2017

  • Quin F. Wills, Esther Mellado-Gomez, Rory Nolan, Damien Warner, Eshita Sharma, John Broxholme, Benjamin Wright, Helen Lockstone, William James, Mark Lynch, Michael Gonzales, Jay West, Anne Leyrat, Sergi Padilla-Parra, Sarah Filippi, Chris Holmes, Michael D. Moore, Rory Bowden, The nature and nurture of cell heterogeneity: accounting for macrophage gene-environment interactions with single-cell RNA-Seq, BMC Genomics, 2017.
  • L.J. Kelly, G.K. Nicholls, Lateral transfer in Stochastic Dollo models, Annals of Applied Statistics, to appear, 2017.
  • Amy K. Styring, Michael Charles, Federica Fantone, Mette Marie Hald, Augusta McMahon, Richard H. Meadow, Geoff K. Nicholls, Ajita K. Patel, Mindy C. Pitre, Alexia Smith, Arkadiusz Sołtysiak, Gil Stein11, Jill A. Weber, Harvey Weiss, Amy Bogaard, Isotope evidence for agricultural extensification reveals how the world’s first cities were fed, Nature Plants, to appear, 2017.
  • S. Flaxman, Y.W. Teh, D. Sejdinovic, Poisson Intensity Estimation with Reproducing Kernels, in Artificial Intelligence and Statistics (AISTATS), 2017, to appear.
    Project: bigbayes
  • Q. Zhang, S. Filippi, A. Gretton, D. Sejdinovic, Large-Scale Kernel Methods for Independence Testing, Statistics and Computing, to appear, 2017.
    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, to appear, 2017.
    Project: bigbayes

2016

  • 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), 2016.
  • T. Gray-Davies, C. Holmes, F. Caron, Scalable Bayesian nonparametric regression via a Plackett-Luce model for conditional ranks, Electronic Journal of Statistics, vol. 10, 1807–1828, 2016.
  • E. Matechou, F. Caron, Modelling individual migration patterns using a Bayesian nonparametric approach for capture-recapture data, Annals of Applied Statistics, 2016.
  • A. Todeschini, F. Caron, Exchangeable Random Measures for Sparse and Modular Graphs with Overlapping Communities, arXiv:1602.02114, 2016.
  • R. B. A. Silva, Evans R. J., Causal Inference through a Witness Protection Program, Journal of Machine Learning Research, vol. 17, no. 56, 1–53, 2016.
  • A. Hitz, Evans R. J., One-Component Regular Variation and Graphical Modeling of Extremes, Journal of Applied Probability, vol. 53, no. 3, 733–746, 2016.
  • R. J. Evans, Graphs for margins of Bayesian networks, Scandanavian Journal of Statistics, vol. 43, no. 3, 625–648, 2016.
  • Sarah Filippi, Chris P Barnes, Paul D W Kirk, Takamasa Kudo, Katsuyuki Kunida, Siobhan S McMahon, Takaho Tsuchiya, Takumi Wada, Shinya Kuroda, Michael P H Stumpf, Robustness of MEK-ERK Dynamics and Origins of Cell-to-Cell Variability in MAPK Signaling, CellReports, 2016.
  • Sarah Filippi, Chris C Holmes, Luis E. Nieto-Barajas, Scalable Bayesian nonparametric measures for exploring pairwise dependence via Dirichlet Process Mixtures, Electronic Journal of Statistics, 2016.
  • Sarah Filippi, Chris C Holmes, A Bayesian Nonparametric Approach to Testing for Dependence Between Random Variables, Bayesian Analysis, 2016.
  • William Herlands, Andrew Wilson, Hannes Nickisch, Seth Flaxman, Daniel Neill, Wilbert Van Panhuis, Eric 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
  • Charles Loeffler, Seth Flaxman, Is Gun Violence Contagious?, 2016.
    Project: bigbayes
  • Bryce Goodman, Seth Flaxman, European Union regulations on algorithmic decision-making and a “right to explanation,” Jun-2016.
    Project: bigbayes
  • Seth Flaxman, Dougal Sutherland, Yu-Xiang Wang, Yee Whye Teh, Understanding the 2016 US Presidential Election using ecological inference and distribution regression with census microdata, Arxiv e-prints, Nov-2016.
    Project: bigbayes
  • S. Bhatt, E. Cameron, Seth Flaxman, D. J. Weiss, D. L. Smith, P. W. Gething, Improved prediction accuracy for disease risk mapping using Gaussian Process stacked generalisation, Dec-2016.
  • Chris J. Maddison, Andriy Mnih, Yee Whye Teh, The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables, 2016.
  • Chris 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.
  • David Silver, Aja Huang, Chris J. Maddison, Arthur Guez, Laurent Sifre, George Driessche, Julian Schrittwieser, Ioannis Antonoglou, Veda Panneershelvam, Marc Lanctot, Sander Dieleman, Dominik Grewe, John Nham, Nal Kalchbrenner, Ilya Sutskever, Timothy Lillicrap, Madeleine Leach, Koray Kavukcuoglu, Thore Graepel, Demis Hassabis, Mastering the game of Go with deep neural networks and tree search, Nature, vol. 529, no. 7587, 484–489, 2016.
  • Konstantina Palla, Francois Caron, Yee Whye Teh, A Bayesian nonparametric model for sparse dynamic networks, Jun-2016.
    Project: bigbayes
  • Konstantina Palla, David Knowles, Zoubin Ghahramani, A birth-death process for feature allocation , 2016.
    Project: bigbayes
  • NA Heard, K Palla, M Skoularidou, Topic modelling of authentication events in an enterprise computer network, 2016.
    Project: bigbayes
  • Patrick Rubin-Delanchy, Daniel J Lawson, Nicholas A Heard, Anomaly detection for cyber-security applications, in Dynamic Networks and Cybersecurity, London: World Scientific, 2016.
  • Patrick Rubin-Delanchy, Nicholas A Heard, On the mid-p-value of a test statistic with arbitrary real support, arXiv preprint:1505.05068, 2016.
  • Juliette Griffié, Michael Shannon, Claire L Bromley, Lies Boelen, Garth L Burn, David J Williamson, Nicholas A Heard, Andrew P Cope, Dylan M Owen, Patrick Rubin-Delanchy, A Bayesian cluster analysis method for single-molecule localization microscopy data, Nature Protocols, vol. 11, 2499–2514, 2016.
  • Patrick Rubin-Delanchy, Niall M Adams, Nicholas A Heard, Disassortivity of computer networks, in Proceedings of IEEE workshop on Big Data Analytics for Cyber-security Computing, 2016.
  • Nicholas 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.
  • Marcus Groß, Ulrich Rendtel, Timo Schmid, Sebastian Schmon, Nikos 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), 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.P. 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
  • V. Perrone, P. A. Jenkins, D. Spano, Y. W. Teh, Poisson Random Fields for Dynamic Feature Models, 2016.
    Project: bigbayes
  • T. Fernandez, N. Rivera, Y. W. Teh, Gaussian Processes for Survival Analysis, in Advances in Neural Information Processing Systems (NIPS), 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
  • J. Arbel, S. Favaro, B. Nipoti, Y. W. Teh, Bayesian nonparametric inference for discovery probabilities: credible intervals and large sample asymptotics, Statistica Sinica, 2016.
    Project: bigbayes
  • 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
  • X. Lu, V. Perrone, L. Hasenclever, Y. W. Teh, S. J. Vollmer, Relativistic Monte Carlo, 2016.
    Project: sgmcmc
  • D. Glowacka, Y. W. Teh, J. Shawe-Taylor, Image Retrieval with a Bayesian Model of Relevance Feedback, 2016.
  • H. Kim, X. Lu, S. Flaxman, Y. W. Teh, Tucker Gaussian Process for Regression and Collaborative Filtering, 2016.
    Project: bigbayes
  • M. Battiston, S. Favaro, D. M. Roy, Y. W. Teh, A Characterization of Product-Form Exchangeable Feature Probability Functions, 2016.
    Project: bigbayes
  • K. Palla, F. Caron, Y. W. Teh, Bayesian Nonparametrics for Sparse Dynamic Networks, 2016.
    Project: bigbayes

