Yee Whye Teh

Yee Whye Teh

Bayesian nonparametrics, probabilistic learning, deep learning

I am a Professor of Statistical Machine Learning at the Department of Statistics, University of Oxford and a Research Scientist at Google DeepMind. I am a European Research Council Consolidator Fellow and an Alan Turing Institute Faculty Fellow. I am interested in developing foundational methodologies for statistical machine learning.

Publications

2017

  • J. Mitrovic, D. Sejdinovic, Y. W. Teh, Deep Kernel Machines via the Kernel Reparametrization Trick, in International Conference on Learning Representations (ICLR) - Workshop Track, 2017.
  • S. Flaxman, Y. W. Teh, D. Sejdinovic, Poisson Intensity Estimation with Reproducing Kernels, in Artificial Intelligence and Statistics (AISTATS), 2017, to appear.
    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

  • 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
  • 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

  • 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

  • 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

  • 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

  • F. Caron, Y. W. Teh, Bayesian Nonparametric Models for Ranked Data, in Advances in Neural Information Processing Systems (NIPS), 2012.
  • V. Rao, Y. W. Teh, MCMC for Continuous-Time Discrete-State Systems, in Advances in Neural Information Processing Systems (NIPS), 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 (NIPS), 2012.
  • L. Elliott, Y. W. Teh, Scalable Imputation of Genetic Data with a Discrete Fragmentation-Coagulation Process, in Advances in Neural Information Processing Systems (NIPS), 2012.

2011

  • V. Rao, Y. W. Teh, Gaussian Process Modulated Renewal Processes, in Advances in Neural Information Processing Systems (NIPS), 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, jcgs, 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 (NIPS), 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.

2010

  • 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 (NIPS), 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

  • V. Rao, Y. W. Teh, Spatial Normalized Gamma Processes, in Advances in Neural Information Processing Systems (NIPS), 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 (NIPS), 2009, vol. 21.
  • D. Görür, Y. W. Teh, An Efficient Sequential Monte-Carlo Algorithm for Coalescent Clustering, in Advances in Neural Information Processing Systems (NIPS), 2009, vol. 21.
  • Y. W. Teh, D. Görür, Indian Buffet Processes with Power-law Behavior, in Advances in Neural Information Processing Systems (NIPS), 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 (NIPS), 2009, vol. 21.
  • J. Gasthaus, F. Wood, D. Görür, Y. W. Teh, Dependent Dirichlet Process Spike Sorting, in Advances in Neural Information Processing Systems (NIPS), 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 (NIPS), 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

  • H. L. Chieu, W. S. Lee, Y. W. Teh, Cooled and Relaxed Survey Propagation for MRFs, in Advances in Neural Information Processing Systems (NIPS), 2008, vol. 20.
  • Y. W. Teh, H. Daume III, D. M. Roy, Bayesian Agglomerative Clustering with Coalescents, in Advances in Neural Information Processing Systems (NIPS), 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 (NIPS), 2008, vol. 20.

2007

  • 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 (NIPS), 2007, vol. 19, 1353–1360.

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

  • 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 (NIPS), 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 (NIPS), 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

  • 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

  • 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 (NIPS), 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

  • Y. W. Teh, M. Welling, The Unified Propagation and Scaling Algorithm, in Advances in Neural Information Processing Systems (NIPS), 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

  • 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 (NIPS), 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

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

1998

  • F. Bacchus, Y. W. Teh, Making Forward Chaining Relevant, in Proceedings of the International Conference on Artificial Intelligence Planning Systems, 1998.

Software

2016

  • B. Lakshminarayanan, D. M. Roy, Y. W. Teh, Mondrian Forest. 2016.
    Project: bigbayes
  • L. Hasenclever, S. Webb, T. Lienart, S. Vollmer, B. Lakshminarayanan, C. Blundell, Y. W. Teh, Posterior Server. 2016.
    Project: sgmcmc
  • L. Elliott, Y. W. Teh, BNPPhase. 2016.
    Project: bigbayes

2015

  • B. Lakshminarayanan, D. M. Roy, Y. W. Teh, PGBart. 2015.
    Project: bigbayes
  • M. De Iorio, L. T. Elliott, S. Favaro, Y. W. Teh, HDPStructure. 2015.
    Project: bigbayes
  • L. Boyles, Y. W. Teh, CPABS: Cancer Phylogenetic Reconstruction with Aldous’ Beta Splitting. 2015.
    Project: bigbayes

2014

  • M. Xu, B. Lakshminarayanan, Y. W. Teh, J. Zhu, B. Zhang, SMS: Sampling via Moment Sharing, Advances in Neural Information Processing Systems. 2014.
    Project: sgmcmc