procedure, in Artificial Intelligence and Statistics (AISTATS), 2003, vol. 9.
  
  Iterative proportional fitting (IPF) on junction trees is an important tool for learning in graphical models. We identify the propagation and IPF updates on the junction tree as fixed point equations of a single constrained entropy maximization problem. This allows a more efficient message updating protocol than the well known effective IPF of Jirousek and Preucil (1995). When the junction tree has an intractably large maximum clique size we propose to maximize an approximate constrained entropy based on region graphs (Yedidia et al., 2002). To maximize the new objective we propose a “loopy” version of IPF. We show that this yields accurate estimates of the weights of undirected graphical models in a simple experiment.

  @inproceedings{TehWel2003a,
    author = {Teh, Y. W. and Welling, M.},
    booktitle = {Artificial Intelligence and Statistics (AISTATS)},
    title = {On Improving the Efficiency of the Iterative Proportional Fitting Procedure},
    volume = {9},
    year = {2003},
    bdsk-url-1 = {http://www.stats.ox.ac.uk/\\~{}teh/research/inference/aistats2003.pdf}
  }
  

• M. Welling , Y. W. Teh , Approximate Inference in Boltzmann Machines, Artificial Intelligence, vol. 143, no. 1, 19–50, 2003.
  
  Inference in Boltzmann machines is NP-hard in general. As a result approximations are often necessary. We discuss first order mean field and second order Onsager truncations of the Plefka expansion of the Gibbs free energy. The Bethe free energy is introduced and rewritten as a Gibbs free energy. From there a convergent belief optimization algorithm is derived to minimize the Bethe free energy. An analytic expression for the linear response estimate of the covariances is found which is exact on Boltzmann trees. Finally, a number of theorems is proven concerning the Plefka expansion, relating the first order mean field and the second order Onsager approximation to the Bethe approximation. Experiments compare mean field approximation, Onsager approximation, belief propagation and belief optimization.

  @article{WelTeh2003a,
    author = {Welling, M. and Teh, Y. W.},
    journal = {Artificial Intelligence},
    number = {1},
    pages = {19-50},
    title = {Approximate Inference in {B}oltzmann Machines},
    volume = {143},
    year = {2003},
    bdsk-url-1 = {http://www.sciencedirect.com/science/article/pii/S0004370202003612},
    bdsk-url-2 = {http://www.stats.ox.ac.uk/\\~{}teh/research/inference/aij2003.pdf}
  }
  

2002

• C. C. Holmes , D. G. Denison , Perfect sampling for the wavelet reconstruction of signals, IEEE Transactions on Signal Processing, vol. 50, no. 2, 337–344, 2002.
  

  @article{holmes2002perfect,
    title = {Perfect sampling for the wavelet reconstruction of signals},
    author = {Holmes, Chris C. and Denison, David GT},
    journal = {IEEE Transactions on Signal Processing},
    volume = {50},
    number = {2},
    pages = {337--344},
    year = {2002},
    publisher = {IEEE}
  }
  

• A. Guglielmi , C. C. Holmes , S. G. Walker , Perfect simulation involving functionals of a Dirichlet process, Journal of Computational and Graphical Statistics, vol. 11, no. 2, 306–310, 2002.
  

  @article{guglielmi2002perfect,
    title = {Perfect simulation involving functionals of a Dirichlet process},
    author = {Guglielmi, Alessandra and Holmes, Chris C and Walker, Stephen G},
    journal = {Journal of Computational and Graphical Statistics},
    volume = {11},
    number = {2},
    pages = {306--310},
    year = {2002},
    publisher = {Taylor \& Francis}
  }
  

• D. Denison , N. Adams , C. Holmes , D. Hand , Bayesian partition modelling, Computational statistics & data analysis, vol. 38, no. 4, 475–485, 2002.
  

  @article{denison2002bayesian,
    title = {Bayesian partition modelling},
    author = {Denison, DGT and Adams, NM and Holmes, CC and Hand, DJ},
    journal = {Computational statistics \& data analysis},
    volume = {38},
    number = {4},
    pages = {475--485},
    year = {2002},
    publisher = {Elsevier}
  }
  

• J. Ferreira , D. Denison , C. Holmes , Partition modelling, 2002.
  

  @article{ferreira2002partition,
    title = {Partition modelling},
    author = {Ferreira, JTAS and Denison, DGT and Holmes, CC},
    year = {2002}
  }
  

• C. Holmes , N. Adams , A probabilistic nearest neighbour method for statistical pattern recognition, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol. 64, no. 2, 295–306, 2002.
  