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.
  • R. J. Evans, Conditional distributions and log-linear parameters, Electronic Journal of Statistics, vol. 9, no. 1, 475–491, 2015.
  • R. J. Evans, Smooth, identifiable supermodels of discrete DAG models with latent variables, 2015.
  • R. J. Evans, Margins of discrete Bayesian networks, 2015.
  • R. J. Evans, V. Didelez, Recovering from Selection Bias using Marginal Structure in Discrete Models, in UAI-15 (Causal Inference Workshop), 2015.
  • Siobhan S Mc Mahon, Oleg Lenive, Sarah Filippi, Michael P H Stumpf, Information processing by simple molecular motifs and susceptibility to noise, Journal of The Royal Society Interface, 2015.
  • Chris J. Maddison, Aja Huang, Ilya Sutskever, David Silver, Move Evaluation in Go Using Deep Convolutional Neural Networks, in International Conference on Learning Representations, 2015.
  • Patrick Rubin-Delanchy, Garth L Burn, Juliette Griffié, David J Williamson, Nicholas A Heard, Andrew P Cope, Dylan M Owen, Bayesian cluster identification in single-molecule localization microscopy data, Nature methods, vol. 12, 1072–1076, 2015.
  • Patrick Rubin-Delanchy, Daniel J Lawson, Posterior predictive p-values and the convex order, arXiv preprint:1412.3442, 2015.
  • F.-X. Briol, C.J. Oates, M. Girolami, M.A. Osborne, D. Sejdinovic, Probabilistic Integration: A Role for Statisticians in Numerical Analysis?, ArXiv e-prints:1512.00933, 2015.
  • I. Schuster, H. Strathmann, B. Paige, D. Sejdinovic, Kernel Sequential Monte Carlo, ArXiv e-prints:1510.03105, 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 (NIPS), 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 (NIPS), vol. 28, 2015, 1981–1989.
  • Dejan Vukobratovic, Dino Sejdinovic, Aleksandra Pizurica, Compressed Sensing Using Sparse Binary Measurements: A Rateless Coding Perspective, in IEEE International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), 2015.
  • Zeb Kurth-Nelson, Gareth Barnes, Dino Sejdinovic, Ray Dolan, Peter Dayan, Temporal structure in associative retrieval, eLife, vol. 4, no. e04919, 2015.
  • Wittawat Jitkrittum, Arthur Gretton, Nicolas Heess, S. M. Ali Eslami, Balaji Lakshminarayanan, Dino Sejdinovic, Zoltán 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, Cancer Research, 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 (NIPS), 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 (NIPS), 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. Lomeli, S. Favaro, Y. W. Teh, A Marginal Sampler for σ-Stable Poisson-Kingman Mixture Models, jcgs, 2015.
    Project: bigbayes
  • M. Balog, Y. W. Teh, The Mondrian Process for Machine Learning, 2015.
    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, 2015.
    Project: sgmcmc
  • 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

  • F. Caron, E. Fox, Sparse Graphs using exchangeable random measures, arXiv: 1401.1137, 2014.
  • Adrien Todeschini, François Caron, Marc Fuentes, Pierrick Legrand, Pierre 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.
  • Siobhan S Mc Mahon, Aaron Sim, Sarah Filippi, Robert Johnson, Juliane Liepe, Dominic Smith, Michael P H Stumpf, Information theory and signal transduction systems: From molecular information processing to network inference., Seminars in cell & developmental biology, 2014.
  • Adam L MacLean, Sarah Filippi, Michael P H Stumpf, The ecology in the hematopoietic stem cell niche determines the clinical outcome in chronic myeloid leukemia, PNAS, 2014.
  • Juliane Liepe, Paul Kirk, Sarah Filippi, Tina Toni, Chris P Barnes, Michael P H Stumpf, A framework for parameter estimation and model selection from experimental data in systems biology using approximate Bayesian computation., Nature Protocols, 2014.
  • Chris J. Maddison, Daniel Tarlow, Structured Generative Models of Natural Source Code, in Proceedings of the 31st International Conference on Machine Learning, 2014.
  • Chris J. Maddison, Daniel Tarlow, Tom Minka, A* Sampling, in Advances in Neural Information Processing Systems 27, 2014.
  • Patrick Rubin-Delanchy, Nicholas A Heard, A test for dependence between two point processes on the real line, arXiv preprint:1408.3845, 2014.
  • Patrick Rubin-Delanchy, Daniel J Lawson, Melissa JM Turcotte, Nicholas A Heard, Niall M Adams, Three statistical approaches to sessionizing network flow data, in Proceedings of the IEEE Joint Intelligence and Security Informatics Conference (JISIC), 2014.
  • Nicholas A Heard, Daniel J Lawson, Patrick Rubin-Delanchy, Filtering automated polling traffic in computer network flow data, in Proceedings of the IEEE Joint Intelligence and Security Informatics Conference (JISIC), 2014.
  • Daniel J Lawson, Patrick Rubin-Delanchy, Nicholas A Heard, Niall 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.
  • Sebastian Schmon, Teil, Georeferenzierung, Zeitschrift für amtliche Statistik, vol. 3, no. 2, 14, 2014.
  • Kacper Chwialkowski, Dino Sejdinovic, Arthur Gretton, A Wild Bootstrap for Degenerate Kernel Tests, in Advances in Neural Information Processing Systems (NIPS), vol. 27, 2014, 3608–3616.
  • D. Sejdinovic, H. Strathmann, M.G. Lomeli, C. Andrieu, A. Gretton, Kernel Adaptive Metropolis-Hastings, in International Conference on Machine Learning (ICML), 2014, 1665–1673.
  • Oliver Johnson, Dino Sejdinovic, James Cruise, Robert Piechocki, Ayalvadi 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 (NIPS), 2014.
    Project: bigbayes
  • B. Paige, F. Wood, A. Doucet, Y. W. Teh, Asynchronous Anytime Sequential Monte Carlo, in Advances in Neural Information Processing Systems (NIPS), 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

