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

2018

  • 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.
  • 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
  • X. Miscouridou , F. Caron , Y. W. Teh , Modelling sparsity, heterogeneity, reciprocity and community structure in temporal interaction data, Mar-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, arXiv preprint arXiv:1802.04537, 2018.
  • J. Mitrovic , D. Sejdinovic , Y. Teh , Causal Inference via Kernel Deviance Measures, ArXiv e-prints:1804.04622, 2018.
  • 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
  • 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, 2018.
    Project: bigbayes

2017

  • L. Hasenclever , S. Webb , T. Lienart , S. Vollmer , B. Lakshminarayanan , C. Blundell , Y. W. Teh , Distributed Bayesian Learning with Stochastic Natural Gradient Expectation Propagation and the Posterior Server, Journal of Machine Learning Research, vol. 18, no. 106, 1–37, 2017.
    Project: sgmcmc
  • 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.
  • T. Rainforth , Y. Zhou , X. Lu , Y. W. Teh , F. Wood , H. Yang , J. Meent , Inference Trees: Adaptive Inference with Exploration [Workshop Version], NIPS Workshop on Advances in Approximate Bayesian Inference, 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.
  • X. Lu , T. Rainforth , Y. Zhou , Y. W. Teh , F. Wood , H. Yang , J. Meent , On Exploration, Exploitation and Learning in Adaptive Importance Sampling, NIPS Workshop on Advances in Approximate Bayesian Inference, 2017.
  • S. Flaxman , Y. Teh , D. Sejdinovic , Poisson Intensity Estimation with Reproducing Kernels, Electronic Journal of Statistics, vol. 11, no. 2, 5081–5104, 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.
    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 (NIPS), 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.
  • 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 (NIPS), 2017.
  • 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
  • X. Lu , V. Perrone , L. Hasenclever , Y. W. Teh , S. J. Vollmer , Relativistic Monte Carlo, in Artificial Intelligence and Statistics (AISTATS), 2017.
    Project: sgmcmc

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
  • H. Kim , X. Lu , S. Flaxman , Y. W. Teh , Collaborative Filtering with Side Information: a Gaussian Process Perspective, 2016.
  • K. Palla , F. Caron , Y. W. