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.
  • T. Rainforth , A. Foster , D. R. Ivanova , F. Bickford Smith , Modern Bayesian experimental design, Statistical Science (to appear), 2023.
  • S. Bouabid , J. Fawkes , D. Sejdinovic , Returning The Favour: When Regression Benefits From Probabilistic Causal Knowledge, arXiv preprint arXiv:2301.11214, 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.
  • R. J. Evans , V. Didelez , Parameterizing and simulating from causal models, Journal of the Royal Statistical Society, Series B (with discussion), 2023.
  • R. J. Evans , Latent-free equivalent mDAGs, Algebraic Statistics, 2023.
  • T. Reichelt , L. Ong , T. Rainforth , Pitfalls of Full Bayesian Inference in Universal Probabilistic Programming, in POPL Workshop on Languages for Inference (LAFI), 2023.

2022

  • C. U. Carmona , G. Nicholls , Scalable Semi-Modular Inference with Variational Meta-Posteriors, Apr. 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.
  • 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.
  • S. Bouabid , D. Watson-Parris , D. Sejdinovic , Bayesian inference for aerosol vertical profiles, in NeurIPS Workshop on Tackling Climate Change with Machine Learning, 2022.
  • S. Bouabid , D. Watson-Parris , S. Stefanović , A. Nenes , D. Sejdinovic , AODisaggregation: toward global aerosol vertical profiles, arXiv preprint arXiv:2205.04296, 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.
  • 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.
  • Y. Shi , V. De Bortoli , G. Deligiannidis , A. Doucet , Conditional Simulation Using Diffusion Schr\backslash" odinger Bridges, arXiv preprint arXiv:2202.13460, 2022.
  • E. Clerico , A. Shidani , G. Deligiannidis , A. Doucet , Chained Generalisation Bounds, in COLT 2022, 2022, no. arXiv:2203.00977.
  • 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.
  • A. Shidani , G. Deligiannidis , A. Doucet , Ranking in Contextual Multi-Armed Bandits, arXiv preprint arXiv:2207.00109, 2022.
  • E. Clerico , G. Deligiannidis , B. Guedj , A. Doucet , A PAC-Bayes bound for deterministic classifiers, arXiv preprint arXiv:2209.02525, 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.
  • J. Fawkes , R. J. Evans , D. Sejdinovic , Selection, ignorability and challenges with causal fairness, in Conference on Causal Learning and Reasoning, 2022, 275–289.
  • B. Yao , R. J. Evans , Algebraic properties of HTC-identifiable graphs, Algebraic Statistics, vol. 13, no. 1, 19–39, 2022.
  • 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.
  • J. Fawkes , R. Evans , D. Sejdinovic , Selection, Ignorability and Challenges With Causal Fairness, arXiv preprint arXiv:2202.13774, 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.
  • T. Reichelt , L. Ong , T. Rainforth , Rethinking Variational Inference for Probabilistic Programs with Stochastic Support, in Advances in Neural Information Processing Systems, 2022.
  • 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.

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.
  • 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.
  • 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.
  • 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.
  • 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.
  • E. Clerico , G. Deligiannidis , A. Doucet , Wide stochastic networks: Gaussian limit and PAC-Bayesian training, arXiv preprint arXiv:2106.09798, 2021.
  • G. Deligiannidis , V. De Bortoli , A. Doucet , Quantitative uniform stability of the iterative proportional fitting procedure, arXiv preprint arXiv:2108.08129, 2021.
  • E. Clerico , G. Deligiannidis , A. Doucet , Conditional Gaussian PAC-Bayes, in Accepted at AISTATS 2022, 2021, no. arXiv preprint arXiv:2110.11886.
  • 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.
  • R. J. Evans , Dependency in DAG models with hidden variables, in Uncertainty in Artificial Intelligence, 2021, 813–822.
  • 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.
  • F. Falck , H. Zhang , M. Willetts , G. Nicholson , C. Yau , C. Holmes , Multi-Facet Clustering Variational Autoencoders, Advances in Neural Information Processing Systems, 2021.
  • T. Farghly , P. Rebeschini , Time-independent Generalization Bounds for SGLD in Non-convex Settings, in Advances in Neural Information Processing Systems 34, 2021.
  • E. Fong , C. Holmes , S. G. Walker , Martingale Posterior Distributions, arXiv preprint arXiv:2103.15671, 2021.
  • A. Foster , R. Pukdee , T. Rainforth , Improving Transformation Invariance in Contrastive Representation Learning, International Conference on Learning Representations (ICLR), 2021.
  • 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.
  • E. Mathieu , A. Foster , Y. W. Teh , On Contrastive Representations of Stochastic Processes, 35th Conference on Neural Information Processing Systems (NeurIPS 2021), 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.
  • 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.
  • J. Kossen , S. Farquhar , Y. Gal , T. Rainforth , Active Testing: Sample-Efficient Model Evaluation, International Conference on Machine Learning, 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • J. E. Lee , G. K. Nicholls , Tree based credible set estimation, Statistics and Computing, vol. 31, 69, 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.
  • 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.
  • S. L. Chau , S. Bouabid , D. Sejdinovic , Deconditional Downscaling with Gaussian Processes, in Advances in Neural Information Processing Systems (NeurIPS), 2021.
  • 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.
  • R. Hu , G. K. Nicholls , D. Sejdinovic , Large Scale Tensor Regression using Kernels and Variational Inference, Machine Learning, 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.
  • 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.
  • A. Caterini , R. Cornish , D. Sejdinovic , A. Doucet , Variational Inference with Continuously-Indexed Normalizing Flows, in Uncertainty in Artificial Intelligence (UAI), 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • R. Hu , D. Sejdinovic , Robust Deep Interpretable Features for Binary Image Classification, in Proceedings of the Northern Lights Deep Learning Workshop, 2021, vol. 2.
  • 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.
  • A. Campbell , Y. Shi , T. Rainforth , A. Doucet , Online Variational Filtering and Parameter Learning, in Advances in Neural Information Processing Systems, 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.
  • M. Willetts , A. Camuto , T. Rainforth , S. Roberts , C. Holmes , Improving VAEs’ Robustness to Adversarial Attack, in International Conference on Learning Representations (ICLR), 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.
  • 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.
  • A. Camuto , M. Willetts , Variational Autoencoders: A Harmonic Perspective, in arXiv preprint, 2021.
  • B. Barrett , A. Camuto , M. Willetts , T. Rainforth , Certifiably Robust Variational Autoencoders , in arXiv preprint, 2021.
  • M. Willetts , B. Paige , I Don’t Need u: Identifiable Non-Linear ICA Without Side Information, in arXiv preprint, 2021.
  • J. Xu , H. Kim , T. Rainforth , Y. W. Teh , Group Equivariant Subsampling, in Neural Information Processing Systems (NeurIPS), 2021.
    Project: tencent-lsml

2020

  • S. M. Schmon , G. Deligiannidis , A. Doucet , M. K. Pitt , Large-sample asymptotics of the pseudo-marginal method, Biometrika, Jul. 2020.
  • 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.
  • 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.
  • F. Faizi , P. Buigues , G. Deligiannidis , E. Rosta , Simulated tempering with irreversible Gibbs sampling techniques, Journal of Chemical Physics, vol. 153, no. 21, 2020.
  • F. Faizi , G. Deligiannidis , E. Rosta , Efficient Irreversible Monte Carlo Samplers, Journal of Chemical Theory and Computation, vol. 16, no. 4, 2124–2138, 2020.
  • S. Schmon , A. Doucet , G. Deligiannidis , Bernoulli race particle filters, in AISTATS 2019 - 22nd International Conference on Artificial Intelligence and Statistics, 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.
  • J. Heng , A. Bishop , G. Deligiannidis , A. Doucet , Controlled sequential monte carlo, Annals of Statistics, vol. 48, no. 5, 2904–2929, 2020.
  • 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.
  • S. Schmon , G. Deligiannidis , A. Doucet , M. Pitt , Large sample asymptotics of the pseudo-marginal method, Biometrika, 2020.
  • G. Deligiannidis , A. Doucet , S. Rubenthaler , Ensemble Rejection Sampling, aarXiv:2001.0988, 2020.
  • R. Cornish , A. Caterini , G. Deligiannidis , A. Doucet , Relaxing bijectivity constraints with continuously indexed normalising flows, in ICML, 2020, 2133–2143.
  • S. Hayou , E. Clerico , B. He , G. Deligiannidis , A. Doucet , J. Rousseau , Stable ResNet, AISTATS 2021, 2020.
  • U. Simsekli , O. Sener , G. Deligiannidis , M. Erdogdu , Hausdorff Dimension, Heavy Tails, and Generalization in Neural Networks, NeurIPS (Spotlight), vol. 33, 2020.
  • R. J. Evans , Model selection and local geometry, Annals of Statistics, no. 6, 3514–3544, 2020.
  • 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.
  • E. Fong , C. Holmes , On the marginal likelihood and cross-validation, Biometrika, vol. 107, no. 2, 489–496, 2020.
  • 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
  • K. Märtens , C. Yau , BasisVAE: Translation-invariant feature-level clustering with Variational Autoencoders, International Conference on Artificial Intelligence and Statistics (AISTATS), 2020.
  • K. Märtens , C. Yau , Neural Decomposition: Functional ANOVA with Variational Autoencoders, International Conference on Artificial Intelligence and Statistics (AISTATS), 2020.
  • E. Mathieu , M. Nickel , Riemannian Continuous Normalizing Flows, in Advances in Neural Information Processing Systems 33, 2020.
  • 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.
  • 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.
  • M. Roeling , G. K. Nicholls , Imputation of attributes in networked data using Bayesian Autocorrelation Regression Models, Social Networks, vol. 62, 24–32, 2020.
  • 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.
  • 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.
  • 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.
  • T. Joy , S. M. Schmon , P. Torr , S. Narayanaswamy , T. Rainforth , Rethinking Semi–Supervised Learning in VAEs, https://arxiv.org/abs/2006.10102, 2020.
  • S. Groha , S. M. Schmon , A. Gusev , Neural ODEs for Multi-state Survival Analysis, https://arxiv.org/abs/2006.04893, 2020.
  • S. M. Schmon , P. W. Cannon , J. Knoblauch , Generalized Posteriors in Approximate Bayesian Computation. 2020.
  • 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.
  • 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.
  • 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.
  • T. Rudner , D. Sejdinovic , Y. Gal , Inter-domain Deep Gaussian Processes, in International Conference on Machine Learning (ICML), 2020, PMLR 119:8286–8294.
  • 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.
  • J. Amersfoort , L. Smith , Y. W. Teh , Y. Gal , Uncertainty Estimation Using a Single Deep Deterministic Neural Network, International Conference on Machine Learning, 2020.
  • M. Willetts , X. Miscouridou , S. Roberts , C. Holmes , Relaxed-Responsibility Hierarchical Discrete VAEs, arXiv preprint, 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.
  • 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.
  • 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
  • D. Tolpin , Y. Zhou , H. Yang , Stochastically Differentiable Probabilistic Programs, arXiv preprint arXiv:2003.00704, 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.
  • P. Rebeschini , S. Tatikonda , Locality in Network Optimization, IEEE Transactions on Control of Network Systems, vol. 6, no. 2, 487–500, Jun. 2019.
  • 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.
  • 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
  • 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
  • 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
  • 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.
  • 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.
  • 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. 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.
  • 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
  • B. Bloem-Reddy , Y. W. Teh , Probabilistic symmetry and invariant neural networks, Jan. 2019.
    Project: bigbayes
  • F. Ayed , F. Caron , Nonnegative Bayesian nonparametric factor models with completely random measures for community detection, 2019.
  • F. Ayed , J. Lee , F. Caron , Beyond the Chinese Restaurant and Pitman-Yor processes: Statistical Models with Double Power-law Behavior, 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.
  • 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.
  • 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.
  • R. J. Evans , T. Richardson , Smooth, identifiable supermodels of discrete DAG models with latent variables, Bernoulli, vol. 25, no. 2, 848–876, 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.
  • 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
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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
  • 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
  • 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.
  • 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.
  • 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.
  • 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
  • C. Naik , F. Caron , J. Rousseau , Sparse Networks with Core-Periphery Structure, 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.
  • 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.
  • J. E. Lee , G. K. Nicholls , R. Ryder , Calibration procedures for approximate Bayesian credible sets, Bayesian Analysis, vol. 14, 1245–1269, 2019.
  • 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.
  • 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.
  • F. Camerlenghi , S. Favaro , Z. Naulet , F. Panero , Optimal disclosure risk assessment, under revision at the Annals of Statistics, 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
  • 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
  • A. Golinski , F. Wood , T. Rainforth , Amortized Monte Carlo Integration, International Conference on Machine Learning (ICML, Best Paper honorable mention), 2019.
    Project: bigbayes
  • 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
  • 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
  • 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.
  • 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.
  • 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.
  • P. Rebeschini , S. Tatikonda , A new approach to Laplacian solvers and flow problems, Journal of Machine Learning Research, vol. 20, no. 36, 2019.
  • 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.
  • 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.
  • 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.
  • S. M. Schmon , G. Deligiannidis , A. Doucet , Bernoulli Race Particle Filters, AISTATS, 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.
  • 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.
  • 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.
  • 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.
  • D. Rindt , D. Sejdinovic , D. Steinsaltz , Nonparametric Independence Testing for Right-Censored Data using Optimal Transport, ArXiv e-prints:1906.03866, 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
  • G. Camps-Valls , D. Sejdinovic , J. Runge , M. Reichstein , A Perspective on Gaussian Processes for Earth Observation, National Science Review, 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.
  • 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.
  • 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.
  • A. Kirsch , J. Amersfoort , Y. Gal , BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learning, Advances in Neural Information Processing Systems, 2019.
  • M. Willetts , S. Roberts , C. Holmes , Disentangling to Cluster: Gaussian Mixture Variational Ladder Autoencoders, in NeurIPS Bayesian Deep Learning Workshop, 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
  • 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