  @article{holmes2002probabilistic,
    title = {A probabilistic nearest neighbour method for statistical pattern recognition},
    author = {Holmes, CC and Adams, NM},
    journal = {Journal of the Royal Statistical Society: Series B (Statistical Methodology)},
    volume = {64},
    number = {2},
    pages = {295--306},
    year = {2002},
    publisher = {Wiley Online Library}
  }
  

• C. Holmes , D. T. Denison , B. Mallick , Accounting for model uncertainty in seemingly unrelated regressions, Journal of Computational and Graphical Statistics, vol. 11, no. 3, 533–551, 2002.
  

  @article{holmes2002accounting,
    title = {Accounting for model uncertainty in seemingly unrelated regressions},
    author = {Holmes, CC and Denison, DG T and Mallick, BK},
    journal = {Journal of Computational and Graphical Statistics},
    volume = {11},
    number = {3},
    pages = {533--551},
    year = {2002},
    publisher = {Taylor \& Francis}
  }
  

• C. Holmes , D. Denison , B. Mallick , Bayesian model order determination and basis selection for seemingly unrelated regressions, Journal of Computational and Graphical Statistics, vol. 11, 533s551, 2002.
  

  @article{holmes2002bayesian,
    title = {Bayesian model order determination and basis selection for seemingly unrelated regressions},
    author = {Holmes, C and Denison, D and Mallick, B},
    journal = {Journal of Computational and Graphical Statistics},
    volume = {11},
    pages = {533s551},
    year = {2002}
  }
  

• D. G. Denison , Bayesian methods for nonlinear classification and regression. John Wiley & Sons, 2002.
  

  @misc{denison2002bayesiao,
    title = {Bayesian methods for nonlinear classification and regression},
    author = {Denison, David GT},
    year = {2002},
    publisher = {John Wiley \& Sons}
  }
  

• C. Holmes , [Spline Adaptation in Extended Linear Models]: Comment, Statistical Science, vol. 17, no. 1, 22–24, 2002.
  

  @article{holmes2002spline,
    title = {[Spline Adaptation in Extended Linear Models]: Comment},
    author = {Holmes, CC},
    journal = {Statistical Science},
    volume = {17},
    number = {1},
    pages = {22--24},
    year = {2002},
    publisher = {Institute of Mathematical Statistics}
  }
  

• M. Jones , G. K. Nicholls , New radiocarbon calibration software, Radiocarbon, vol. 44, no. 3, 663–674, 2002.
  

  @article{Jones2002663,
    author = {Jones, M. and Nicholls, G. K.},
    title = {New radiocarbon calibration software},
    journal = {Radiocarbon},
    year = {2002},
    volume = {44},
    number = {3},
    pages = {663-674},
    note = {cited By 23},
    document_type = {Conference Paper},
    source = {Scopus}
  }
  

• A. Drummond , G. K. Nicholls , A. Rodrigo , W. Solomon , Estimating mutation parameters, population history and genealogy simultaneously from temporally spaced sequence data, Genetics, vol. 161, no. 3, 1307–1320, 2002.
  

  @article{Drummond20021307,
    author = {Drummond, A.J. and Nicholls, G. K. and Rodrigo, A.G. and Solomon, W.},
    title = {Estimating mutation parameters, population history and genealogy simultaneously from temporally spaced sequence data},
    journal = {Genetics},
    year = {2002},
    volume = {161},
    number = {3},
    pages = {1307-1320},
    note = {cited By 606},
    document_type = {Article},
    source = {Scopus}
  }
  

• S. Holdaway , P. Fanning , M. Jones , J. Shiner , D. Witter , G. K. Nicholls , Variability in the chronology of late Holocene aboriginal occupation on the arid margin of Southeastern Australia, Journal of Archaeological Science, vol. 29, no. 4, 351–363, 2002.
  

  @article{Holdaway2002351,
    author = {Holdaway, S.J. and Fanning, P.C. and Jones, M. and Shiner, J. and Witter, D.C. and Nicholls, G. K.},
    title = {Variability in the chronology of late Holocene aboriginal occupation on the arid margin of Southeastern Australia},
    journal = {Journal of Archaeological Science},
    year = {2002},
    volume = {29},
    number = {4},
    pages = {351-363},
    doi = {10.1006/jasc.2002.0719},
    note = {cited By 48},
    document_type = {Article},
    source = {Scopus}
  }
  

• A. Philippe , J. Rousseau , . others , Non-informative priors in the case of Gaussian long-memory processes, Bernoulli, vol. 8, no. 4, 451–473, 2002.
  