  • Adrien Todeschini, François Caron, Marie Chavent, Probabilistic low-rank matrix completion with adaptive spectral regularization algorithms, in Advances in Neural Information Processing Systems (NIPS), 2013, 845–853.
  • 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.
  • R. J. Evans, A. Forcina, Two algorithms for fitting constrained marginal models, Computational Statistics and Data Analysis, vol. 66, 1–7, 2013.
  • Daniel Silk, Sarah Filippi, Michael P H Stumpf, Optimizing threshold-schedules for sequential approximate Bayesian computation: applications to molecular systems., Statistical Applications in Genetics and Molecular Biology, 2013.
  • Sarah Filippi, Chris P Barnes, Julien Cornebise, Michael P H Stumpf, On optimality of kernels for approximate Bayesian computation using sequential Monte Carlo., Statistical Applications in Genetics and Molecular Biology, 2013.
  • Juliane Liepe, Sarah Filippi, Michał Komorowski, Michael P H Stumpf, Maximizing the Information Content of Experiments in Systems Biology, PLoS computational biology, 2013.
  • Roger Grosse, Chris J. Maddison, Ruslan Salakhutdinov, Annealing Between Distributions by Averaging Moments, in Advances in Neural Information Processing Systems 26, 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.
  • Dino Sejdinovic, Arthur Gretton, Wicher Bergsma, A Kernel Test for Three-Variable Interactions, in Advances in Neural Information Processing Systems (NIPS), vol. 26, 2013, 1124–1132.
  • Dino Sejdinovic, Bharath Sriperumbudur, Arthur Gretton, Kenji Fukumizu, Equivalence of distance-based and RKHS-based statistics in hypothesis testing, Annals of Statistics, vol. 41, no. 5, 2263–2291, Oct. 2013.
  • 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 (NIPS), 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 (NIPS), 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

  • Cédric Archambeau, Francois 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 (NIPS), 2012.
  • R. J. Evans, Graphical methods for inequality constraints in marginalized DAGs, in Machine Learning for Signal Processing, 2012.
  • Anindita Roy, Gillian Cowan, Adam J Mead, Sarah Filippi, Georg Bohn, Aristeidis Chaidos, Oliver Tunstall, Jerry K Y Chan, Mahesh Choolani, Phillip Bennett, Sailesh Kumar, Deborah Atkinson, Josephine Wyatt-Ashmead, Ming Hu, Michael P H Stumpf, Katerina Goudevenou, David O’Connor, Stella T Chou, Mitchell J Weiss, Anastasios Karadimitris, Sten Eirik Jacobsen, Paresh Vyas, Irene Roberts, Perturbation of fetal liver hematopoietic stem and progenitor cell development by trisomy 21., Proceedings of the National Academy of Sciences, 2012.
  • Chris P Barnes, Sarah Filippi, Michael P H Stumpf, Thomas Thorne, Considerate approaches to constructing summary statistics for ABC model selection, Statistics and Computing, 2012.