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.
  • 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
  • A. Golinski , Y. W. Teh , F. Wood , T. Rainforth , Amortized Monte Carlo Integration, in Symposium on Advances in Approximate Bayesian Inference, 2018.
    Project: bigbayes
  • J. Mitrovic , D. Sejdinovic , Y. Teh , Causal Inference via Kernel Deviance Measures, in Advances in Neural Information Processing Systems (NeurIPS), 2018.
    Project: bigbayes
  • 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
  • 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
  • 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
  • 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
  • X. Miscouridou , A. Perotte , N. Elhadad , R. Ranganath , Deep Survival Analysis: Nonparametrics and Missingness, in pmlr, 2018.
    Project: bigbayes
  • 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
  • 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
  • R. van den Berg , L. Hasenclever , J. M. Tomczak , M. Welling , Sylvester Normalizing Flows for Variational Inference, Mar-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
  • F. Ayed , M. Battiston , F. Camerlenghi , S. Favaro , Consistent estimation of the missing mass for feature models, 2018.
    Project: bigbayes
  • M. Battiston , S. Favaro , Y. W. Teh , Bayesian nonparametric approaches to sample-size estimation for finding unseen species, 2018.
    Project: bigbayes
  • F. Ayed , M. Battiston , F. Camerlenghi , S. Favaro , On the consistent estimation of the missing mass, 2018.
    Project: bigbayes
  • F. Ayed , M. Battiston , F. Camerlenghi , S. Favaro , On the Good-Turing estimator for feature allocation models, 2018.
    Project: bigbayes
  • B. Bloem-Reddy , Y. W. Teh , Neural network models of exchangeable sequences, NeurIPS Workshop on Bayesian Deep Learning, 2018.
    Project: bigbayes
  • A. Caterini , A. Doucet , D. Sejdinovic , Hamiltonian Variational Auto-Encoder, in Advances in Neural Information Processing Systems (NeurIPS), 2018, to appear.
  • A. Caterini , D. E. Chang , Deep Neural Networks in a Mathematical Framework. Springer, 2018.
  • G. Deligiannidis , A. Doucet , M. Pitt , The correlated pseudomarginal method, JRSSB, vol. 80, no. 5, 839–870, 2018.
  • G. Deligiannidis , A. Lee , Which ergodic averages have finite asymptotic variance?, Annals of Applied Probability, vol. 28, no. 4, 2309–2334, 2018.
  • E. Dupont , S. Suresha , Probabilistic Semantic Inpainting with Pixel Constrained CNNs, arXiv preprint arXiv:1810.03728, 2018.
  • E. Dupont , Learning Disentangled Joint Continuous and Discrete Representations, in Advances in Neural Information Processing Systems, 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.
  • R. J. Evans , Margins of discrete Bayesian networks, Annals of Statistics, vol. 46, no. 6A, 2623–2656, 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.
  • C. Loeffler , S. Flaxman , Is gun violence contagious? A spatiotemporal test, Journal of Quantitative Criminology, vol. 34, no. 4, 999–1017, 2018.
    Project: bigbayes
  • 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
  • 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
  • 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.
  • 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.
  • 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
  • H. Kim , A. Mnih , Disentangling by Factorising, in International Conference on Machine Learning (ICML), 2018.
    Project: bigbayes
  • T. A. Le , A. R. Kosiorek , N. Siddharth , Y. W. Teh , F. Wood , Revisiting Reweighted Wake-Sleep, CoRR, vol. abs/1805.10469, 2018.
  • 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.
  • 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
  • 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
  • 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
  • E. Pompe , C. Holmes , K. Łatuszyński , A Framework for Adaptive MCMC Targeting Multimodal Distributions, arXiv preprint arXiv:1812.02609, 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
  • 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
  • T. Rainforth , Nesting Probabilistic Programs, Conference on Uncertainty in Artificial Intelligence (UAI), 2018.
    Project: bigbayes
  • T. Rainforth , R. Cornish , H. Yang , A. Warrington , F. Wood , On Nesting Monte Carlo Estimators, International Conference on Machine Learning (ICML), 2018.
    Project: bigbayes
  • T. A. Le , M. Igl , T. Rainforth , T. Jin , F. Wood , Auto-Encoding Sequential Monte Carlo, in International Conference on Learning Representations, 2018.
  • D. T. Frazier , G. M. Martin , C. P. Robert , J. Rousseau , Asymptotic properties of approximate Bayesian computation, arXiv preprint arXiv:1607.06903, 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.
  • 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.
  • 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.
  • H. C. L. Law , P. Zhao , J. Huang , D. Sejdinovic , Hyperparameter Learning via Distributional Transfer, ArXiv e-prints:1810.06305, 2018.
    Project: tencent-lsml
  • 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.
  • 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
  • H. Law , D. Sutherland , D. Sejdinovic , S. Flaxman , Bayesian Approaches to Distribution Regression, in Artificial Intelligence and Statistics (AISTATS), 2018.
    Project: bigbayes
  • 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.
  • M. Willetts , S. Roberts , C. Holmes , Semi-Unsupervised Learning using Deep Generative Models, in NeurIPS Bayesian Deep Learning Workshop, 2018.
  • M. Willetts , A. Doherty , S. Roberts , C. Holmes , Semi-Unsupervised Learning of Human Activity using Deep Generative Models, in NeurIPS ML4Health Workshop, 2018.

2017

  • N. Dhir , A. R. Kosiorek , I. Posner , Bayesian Delay Embeddings for Dynamical Systems, in NIPS Timeseries Workshop, 2017.
  • A. R. Kosiorek , A. Bewley , I. Posner , Hierarchical Attentive Recurrent Tracking, in Neural Information Processing Systems, 2017.
  • 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
  • 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
  • G. Di Benedetto , F. Caron , Y. W. Teh , Non-exchangeable random partition models for microclustering, Nov-2017.
    Project: bigbayes
  • B. Bloem-Reddy , P. Orbanz , Preferential Attachment and Vertex Arrival Times, Oct. 2017.
    Project: bigbayes
  • 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
  • A. Todeschini , X. Miscouridou , F. Caron , Exchangeable Random Measures for Sparse and Modular Graphs with Overlapping Communities, Aug-2017.
    Project: bigbayes
  • T. Nagapetyan , A. B. Duncan , L. Hasenclever , S. J. Vollmer , L. Szpruch , K. Zygalakis , The True Cost of Stochastic Gradient Langevin Dynamics, Jun-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.
  • 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.
  • 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.
  • 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.
  • 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
  • X. Lu , V. Perrone , L. Hasenclever , Y. W. Teh , S. J. Vollmer , Relativistic Monte Carlo, in Artificial Intelligence and Statistics (AISTATS), 2017.
    Project: bigbayes
  • 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.
  • 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
  • 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
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • A. Caterini , A Novel Mathematical Framework for the Analysis of Neural Networks, Master's thesis, University of Waterloo, 2017.
  • 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.
  • P. Vanetti , A. Bouchard-Côté , G. Deligiannidis , A. Doucet , Piecewise-Deterministic Markov Chain Monte Carlo, arXiv preprint arXiv:1707.05296, 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.
  • 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.
  • 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
  • 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
  • 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.
  • S. Filippi , C. C. Holmes , . others , A Bayesian nonparametric approach to testing for dependence between random variables, Bayesian Analysis, 2017.
  • 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.
  • 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.
  • 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.
  • C. Holmes , S. Walker , Assigning a value to a power likelihood in a general Bayesian model, Biometrika, vol. 104, no. 2, 497–503, 2017.
  • T. Rukat , C. C. Holmes , M. K. Titsias , C. Yau , Bayesian Boolean Matrix Factorisation, arXiv preprint arXiv:1702.06166, 2017.
  • P. M. Esperança , L. J. Aslett , C. C. Holmes , Encrypted accelerated least squares regression, arXiv preprint arXiv:1703.00839, 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.
  • 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.
  • A. Doucet , C. Holmes , R. Bardenet , On Markov chain Monte Carlo Methods for Tall Data, 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.
  • 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.
  • 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
  • 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.
  • L. J. Kelly , G. K. Nicholls , Lateral transfer in Stochastic Dollo Models, The Annals of Applied Statistics, vol. 11, 1146–1168, 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
  • 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
  • T. Rainforth , Automating Inference, Learning, and Design using Probabilistic Programming, PhD thesis, University of Oxford, 2017.
  • B. T. Vincent , T. Rainforth , The DARC Toolbox: automated, flexible, and efficient delayed and risky choice experiments using Bayesian adaptive design, 2017.
  • 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
  • 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.
  • F. Caron , J. Rousseau , On sparsity and power-law properties of graphs based on exchangeable point processes, arXiv preprint arXiv:1708.03120, 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.
  • 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.
  • T. G. J. Rudner , D. Sejdinovic , Inter-domain Deep Gaussian Processes, NeurIPS 2017 Workshop on Bayesian Deep Learning, 2017.
  • T. Rukat , C. C. Holmes , M. K. Titsias , C. Yau , Bayesian Boolean Matrix Factorisation, 2017.
  • 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.
  • 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
  • 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
  • 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
  • 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.
  • 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.
  • 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.
  • S. Flaxman , Y. W. Teh , D. Sejdinovic , Poisson Intensity Estimation with Reproducing Kernels, in Artificial Intelligence and Statistics (AISTATS), 2017.
    Project: bigbayes
  • 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.
  • 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
  • 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
  • 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.