  @article{philippe2002non,
    title = {Non-informative priors in the case of Gaussian long-memory processes},
    author = {Philippe, Anne and Rousseau, Judith and others},
    journal = {Bernoulli},
    volume = {8},
    number = {4},
    pages = {451--473},
    year = {2002},
    publisher = {Bernoulli Society for Mathematical Statistics and Probability}
  }
  

• J. Rousseau , Asymptotic properties of HPD regions in the discrete case, Journal of multivariate analysis, vol. 83, no. 1, 1–21, 2002.
  

  @article{rousseau2002asymptotic,
    title = {Asymptotic properties of HPD regions in the discrete case},
    author = {Rousseau, Judith},
    journal = {Journal of multivariate analysis},
    volume = {83},
    number = {1},
    pages = {1--21},
    year = {2002},
    publisher = {Elsevier}
  }
  

• Y. W. Teh , M. Welling , The Unified Propagation and Scaling Algorithm, in Advances in Neural Information Processing Systems (NeurIPS), 2002, vol. 14.
  
  In this paper we will show that a restricted class of constrained minimum divergence problems, named generalized inference problems, can be solved by approximating the KL divergence with a Bethe free energy. The algorithm we derive is closely related to both loopy belief propagation and iterative scaling. This unified propagation and scaling algorithm reduces to a convergent alternative to loopy belief propagation when no constraints are present. Experiments show the viability of our algorithm.

  @inproceedings{TehWel2002a,
    author = {Teh, Y. W. and Welling, M.},
    booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
    title = {The Unified Propagation and Scaling Algorithm},
    volume = {14},
    year = {2002},
    bdsk-url-1 = {http://papers.nips.cc/paper/2001-the-unified-propagation-and-scaling-algorithm},
    bdsk-url-2 = {http://papers.nips.cc/paper/2001-the-unified-propagation-and-scaling-algorithm.pdf}
  }
  

• S. Kakade , Y. W. Teh , S. Roweis , An Alternate Objective Function for Markovian Fields, in International Conference on Machine Learning (ICML), 2002, vol. 19.
  
  In labelling or prediction tasks, a trained model’s test performance is often based on the quality of its single-time marginal distributions over labels rather than its joint distribution over label sequences. We propose using a new cost function for discriminative learning that more accurately reflects such test time conditions. We present an efficient method to compute the gradient of this cost for Maximum Entropy Markov Models, Conditional Random Fields, and for an extension of these models involving hidden states. Our experimental results show that the new cost can give significant improvements and that it provides a novel and effective way of dealing with the ’label-bias’ problem.

  @inproceedings{KakTehRow2002a,
    author = {Kakade, S. and Teh, Y. W. and Roweis, S.},
    booktitle = {International Conference on Machine Learning (ICML)},
    title = {An Alternate Objective Function for {M}arkovian Fields},
    volume = {19},
    year = {2002},
    bdsk-url-1 = {http://www.stats.ox.ac.uk/\\~{}teh/research/newcost/icml2002.pdf}
  }
  

2001

• C. Holmes , B. Mallick , Bayesian regression with multivariate linear splines, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol. 63, no. 1, 3–17, 2001.
  

  @article{holmes2001bayesian,
    title = {Bayesian regression with multivariate linear splines},
    author = {Holmes, CC and Mallick, BK},
    journal = {Journal of the Royal Statistical Society: Series B (Statistical Methodology)},
    volume = {63},
    number = {1},
    pages = {3--17},
    year = {2001},
    publisher = {Wiley Online Library}
  }
  

• D. Denison , C. Holmes , Bayesian partitioning for estimating disease risk, Biometrics, vol. 57, no. 1, 143–149, 2001.
  

  @article{denison2001bayesian,
    title = {Bayesian partitioning for estimating disease risk},
    author = {Denison, DGT and Holmes, CC},
    journal = {Biometrics},
    volume = {57},
    number = {1},
    pages = {143--149},
    year = {2001},
    publisher = {Blackwell Publishing Ltd}
  }
  

• S. J. Roberts , C. C. Holmes , D. Denison , Minimum-entropy data partitioning using reversible jump Markov chain Monte Carlo, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 23, no. 8, 909–914, 2001.
  

  @article{roberts2001minimum,
    title = {Minimum-entropy data partitioning using reversible jump Markov chain Monte Carlo},
    author = {Roberts, Stephen J. and Holmes, Chris C and Denison, Dave},
    journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
    volume = {23},
    number = {8},
    pages = {909--914},
    year = {2001},
    publisher = {IEEE}
  }
  

• S. Roberts , C. C. Holmes , D. Denison , Minimum-entropy data clustering using reversible jump markov chain monte carlo, Artificial Neural Networks—ICANN 2001, 103–110, 2001.
  

  @article{roberts2001minimun,
    title = {Minimum-entropy data clustering using reversible jump markov chain monte carlo},
    author = {Roberts, Stephen and Holmes, Chris C and Denison, Dave},
    journal = {Artificial Neural Networks—ICANN 2001},
    pages = {103--110},
    year = {2001},
    publisher = {Springer Berlin/Heidelberg}
  }
  

• C. C. D. L. Holmes , Bayesian methods for nonlinear classification and regression, PhD thesis, Department of Mathematics, Imperial College, 2001.
  

  @phdthesis{holmes2001bayesiao,
    title = {Bayesian methods for nonlinear classification and regression},
    author = {Holmes, Chris C De Lance},
    year = {2001},
    school = {Department of Mathematics, Imperial College}
  }
  

• G. K. Nicholls , M. Jones , Radiocarbon dating with temporal order constraints, Journal of the Royal Statistical Society. Series C: Applied Statistics, vol. 50, no. 4, 503–521, 2001.
  

  @article{Nicholls2001503,
    author = {Nicholls, G. K. and Jones, M.},
    title = {Radiocarbon dating with temporal order constraints},
    journal = {Journal of the Royal Statistical Society. Series C: Applied Statistics},
    year = {2001},
    volume = {50},
    number = {4},
    pages = {503-521},
    note = {cited By 38},
    document_type = {Article},
    source = {Scopus}
  }
  

• M. Jones , G. K. Nicholls , Reservoir offset models for radiocarbon calibration, Radiocarbon, vol. 43, no. 1, 119–124, 2001.
  

  @article{Jones2001119,
    author = {Jones, M. and Nicholls, G. K.},
    title = {Reservoir offset models for radiocarbon calibration},
    journal = {Radiocarbon},
    year = {2001},
    volume = {43},
    number = {1},
    pages = {119-124},
    note = {cited By 16},
    document_type = {Article},
    source = {Scopus}
  }
  

• G. K. Nicholls , Spontaneous magnetization in the plane, Journal of Statistical Physics, vol. 102, no. 5-6, 1229–1251, 2001.
  

  @article{Nicholls20011229,
    author = {Nicholls, G. K.},
    title = {Spontaneous magnetization in the plane},
    journal = {Journal of Statistical Physics},
    year = {2001},
    volume = {102},
    number = {5-6},
    pages = {1229-1251},
    doi = {10.1023/A:1004888211837},
    note = {cited By 6},
    document_type = {Article},
    source = {Scopus}
  }
  

• O. Lieberman , J. Rousseau , D. M. Zucker , Valid Edgeworth expansion for the sample autocorrelation function under long range dependence, Econometric Theory, vol. 17, no. 1, 257–275, 2001.
  

  @article{lieberman2001valid,
    title = {Valid Edgeworth expansion for the sample autocorrelation function under long range dependence},
    author = {Lieberman, Offer and Rousseau, Judith and Zucker, David M},
    journal = {Econometric Theory},
    volume = {17},
    number = {1},
    pages = {257--275},
    year = {2001},
    publisher = {Cambridge University Press}
  }
  

• J. Rousseau , M. Ghosh , D. Kim , Non-informative priors for the bivariate Fieller-Creasy problem, Statistics and Decisions, vol. 19, 227, 2001.
  

  @article{rousseau2001non,
    title = {Non-informative priors for the bivariate Fieller-Creasy problem},
    author = {Rousseau, Judith and Ghosh, M and Kim, D},
    journal = {Statistics and Decisions},
    volume = {19},
    pages = {227},
    year = {2001}
  }
  

• M. Welling , Y. W. Teh , Belief Optimization for Binary Networks : A Stable Alternative to Loopy Belief Propagation, in Uncertainty in Artificial Intelligence (UAI), 2001, vol. 17.
  