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.
  • 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.
  • 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.
  • 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
  • K. Palla , F. Caron , Y. W. Teh , A Bayesian nonparametric model for sparse dynamic networks, Jun-2016.
    Project: bigbayes
  • P. Rebeschini , S. Tatikonda , Decay of correlation in network flow problems, in 2016 Annual Conference on Information Science and Systems (CISS), 2016, 169–174.
  • 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.
  • 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.
  • 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.
  • B. Bloem-Reddy , J. P. Cunningham , Slice Sampling on Hamiltonian Trajectories, in International Conference on Machine Learning (ICML), 2016, vol. 33, 3050–3058.
  • 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.
  • A. Caterini , D. E. Chang , A Geometric Framework for Convolutional Neural Networks, ArXiv e-prints:1608.04374, 2016.
  • G. Deligiannidis , S. Utev , Optimal Bounds for the Variance of Self-Intersection Local Times, International Journal of Stochastic Analysis, vol. 2016, 2016.
  • R. J. Evans , Graphs for margins of Bayesian networks, Scandinavian Journal of Statistics, vol. 43, no. 3, 625–648, 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.
  • 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.
  • 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.
  • 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.
  • S. Filippi , C. C. Holmes , A Bayesian Nonparametric Approach to Testing for Dependence Between Random Variables, Bayesian Analysis, 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
  • 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.
  • 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.
  • J. Watson , C. C. Holmes , . others , Approximate models and robust decisions, Statistical Science, vol. 31, no. 4, 465–489, 2016.
  • 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.
  • 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.
  • 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.
  • M. Behr , C. C. Holmes , A. Munk , Multiscale Blind Source Separation, arXiv preprint arXiv:1608.07173, 2016.
  • J. Watson , C. C. Holmes , . others , Rejoinder: Approximate Models and Robust Decisions, Statistical Science, vol. 31, no. 4, 516–520, 2016.
  • H. Kim , X. Lu , S. Flaxman , Y. W. Teh , Collaborative Filtering with Side Information: a Gaussian Process Perspective, 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • N. Heard , K. Palla , M. Skoularidou , Topic modelling of authentication events in an enterprise computer network, 2016.
    Project: bigbayes
  • 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.
  • T. Rainforth , R. Cornish , H. Yang , F. Wood , On the Pitfalls of Nested Monte Carlo, NIPS Workshop on Advances in Approximate Bayesian Inference, 2016.
  • 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.
  • 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.
  • J. Rousseau , On the frequentist properties of bayesian nonparametric methods, Annual Review of Statistics and Its Application, vol. 3, 211–231, 2016.
  • 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.
  • E. Gassiat , J. Rousseau , . others , Nonparametric finite translation hidden Markov models and extensions, Bernoulli, vol. 22, no. 1, 193–212, 2016.
  • P. Rubin-Delanchy , D. J. Lawson , N. A. Heard , Anomaly detection for cyber-security applications, in Dynamic Networks and Cybersecurity, London: World Scientific, 2016.
  • P. Rubin-Delanchy , N. A. Heard , On the mid-p-value of a test statistic with arbitrary real support, arXiv preprint:1505.05068, 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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
  • 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.
  • B. Paige , D. Sejdinovic , F. Wood , Super-Sampling with a Reservoir, in Uncertainty in Artificial Intelligence (UAI), 2016, 567–576.
  • S. Flaxman , D. Sejdinovic , J. Cunningham , S. Filippi , Bayesian Learning of Kernel Embeddings, in Uncertainty in Artificial Intelligence (UAI), 2016, 182–191.
    Project: bigbayes
  • M. Park , W. Jitkrittum , D. Sejdinovic , K2-ABC: Approximate Bayesian Computation with Kernel Embeddings, in Artificial Intelligence and Statistics (AISTATS), 2016, 398–407.
  • T. Fernandez , Y. W. Teh , Posterior Consistency for a Non-parametric Survival Model under a Gaussian Process Prior, 2016.
    Project: bigbayes
  • T. Fernandez , N. Rivera , Y. W. Teh , Gaussian Processes for Survival Analysis, in Advances in Neural Information Processing Systems (NeurIPS), 2016.
    Project: bigbayes
  • 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
  • L. T. Elliott , Y. W. Teh , A Nonparametric HMM for Genetic Imputation and Coalescent Inference, Electronic Journal of Statistics, 2016.
    Project: bigbayes
  • 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
  • Y. W. Teh , Bayesian Nonparametric Modelling and the Ubiquitous Ewens Sampling Formula, Statistical Science, vol. 31, no. 1, 34–36, 2016.
    Project: bigbayes
  • M. Balog , B. Lakshminarayanan , Z. Ghahramani , D. M. Roy , Y. W. Teh , The Mondrian Kernel, in Uncertainty in Artificial Intelligence (UAI), 2016.
    Project: bigbayes
  • 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
  • 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
  • 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
  • D. Glowacka , Y. W. Teh , J. Shawe-Taylor , Image Retrieval with a Bayesian Model of Relevance Feedback, 2016.
  • K. Palla , F. Caron , Y. W. Teh , Bayesian Nonparametrics for Sparse Dynamic Networks, 2016.
    Project: bigbayes
  • 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