  We present a novel inference algorithm for arbitrary, binary, undirected graphs. Unlike loopy belief propagation, which iterates fixed point equations, we directly descend on the Bethe free energy. The algorithm consists of two phases, first we update the pairwise probabilities, given the marginal probabilities at each unit,using an analytic expression. Next, we update the marginal probabilities, given the pairwise probabilities by following the negative gradient of the Bethe free energy. Both steps are guaranteed to decrease the Bethe free energy, and since it is lower bounded, the algorithm is guaranteed to converge to a local minimum. We also show that the Bethe free energy is equal to the TAP free energy up to second order in the weights. In experiments we confirm that when belief propagation converges it usually finds identical solutions as our belief optimization method. However, in cases where belief propagation fails to converge, belief optimization continues to converge to reasonable beliefs. The stable nature of belief optimization makes it ideally suited for learning graphical models from data.

  @inproceedings{WelTeh2001a,
    author = {Welling, M. and Teh, Y. W.},
    booktitle = {Uncertainty in Artificial Intelligence (UAI)},
    title = {Belief Optimization for Binary Networks : A Stable Alternative to Loopy Belief Propagation},
    volume = {17},
    year = {2001},
    bdsk-url-1 = {https://arxiv.org/pdf/1301.2317v1.pdf}
  }
  

• G. E. Hinton , Y. W. Teh , Discovering multiple constraints that are frequently Approximately Satisfied, in Uncertainty in Artificial Intelligence (UAI), 2001, vol. 17, 227–234.
  
  Some high-dimensional data.sets can be modelled by assuming that there are many different linear constraints, each of which is Frequently Approximately Satisfied (FAS) by the data. The probability of a data vector under the model is then proportional to the product of the probabilities of its constraint violations. We describe three methods of learning products of constraints using a heavy-tailed probability distribution for the violations.

  @inproceedings{HinTeh2001a,
    author = {Hinton, G. E. and Teh, Y. W.},
    booktitle = {Uncertainty in Artificial Intelligence (UAI)},
    pages = {227-234},
    title = {Discovering multiple constraints that are frequently Approximately Satisfied},
    volume = {17},
    year = {2001},
    bdsk-url-1 = {https://arxiv.org/pdf/1301.2278v1.pdf}
  }
  

• G. E. Hinton , M. Welling , Y. W. Teh , S. Osindero , A New View of ICA, in Proceedings of the International Conference on Independent Component Analysis and Blind Signal Separation, 2001, vol. 3.
  
  We present a new way of interpreting ICA as a probability density model and a new way of fitting this model to data. The advantage of our approach is that it suggests simple, novel extensions to overcomplete, undercomplete and multilayer non-linear versions of ICA.

  @inproceedings{HinWelTeh2001a,
    author = {Hinton, G. E. and Welling, M. and Teh, Y. W. and Osindero, S.},
    booktitle = {Proceedings of the International Conference on Independent Component Analysis and Blind Signal Separation},
    title = {A New View of {ICA}},
    volume = {3},
    year = {2001},
    bdsk-url-1 = {http://www.stats.ox.ac.uk/\\~{}teh/research/unsup/ica2001.pdf}
  }
  

• Y. W. Teh , G. E. Hinton , Rate-Coded Restricted Boltzmann Machines for Face Recognition, in Advances in Neural Information Processing Systems (NeurIPS), 2001, vol. 13.
  
  We describe a neurally-inspired, unsupervised learning algorithm that builds a non-linear generative model for pairs of face images from the same individual. Individuals are then recognized by finding the highest relative probability pair among all pairs that consist of a test image and an image whose identity is known. Our method compares favorably with other methods in the literature. The generative model consists of a single layer of rate-coded, non-linear feature detectors and it has the property that, given a data vector, the true posterior probability distribution over the feature detector activities can be inferred rapidly without iteration or approximation. The weights of the feature detectors are learned by comparing the correlations of pixel intensities and feature activations in two phases: When the network is observing real data and when it is observing reconstructions of real data generated from the feature activations.

  @inproceedings{TehHin2001a,
    author = {Teh, Y. W. and Hinton, G. E.},
    booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
    title = {Rate-Coded Restricted {Boltzmann} Machines for Face Recognition},
    volume = {13},
    year = {2001},
    bdsk-url-1 = {https://papers.nips.cc/paper/1886-rate-coded-restricted-boltzmann-machines-for-face-recognition},
    bdsk-url-2 = {https://papers.nips.cc/paper/1886-rate-coded-restricted-boltzmann-machines-for-face-recognition.pdf}
  }
  

• Y. W. Teh , M. Welling , Passing and Bouncing Messages for Generalized Inference, Gatsby Computational Neuroscience Unit, University College London, GCNU TR 2001-01, 2001.
  
  @techreport{TehWel2001a,
    author = {Teh, Y. W. and Welling, M.},
    institution = {Gatsby Computational Neuroscience Unit, University College London},
    number = {GCNU TR 2001-01},
    title = {Passing and Bouncing Messages for Generalized Inference},
    year = {2001},
    bdsk-url-1 = {http://www.stats.ox.ac.uk/\\~{}teh/research/inference/bpis2001.pdf}
  }
  

2000

• C. C. Holmes , B. K. Mallick , Bayesian wavelet networks for nonparametric regression, IEEE transactions on neural networks, vol. 11, no. 1, 27–35, 2000.
  

  @article{holmes2000bayesian,
    title = {Bayesian wavelet networks for nonparametric regression},
    author = {Holmes, Chris C and Mallick, Bani K},
    journal = {IEEE transactions on neural networks},
    volume = {11},
    number = {1},
    pages = {27--35},
    year = {2000},
    publisher = {IEEE}
  }
  

• C. Fox , G. K. Nicholls , M. Palm , Efficient solution of boundary-value problems for image reconstruction via sampling, Journal of Electronic Imaging, vol. 9, no. 3, 251–259, 2000.
  

  @article{Colin2000251,
    author = {Fox, C. and Nicholls, G. K. and Palm, M.},
    title = {Efficient solution of boundary-value problems for image reconstruction via sampling},
    journal = {Journal of Electronic Imaging},
    year = {2000},
    volume = {9},
    number = {3},
    pages = {251-259},
    note = {cited By 4},
    document_type = {Article},
    source = {Scopus}
  }
  

• O. Lieberman , J. Rousseau , D. M. Zucker , Small-sample likelihood-based inference in the ARFIMA model, Econometric theory, vol. 16, no. 2, 231–248, 2000.
  

  @article{lieberman2000small,
    title = {Small-sample likelihood-based inference in the ARFIMA model},
    author = {Lieberman, Offer and Rousseau, Judith and Zucker, David M},
    journal = {Econometric theory},
    volume = {16},
    number = {2},
    pages = {231--248},
    year = {2000},
    publisher = {Cambridge University Press}
  }
  

• J. Rousseau , Coverage properties of one-sided intervals in the discrete case and application to matching priors, Annals of the Institute of Statistical Mathematics, vol. 52, no. 1, 28–42, 2000.
  

  @article{rousseau2000coverage,
    title = {Coverage properties of one-sided intervals in the discrete case and application to matching priors},
    author = {Rousseau, Judith},
    journal = {Annals of the Institute of Statistical Mathematics},
    volume = {52},
    number = {1},
    pages = {28--42},
    year = {2000},
    publisher = {Springer}
  }
  

• G. E. Hinton , Z. Ghahramani , Y. W. Teh , Learning to Parse Images, in Advances in Neural Information Processing Systems (NeurIPS), 2000, vol. 12.
  
  We describe a class of probabilistic models that we call credibility networks. Using parse trees as internal representations of images, credibility networks are able to perform segmentation and recognition simultaneously, removing the need for ad hoc segmentation heuristics. Promising results in the problem of segmenting handwritten digits were obtained.

  @inproceedings{HinGhaTeh2000a,
    author = {Hinton, G. E. and Ghahramani, Z. and Teh, Y. W.},
    booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
    title = {Learning to Parse Images},
    volume = {12},
    year = {2000},
    bdsk-url-1 = {https://papers.nips.cc/paper/1710-learning-to-parse-images},
    bdsk-url-2 = {https://papers.nips.cc/paper/1710-learning-to-parse-images.pdf}
  }
  

1999

• A. Guglielmi , C. C. Holmes , S. G. Walker , Perfect simulation involving a continuous and unbounded state space, Preprint, 1999.
  

  @article{guglielmi1999perfect,
    title = {Perfect simulation involving a continuous and unbounded state space},
    author = {Guglielmi, Alessandra and Holmes, Chris C and Walker, Stephen G},
    journal = {Preprint},
    year = {1999},
    publisher = {Citeseer}
  }
  

• C. Holmes , D. Denison , Bayesian wavelet analysis with a model complexity prior, Bayesian statistics, vol. 6, 769–776, 1999.
  