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.
  • 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.
  • P. Rebeschini , R. Handel , Can local particle filters beat the curse of dimensionality?, Ann. Appl. Probab., vol. 25, no. 5, 2809–2866, Oct. 2015.
  • M. Hoffmann , J. Rousseau , J. Schmidt-Hieber , On adaptive posterior concentration rates, Ann. Statist., vol. 43, no. 5, 2259–2295, Oct. 2015.
  • T. Rukat , Distributed analysis of expression quantitative trait loci in Apache Spark, Jul-2015.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • G. Deligiannidis , M. Peligrad , S. Utev , Asymptotic Variance of Stationary Reversible and Normal Markov Processes, Electronic Journal of Probability, vol. 20, 2015.
  • 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.
  • R. J. Evans , Conditional distributions and log-linear parameters, Electronic Journal of Statistics, vol. 9, no. 1, 475–491, 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.
  • B. J. Gram-Hansen , Electron-Proton Entanglement in the Hydrogen Atom, Master's thesis, 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.
  • R. Bardenet , A. Doucet , C. C. Holmes , On Markov chain Monte Carlo methods for tall data, arXiv preprint arXiv:1505.02827, 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.
  • M. Rantalainen , C. M. Lindgren , C. C. Holmes , Robust Linear Models for Cis-eQTL Analysis, PloS one, vol. 10, no. 5, e0127882, 2015.
  • 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.
  • L. J. Aslett , P. M. Esperança , C. C. Holmes , Encrypted statistical machine learning: new privacy preserving methods, arXiv preprint arXiv:1508.06845, 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.
  • C. C. Drovandi , C. C. Holmes , J. McGree , K. Mengersen , S. Richardson , E. Ryan , A principled experimental design approach to Big Data analysis, 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.
  • 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.
  • R. Bardenet , A. Doucet , C. Holmes , Markov chain Monte Carlo and tall data, preprint, 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.
  • J. Lee , S. Choi , Tree-guided MCMC inference for normalized random measure mixture models, in Advances in Neural Information Processing Systems (NeurIPS), 2015.
  • J. Lee , S. Choi , Bayesian hierarchical clustering with exponential family: small-variance asymptotics and reducibility, in Artificial Intelligence and Statistics (AISTATS), 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.
  • T. Rainforth , F. Wood , Canonical Correlation Forests, arXiv preprint arXiv:1507.05444, 2015.
  • P. Rebeschini , R. Handel , Phase transitions in nonlinear filtering, Electron. J. Probab., vol. 20, 46 pp., 2015.
  • 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.
  • 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.
  • 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.
  • P. Rubin-Delanchy , D. J. Lawson , Posterior predictive p-values and the convex order, arXiv preprint:1412.3442, 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.
  • 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.
  • H. Strathmann , D. Sejdinovic , M. Girolami , Unbiased Bayes for Big Data: Paths of Partial Posteriors, ArXiv e-prints:1501.03326, 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.
  • 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.
  • 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.
  • Z. Kurth-Nelson , G. Barnes , D. Sejdinovic , R. Dolan , P. Dayan , Temporal structure in associative retrieval, eLife, vol. 4, no. e04919, 2015.
  • 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.
  • 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
  • S. Favaro , B. Nipoti , Y. W. Teh , Rediscovery of Good-Turing Estimators via Bayesian Nonparametrics, Biometrics, 2015.
    Project: bigbayes
  • P. G. Moreno , A. Artés-Rodríguez , Y. W. Teh , F. Perez-Cruz , Bayesian Nonparametric Crowdsourcing, Journal of Machine Learning Research (JMLR), 2015.
    Project: bigbayes
  • 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.
  • 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
  • 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
  • T. Lienart , Y. W. Teh , A. Doucet , Expectation Particle Belief Propagation, in Advances in Neural Information Processing Systems (NeurIPS), 2015.
    Project: sgmcmc
  • 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
  • M. Balog , Y. W. Teh , The Mondrian Process for Machine Learning, 2015.
    Project: bigbayes
  • P. Orbanz , L. James , Y. W. Teh , Scaled subordinators and generalizations of the Indian buffet process, 2015.
    Project: bigbayes
  • M. De Iorio , L. Elliott , S. Favaro , Y. W. Teh , Bayesian Nonparametric Inference of Population Admixtures, 2015.
    Project: bigbayes
  • 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

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.
  • P. Rebeschini , R. Handel , Comparison Theorems for Gibbs Measures, Journal of Statistical Physics, vol. 157, no. 2, 234–281, Oct. 2014.
  • 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.
  • 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.
  • A. Todeschini , F. Caron , M. Fuentes , P. Legrand , P. Del Moral , Biips: software for Bayesian inference with interacting particle systems, arXiv:1412.3779, 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.
  • 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.
  • 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.
  • 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.
  • B. J. Gram-Hansen , An Insight Into: Quantum Random Walks, Master's thesis, 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • J. Lee , S. Choi , Incremental tree-based inference with dependent normalized random measures, in Artificial Intelligence and Statistics (AISTATS), 2014.
  • C. J. Maddison , D. Tarlow , Structured Generative Models of Natural Source Code, in Proceedings of the 31st International Conference on Machine Learning, 2014.
  • C. J. Maddison , D. Tarlow , T. Minka , A* Sampling, in Advances in Neural Information Processing Systems 27, 2014.
  • 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.
  • 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.
  • 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.
  • P. Alquier , V. Cottet , N. Chopin , J. Rousseau , Bayesian matrix completion: prior specification, arXiv preprint arXiv:1406.1440, 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.
  • S. Petrone , J. Rousseau , C. Scricciolo , Bayes and empirical Bayes: do they merge?, Biometrika, vol. 101, no. 2, 285–302, 2014.
  • 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.
  • P. Rubin-Delanchy , N. A. Heard , A test for dependence between two point processes on the real line, arXiv preprint:1408.3845, 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • D. Sejdinovic , H. Strathmann , M. Lomeli , C. Andrieu , A. Gretton , Kernel Adaptive Metropolis-Hastings, in International Conference on Machine Learning (ICML), 2014, 1665–1673.
  • 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.
  • 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.
  • 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
  • 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
  • T. Herlau , M. Mörup , Y. W. Teh , M. N. Schmidt , Adaptive Reconfiguration Moves for Dirichlet Mixtures, submitted, 2014.
  • 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
  • B. Lakshminarayanan , D. Roy , Y. W. Teh , Mondrian Forests: Efficient Online Random Forests, in Advances in Neural Information Processing Systems (NeurIPS), 2014.
    Project: bigbayes
  • 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
  • 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.

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.
  • 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.
  • 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.
  • 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.
  • 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.
  • G. Deligiannidis , S. Utev , Variance of partial sums of stationary sequences, Annals of Probability, vol. 41, no. 5, 3606–3616, 2013.
  • R. J. Evans , A. Forcina , Two algorithms for fitting constrained marginal models, Computational Statistics and Data Analysis, vol. 66, 1–7, 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.
  • 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.
  • 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.
  • 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.
  • J. Liepe , S. Filippi , M. Komorowski , M. P. H. Stumpf , Maximizing the Information Content of Experiments in Systems Biology, PLoS computational biology, 2013.
  • D. D. Denison , M. H. Hansen , C. C. Holmes , B. Mallick , B. Yu , Nonlinear estimation and classification. Springer Science & Business Media, 2013.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • C. Yau , C. C. Holmes , A decision-theoretic approach for segmental classification, The Annals of Applied Statistics, vol. 7, no. 3, 1814–1835, 2013.
  • 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.
  • 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.
  • R. Grosse , C. J. Maddison , R. Salakhutdinov , Annealing Between Distributions by Averaging Moments, in Advances in Neural Information Processing Systems 26, 2013.
  • L. Schumacher , G. K. Nicholls , A Cell-Level Mechanism of Contrast Gain Control, arXiv preprint arXiv:1310.5968, 2013.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • S. Favaro , Y. W. Teh , MCMC for Normalized Random Measure Mixture Models, Statistical Science, vol. 28, no. 3, 335–359, 2013.
  • B. Lakshminarayanan , D. Roy , Y. W. Teh , Top-down Particle Filtering for Bayesian Decision Trees, in International Conference on Machine Learning (ICML), 2013.
  • C. Blundell , Y. W. Teh , Bayesian Hierarchical Community Discovery, in Advances in Neural Information Processing Systems (NeurIPS), 2013.
  • 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.
  • S. Patterson , Y. W. Teh , Stochastic Gradient Riemannian Langevin Dynamics on the Probability Simplex, in Advances in Neural Information Processing Systems (NeurIPS), 2013.
  • 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.
  • C. Chen , V. A. Rao , W. Buntine , Y. W. Teh , Dependent Normalized Random Measures, in International Conference on Machine Learning (ICML), 2013.
  • B. Lakshminarayanan , Y. W. Teh , Inferring Ground Truth from Multi-annotator Ordinal Data: A Probabilistic Approach, 2013.

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.
  • 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.
  • 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.
  • 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.
  • 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.
  • C. Archambeau , F. Caron , Plackett-Luce regression: A new Bayesian model for polychotomous data, in Uncertainty in Artificial Intelligence (UAI), 2012.
  • F. Caron , Bayesian nonparametric models for bipartite graphs, in Advances in Neural Information Processing Systems (NeurIPS), 2012.
  • R. J. Evans , Graphical methods for inequality constraints in marginalized DAGs, in Machine Learning for Signal Processing, 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • M. Rantalainen , C. C. Holmes , Robust Statistical Methods For Genome-Wide Eqtl Analysis, 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.
  • 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.
  • C. C. Holmes , Large-scale genetic analysis of quantitative traits, PhD thesis, University of Oxford, 2012.
  • C. C. Holmes , Extensions of the case-control design in genome-wide association studies, PhD thesis, University of Oxford, 2012.
  • 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.
  • 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.
  • 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.
  • G. K. Nicholls , C. Fox , A. Watt , Coupled MCMC with a randomized acceptance probability, arXiv preprint arXiv:1205.6857, 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.
  • V. Rivoirard , J. Rousseau , . others , Posterior concentration rates for infinite dimensional exponential families, Bayesian Analysis, vol. 7, no. 2, 311–334, 2012.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • R. Piechocki , D. Sejdinovic , Combinatorial Channel Signature Modulation for Wireless ad-hoc Networks, in IEEE International Conference on Communications (ICC), 2012.
  • 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.
  • F. Caron , Y. W. Teh , Bayesian Nonparametric Models for Ranked Data, in Advances in Neural Information Processing Systems (NeurIPS), 2012.
  • B. Alexe , N. Heess , Y. W. Teh , V. Ferrari , Searching for Objects Driven by Context, in Advances in Neural Information Processing Systems (NeurIPS), 2012.
  • V. Rao , Y. W. Teh , MCMC for Continuous-Time Discrete-State Systems, in Advances in Neural Information Processing Systems (NeurIPS), 2012.
  • A. Mnih , Y. W. Teh , A Fast and Simple Algorithm for Training Neural Probabilistic Language Models, in International Conference on Machine Learning (ICML), 2012.
  • N. Heess , D. Silver , Y. W. Teh , Actor-Critic Reinforcement Learning with Energy-Based Policies, in JMLR Workshop and Conference Proceedings: EWRL 2012, 2012.
  • A. Mnih , Y. W. Teh , Learning Label Trees for Probabilistic Modelling of Implicit Feedback, in Advances in Neural Information Processing Systems (NeurIPS), 2012.
  • 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.
  • 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.

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.
  • T. S. Richardson , R. J. Evans , J. M. Robins , Transparent parameterizations of models for potential outcomes, Bayesian Statistics, vol. 9, 569–610, 2011.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • A. Jasra , C. C. Holmes , Stochastic boosting algorithms, Statistics and Computing, vol. 21, no. 3, 335–347, 2011.
  • 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.
  • 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.
  • C. C. Holmes , L. Held , . others , Response to van der Lans, Bayesian Analysis, vol. 6, no. 2, 357–358, 2011.
  • 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.
  • 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.
  • M. Krnjajić , N. Hjort , C. Holmes , P. Müller , S. Walker , Bayesian Nonparametrics. American Statistical Association, Taylor & Francis, Ltd., 2011.
  • 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.
  • 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.
  • 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.
  • W. Dai , D. Sejdinovic , O. Milenkovic , Gaussian Dynamic Compressive Sensing, in International Conference on Sampling Theory and Applications (SampTA), 2011.
  • V. Rao , Y. W. Teh , Gaussian Process Modulated Renewal Processes, in Advances in Neural Information Processing Systems (NeurIPS), 2011.
  • V. Rao , Y. W. Teh , Fast MCMC sampling for Markov jump processes and continuous time Bayesian networks, in Uncertainty in Artificial Intelligence (UAI), 2011.
  • D. Görür , Y. W. Teh , Concave-Convex Adaptive Rejection Sampling, Journal of Computational and Graphical Statistics, 2011.
  • 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.
  • R. Silva , C. Blundell , Y. W. Teh , Mixed Cumulative Distribution Networks, in Artificial Intelligence and Statistics (AISTATS), 2011.
  • Y. W. Teh , C. Blundell , L. T. Elliott , Modelling Genetic Variations with Fragmentation-Coagulation Processes, in Advances in Neural Information Processing Systems (NeurIPS), 2011.
  • 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.
  • M. Welling , Y. W. Teh , Bayesian Learning via Stochastic Gradient Langevin Dynamics, in International Conference on Machine Learning (ICML), 2011.
  • 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.