  @article{holmes1999bayesian,
    title = {Bayesian wavelet analysis with a model complexity prior},
    author = {Holmes, CC and Denison, DGT},
    journal = {Bayesian statistics},
    volume = {6},
    pages = {769--776},
    year = {1999},
    publisher = {Citeseer}
  }
  

• C. Holmes , D. Denison , B. Mallick , Bayesian partitioning for classification and regression, Manuscript, Imperial College, 1999.
  

  @article{holmes1999bayesiao,
    title = {Bayesian partitioning for classification and regression},
    author = {Holmes, CC and Denison, DGT and Mallick, BK},
    journal = {Manuscript, Imperial College},
    year = {1999}
  }
  

• C. Holmes , B. Mallick , Generalised nonlinear modelling with multivariate smoothing splines, Unpublished manuscript, Statistics Section, Department of Mathematics, Imperial College of London, 1999.
  

  @article{holmes1999generalised,
    title = {Generalised nonlinear modelling with multivariate smoothing splines},
    author = {Holmes, CC and Mallick, BK},
    journal = {Unpublished manuscript, Statistics Section, Department of Mathematics, Imperial College of London},
    year = {1999}
  }
  

• C. Fox , M. Palm , G. K. Nicholls , Efficient, exact PDE solutions for MCMC, in Proceedings of SPIE - The International Society for Optical Engineering, 1999, vol. 3816, 23–30.
  

  @conference{Fox199923,
    author = {Fox, Colin and Palm, Mathias and Nicholls, Geoff K.},
    title = {Efficient, exact PDE solutions for MCMC},
    journal = {Proceedings of SPIE - The International Society for Optical Engineering},
    year = {1999},
    volume = {3816},
    pages = {23-30},
    note = {cited By 0},
    document_type = {Conference Paper},
    source = {Scopus}
  }
  

1998

• C. Holmes , B. Mallick , Bayesian radial basis functions of variable dimension, Neural Computation, vol. 10, no. 5, 1217–1233, 1998.
  

  @article{holmes1998bayesian,
    title = {Bayesian radial basis functions of variable dimension},
    author = {Holmes, CC and Mallick, BK},
    journal = {Neural Computation},
    volume = {10},
    number = {5},
    pages = {1217--1233},
    year = {1998},
    publisher = {MIT Press}
  }
  

• C. Holmes , B. Mallick , Perfect simulation for orthogonal model mixing, Preprint from http://dbwilson. com/exact, 1998.
  

  @article{holmes1998perfect,
    title = {Perfect simulation for orthogonal model mixing},
    author = {Holmes, C and Mallick, B},
    journal = {Preprint from http://dbwilson. com/exact},
    year = {1998}
  }
  

• C. Holmes , B. Mallick , Parallel Markov chain Monte Carlo sampling: an evolutionary based approach, London, Imperial College, 1998.
  

  @article{holmes1998parallel,
    title = {Parallel Markov chain Monte Carlo sampling: an evolutionary based approach},
    author = {Holmes, CC and Mallick, BK},
    journal = {London, Imperial College},
    year = {1998}
  }
  

• R. Webster , A. Lawson , C. Glasbey , G. Horgan , D. Elston , G. Host , M. Mugglestone , M. G. Kenward , J. Kent , A. Stein , . others , Model-based geostatistics-Discussion, 1998.
  

  @article{webster1998model,
    title = {Model-based geostatistics-Discussion},
    author = {Webster, Roberta and Lawson, Andrew and Glasbey, C and Horgan, G and Elston, D and Host, G and Mugglestone, M and Kenward, Michael G and Kent, Jacqueline and Stein, A and others},
    year = {1998}
  }
  

• G. K. Nicholls , Bayesian image analysis with Markov chain Monte Carlo and coloured continuum triangulation models, Journal of the Royal Statistical Society. Series B: Statistical Methodology, vol. 60, no. 3, 643–659, 1998.
  

  @article{Nicholls1998643,
    author = {Nicholls, G. K.},
    title = {Bayesian image analysis with Markov chain Monte Carlo and coloured continuum triangulation models},
    journal = {Journal of the Royal Statistical Society. Series B: Statistical Methodology},
    year = {1998},
    volume = {60},
    number = {3},
    pages = {643-659},
    note = {cited By 17},
    document_type = {Article},
    source = {Scopus}
  }
  

• C. Fox , G. K. Nicholls , Physically based likelihood for ultrasound imaging, in Proceedings of SPIE - The International Society for Optical Engineering, 1998, vol. 3459, 92–99.
  