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.
  • 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.
  • S. Filippi , O. Cappe , A. Garivier , C. Szepesvari , Parametric bandits: The generalized linear case, in Neural Information Processing Systems (NIPS’2010), 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.
  • 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.
  • N. L. Hjort , C. C. Holmes , P. Müller , S. G. Walker , Bayesian nonparametrics. Cambridge University Press, 2010.
  • 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.
  • A. Lee , F. Caron , A. Doucet , C. C. Holmes , A hierarchical Bayesian framework for constructing sparsity-inducing priors, arXiv preprint arXiv:1009.1914, 2010.
  • N. L. Hjort , C. C. Holmes , P. Müller , S. G. Walker , An invitation to Bayesian nonparametrics, Bayesian Nonparametrics, vol. 28, 1, 2010.
  • 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.
  • 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.
  • J. Griffin , C. C. Holmes , Computational issues arising in Bayesian nonparametric hierarchical models, Bayesian Nonparametrics, vol. 28, 208, 2010.
  • 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.
  • 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.
  • C. Yau , C. C. Holmes , A decision theoretic approach for segmental classification using Hidden Markov models, Arxiv preprint arXiv:1007.4532, 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • C. Holmes , P. Müller , S. Walker , Bayesian nonparametrics, Cambridge Univ Pr, 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • C. Blundell , Y. W. Teh , K. A. Heller , Bayesian Rose Trees, in Uncertainty in Artificial Intelligence (UAI), 2010.
  • J. Gasthaus , Y. W. Teh , Improvements to the Sequence Memoizer, in Advances in Neural Information Processing Systems (NeurIPS), 2010.
  • 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.
  • Y. W. Teh , Dirichlet Processes, in Encyclopedia of Machine Learning, Springer, 2010.
  • P. Orbanz , Y. W. Teh , Bayesian Nonparametric Models, in Encyclopedia of Machine Learning, Springer, 2010.
  • J. Gasthaus , F. Wood , Y. W. Teh , Lossless compression based on the Sequence Memoizer, in Data Compression Conference, 2010.

2009

  • F. Caron , A. Doucet , Bayesian Nonparametric Models on Decomposable Graphs, in Advances in Neural Information Processing Systems (NeurIPS), 2009.
  • 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.
  • 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.
  • 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.
  • 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.
  • A. Feller , C. C. Holmes , Beyond toplines: Heterogeneous treatment effects in randomized experiments, Unpublished manuscript, Oxford University, 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.
  • C. C. Holmes , A. Jasra , Antithetic methods for gibbs samplers, Journal of Computational and Graphical Statistics, vol. 18, no. 2, 401–414, 2009.
  • 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.
  • 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.
  • 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.
  • C. C. Holmes , Increasing statistical power and generalizability in genomics microarray research, PhD thesis, University of Oxford, 2009.
  • 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.
  • C. P. Robert , N. Chopin , J. Rousseau , . others , Harold Jeffreys’s theory of probability revisited, Statistical Science, vol. 24, no. 2, 141–172, 2009.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • D. Sejdinovic , R. Piechocki , A. Doufexi , AND-OR tree analysis of distributed LT codes, in IEEE Information Theory Workshop (ITW), 2009, 261–265.
  • 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.
  • D. Sejdinovic , R. Piechocki , A. Doufexi , Rateless distributed source code design, in International ICST Mobile Multimedia Communications Conference (MOBIMEDIA), 2009.
  • D. Sejdinovic , Topics in Fountain Coding, PhD thesis, University of Bristol, 2009.
  • V. Rao , Y. W. Teh , Spatial Normalized Gamma Processes, in Advances in Neural Information Processing Systems (NeurIPS), 2009, vol. 22.
  • F. Wood , Y. W. Teh , A Hierarchical Nonparametric Bayesian Approach to Statistical Language Model Domain Adaptation, in Artificial Intelligence and Statistics (AISTATS), 2009.
  • D. M. Roy , Y. W. Teh , The Mondrian Process, in Advances in Neural Information Processing Systems (NeurIPS), 2009, vol. 21.
  • 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.
  • Y. W. Teh , D. Görür , Indian Buffet Processes with Power-law Behavior, in Advances in Neural Information Processing Systems (NeurIPS), 2009, vol. 22.
  • K. A. Heller , Y. W. Teh , D. Görür , Infinite Hierarchical Hidden Markov Models, in Artificial Intelligence and Statistics (AISTATS), 2009, vol. 5.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • A. Asuncion , M. Welling , P. Smyth , Y. W. Teh , On Smoothing and Inference for Topic Models, in Uncertainty in Artificial Intelligence (UAI), 2009.

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.
  • 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.
  • 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.
  • 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.
  • 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.
  • C. Yau , C. Holmes , CNV discovery using SNP genotyping arrays, Cytogenetic and genome research, vol. 123, no. 1-4, 307–312, 2008.
  • 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.
  • 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.
  • G. K. Nicholls , Horses or farmers? The tower of Babel and confidence in trees, Significance, vol. 5, no. 3, 112–117, 2008.
  • 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.
  • 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.
  • D. Fraser , J. Rousseau , Studentization and deriving accurate p-values, Biometrika, vol. 95, no. 1, 1–16, 2008.
  • A. Chambaz , J. Rousseau , Bounds for Bayesian order identification with application to mixtures, The Annals of Statistics, 938–962, 2008.
  • 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.
  • 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.
  • D. Sejdinovic , R. Piechocki , A. Doufexi , M. Ismail , Fountain coding with decoder side information, in IEEE International Conference on Communications (ICC), 2008, 4477–4482.
  • 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.
  • D. Sejdinovic , R. Piechocki , A. Doufexi , M. Ismail , Rate adaptive binary erasure quantization with dual fountain codes, in IEEE Global Telecommunications Conference (GLOBECOM), 2008.
  • 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.
  • 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.
  • 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.
  • 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.
  • Y. W. Teh , K. Kurihara , M. Welling , Collapsed Variational Inference for HDP, in Advances in Neural Information Processing Systems (NeurIPS), 2008, vol. 20.

2007

  • F. Caron , M. Davy , A. Doucet , Generalized Polya urn for time-varying Dirichlet process mixtures, in Uncertainty in Artificial Intelligence (UAI), 2007.
  • 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.
  • 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.
  • L. Astle , C. Holmes , D. Balding , Turbo Genomic Control, 2007.
  • 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.
  • 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.
  • A. Jasra , D. A. Stephens , C. C. Holmes , Population-based reversible jump Markov chain Monte Carlo, Biometrika, 787–807, 2007.
  • 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.
  • 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.
  • 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.
  • J. Rousseau , Approximating interval hypothesis: p-values and Bayes factors, Bayesian statistics, vol. 8, 417–452, 2007.
  • 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.
  • 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.
  • 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.
  • Y. W. Teh , D. Görür , Z. Ghahramani , Stick-breaking Construction for the Indian Buffet Process, in Artificial Intelligence and Statistics (AISTATS), 2007, vol. 11.
  • 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.
  • Y. J. Lim , Y. W. Teh , Variational Bayesian Approach to Movie Rating Prediction, in Proceedings of KDD Cup and Workshop, 2007.
  • 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.
  • 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.

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.
  • A. Pintore , P. Speckman , C. C. Holmes , Spatially adaptive smoothing splines, Biometrika, 113–125, 2006.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • Y. W. Teh , A Bayesian Interpretation of Interpolated Kneser-Ney, School of Computing, National University of Singapore, TRA2/06, 2006.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.