  @conference{Fox199892,
    author = {Fox, C. and Nicholls, G. K.},
    title = {Physically based likelihood for ultrasound imaging},
    journal = {Proceedings of SPIE - The International Society for Optical Engineering},
    year = {1998},
    volume = {3459},
    pages = {92-99},
    doi = {10.1117/12.323788},
    note = {cited By 2},
    document_type = {Conference Paper},
    source = {Scopus}
  }
  

• G. K. Nicholls , C. Fox , Prior modeling and posterior sampling in impedance imaging, in Proceedings of SPIE - The International Society for Optical Engineering, 1998, vol. 3459, 116–127.
  

  @conference{Nicholls1998116,
    author = {Nicholls, G. K. and Fox, C.},
    title = {Prior modeling and posterior sampling in impedance imaging},
    journal = {Proceedings of SPIE - The International Society for Optical Engineering},
    year = {1998},
    volume = {3459},
    pages = {116-127},
    doi = {10.1117/12.323791},
    note = {cited By 28},
    document_type = {Conference Paper},
    source = {Scopus}
  }
  

• F. Bacchus , Y. W. Teh , Making Forward Chaining Relevant, in Proceedings of the International Conference on Artificial Intelligence Planning Systems, 1998.
  
  Planning by forward chaining through the world space has long been dismissed as being “obviously” infeasible. Nevertheless, this approach to planning has many advantages. Most importantly forward chaining planners maintain complete descriptions of the intermediate states that arise during the course of the plan’s execution. These states can be utilized to provide highly effective search control. Another advantage is that such planners can support richer planning representations that can model, e.g., resources and resource consumption. Forward chaining planners are still plagued however by their traditional weaknesses: a lack of goal direction, and the fact that they search totally ordered action sequences. In this paper we address the issue of goal direction. We present two algorithms that provide a forward chaining planner with more information about the goal, and allow it to avoid certain types of irrelevant state information and actions.

  @inproceedings{BacTeh1998a,
    author = {Bacchus, F. and Teh, Y. W.},
    booktitle = {Proceedings of the International Conference on Artificial Intelligence Planning Systems},
    title = {Making Forward Chaining Relevant},
    year = {1998},
    bdsk-url-1 = {http://www.stats.ox.ac.uk/\\~{}teh/research/planning/aips98.pdf}
  }
  

1997

• C. Holmes , B. Mallick , Bayesian radial basis functions of unknown dimension, Imperial College Report, 1997.
  

  @article{holmes1997bayesian,
    title = {Bayesian radial basis functions of unknown dimension},
    author = {Holmes, CC and Mallick, BK},
    journal = {Imperial College Report},
    year = {1997}
  }
  

1992

• C. Holmes , M. Bosse , C. McLaughlin , S. Buckley , A. Jones , R. Culp , T. Smallman , The US Navy Hospital Ships: The Orthopaedic Capabilities and the Preparations for War., Journal of Orthopaedic Trauma, vol. 6, no. 4, 490, 1992.
  

  @article{holmes1992us,
    title = {The US Navy Hospital Ships: The Orthopaedic Capabilities and the Preparations for War.},
    author = {Holmes, CA and Bosse, MJ and McLaughlin, CS and Buckley, SL and Jones, AA and Culp, RW and Smallman, T},
    journal = {Journal of Orthopaedic Trauma},
    volume = {6},
    number = {4},
    pages = {490},
    year = {1992},
    publisher = {LWW}
  }
  

• S. Buckley , A. Jones , M. Bosse , C. Holmes , R. Culp , T. Smallman , C. McLaughlin , Arthroscopic surgery of the knee on the US Naval Hospital Ships during Operation Desert Shield., Military medicine, vol. 157, no. 9, 441–443, 1992.
  

  @article{buckley1992arthroscopic,
    title = {Arthroscopic surgery of the knee on the US Naval Hospital Ships during Operation Desert Shield.},
    author = {Buckley, SL and Jones, AA and Bosse, MJ and Holmes, CA and Culp, RW and Smallman, T and McLaughlin, CM},
    journal = {Military medicine},
    volume = {157},
    number = {9},
    pages = {441--443},
    year = {1992}
  }