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.
  • 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.
  • 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.
  • J. Stephenson , C. C. Holmes , K. Gallagher , A. Pintore , A statistical technique for modelling non-stationary spatial processes, Geostatistics Banff 2004, 125–134, 2005.
  • 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.
  • 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.
  • 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.
  • 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.
  • C. C. Holmes , S. D. Brown , All systems GO for understanding mouse gene function, The Scientist, vol. 19, no. 1, 20–1, 2005.
  • 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.
  • 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.
  • 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.
  • G. Gayraud , J. Rousseau , Rates of convergence for a Bayesian level set estimation, Scandinavian journal of statistics, vol. 32, no. 4, 639–660, 2005.
  • 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.
  • 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.
  • 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.
  • Y. W. Teh , M. Seeger , M. I. Jordan , Semiparametric Latent Factor Models, in Artificial Intelligence and Statistics (AISTATS), 2005, vol. 10.
  • M. Seeger , Y. W. Teh , M. I. Jordan , Semiparametric Latent Factor Models, Division of Computer Science, University of California at Berkeley, 2005.
  • M. Welling , T. Minka , Y. W. Teh , Structured Region Graphs: Morphing EP into GBP, in Uncertainty in Artificial Intelligence (UAI), 2005, vol. 21.

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.
  • C. C. Holmes , S. D. Brown , All systems GO for understanding mouse gene function, Journal of biology, vol. 3, no. 5, 20, 2004.
  • 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.
  • 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.
  • A. Mira , G. K. Nicholls , Bridge estimation of the probability density at a point, Statistica Sinica, vol. 14, no. 2, 603–612, 2004.
  • 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.
  • 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.
  • M. Welling , Y. W. Teh , Linear Response Algorithms for Approximate Inference in Graphical Models, Neural Computation, vol. 16, 197–221, 2004.
  • 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.
  • 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.

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.
  • C. Holmes , L. Knorr-Held , Efficient simulation of Bayesian logistic regression models, Discussion papers/Sonderforschungsbereich 386 der Ludwig-Maximilians-Universität München, 2003.
  • C. Holmes , N. Heard , Generalized monotonic regression using random change points, Statistics in Medicine, vol. 22, no. 4, 623–638, 2003.
  • 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.
  • C. C. Holmes , N. M. Adams , Likelihood inference in nearest-neighbour classification models, Biometrika, 99–112, 2003.
  • C. Holmes , D. Denison , Classification with bayesian MARS, Machine Learning, vol. 50, no. 1, 159–173, 2003.
  • C. Holmes , B. Mallick , Perfect simulation for Bayesian curve and surface fitting, Preprint from www. stat. tamu. edu/\\~ bmallick/papers/perf. ps, 2003.
  • R. Graziani , C. C. Holmes , Bayesian free knot polynomial splines of random order. Università commerciale Luigi Bocconi, 2003.
  • 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.
  • 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.
  • 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.
  • 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.
  • Y. W. Teh , Bethe Free Energy and Contrastive Divergence Approximations for Undirected Graphical Models, PhD thesis, Department of Computer Science, University of Toronto, 2003.
  • Y. W. Teh , S. Roweis , Automatic Alignment of Local Representations, in Advances in Neural Information Processing Systems (NeurIPS), 2003, vol. 15.
  • Y. W. Teh , M. Welling , On Improving the Efficiency of the Iterative Proportional Fitting Procedure, in Artificial Intelligence and Statistics (AISTATS), 2003, vol. 9.
  • M. Welling , Y. W. Teh , Approximate Inference in Boltzmann Machines, Artificial Intelligence, vol. 143, no. 1, 19–50, 2003.

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.
  • 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.
  • D. Denison , N. Adams , C. Holmes , D. Hand , Bayesian partition modelling, Computational statistics & data analysis, vol. 38, no. 4, 475–485, 2002.
  • J. Ferreira , D. Denison , C. Holmes , Partition modelling, 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.
  • 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.
  • 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.
  • D. G. Denison , Bayesian methods for nonlinear classification and regression. John Wiley & Sons, 2002.
  • C. Holmes , [Spline Adaptation in Extended Linear Models]: Comment, Statistical Science, vol. 17, no. 1, 22–24, 2002.
  • M. Jones , G. K. Nicholls , New radiocarbon calibration software, Radiocarbon, vol. 44, no. 3, 663–674, 2002.
  • 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.
  • 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.
  • A. Philippe , J. Rousseau , . others , Non-informative priors in the case of Gaussian long-memory processes, Bernoulli, vol. 8, no. 4, 451–473, 2002.
  • J. Rousseau , Asymptotic properties of HPD regions in the discrete case, Journal of multivariate analysis, vol. 83, no. 1, 1–21, 2002.
  • Y. W. Teh , M. Welling , The Unified Propagation and Scaling Algorithm, in Advances in Neural Information Processing Systems (NeurIPS), 2002, vol. 14.
  • S. Kakade , Y. W. Teh , S. Roweis , An Alternate Objective Function for Markovian Fields, in International Conference on Machine Learning (ICML), 2002, vol. 19.

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.
  • D. Denison , C. Holmes , Bayesian partitioning for estimating disease risk, Biometrics, vol. 57, no. 1, 143–149, 2001.
  • 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.
  • 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.
  • C. C. D. L. Holmes , Bayesian methods for nonlinear classification and regression, PhD thesis, Department of Mathematics, Imperial College, 2001.
  • 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.
  • M. Jones , G. K. Nicholls , Reservoir offset models for radiocarbon calibration, Radiocarbon, vol. 43, no. 1, 119–124, 2001.
  • G. K. Nicholls , Spontaneous magnetization in the plane, Journal of Statistical Physics, vol. 102, no. 5-6, 1229–1251, 2001.
  • 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.
  • J. Rousseau , M. Ghosh , D. Kim , Non-informative priors for the bivariate Fieller-Creasy problem, Statistics and Decisions, vol. 19, 227, 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.
  • 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.
  • 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.
  • 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.
  • Y. W. Teh , M. Welling , Passing and Bouncing Messages for Generalized Inference, Gatsby Computational Neuroscience Unit, University College London, GCNU TR 2001-01, 2001.

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.
  • 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.
  • 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.
  • 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.
  • G. E. Hinton , Z. Ghahramani , Y. W. Teh , Learning to Parse Images, in Advances in Neural Information Processing Systems (NeurIPS), 2000, vol. 12.

1999

  • A. Guglielmi , C. C. Holmes , S. G. Walker , Perfect simulation involving a continuous and unbounded state space, Preprint, 1999.
  • C. Holmes , D. Denison , Bayesian wavelet analysis with a model complexity prior, Bayesian statistics, vol. 6, 769–776, 1999.
  • C. Holmes , D. Denison , B. Mallick , Bayesian partitioning for classification and regression, Manuscript, Imperial College, 1999.
  • C. Holmes , B. Mallick , Generalised nonlinear modelling with multivariate smoothing splines, Unpublished manuscript, Statistics Section, Department of Mathematics, Imperial College of London, 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.

1998

  • C. Holmes , B. Mallick , Bayesian radial basis functions of variable dimension, Neural Computation, vol. 10, no. 5, 1217–1233, 1998.
  • C. Holmes , B. Mallick , Perfect simulation for orthogonal model mixing, Preprint from http://dbwilson. com/exact, 1998.
  • C. Holmes , B. Mallick , Parallel Markov chain Monte Carlo sampling: an evolutionary based approach, London, Imperial College, 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.
  • 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.
  • 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.
  • 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.
  • F. Bacchus , Y. W. Teh , Making Forward Chaining Relevant, in Proceedings of the International Conference on Artificial Intelligence Planning Systems, 1998.

1997

  • C. Holmes , B. Mallick , Bayesian radial basis functions of unknown dimension, Imperial College Report, 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.
  • 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.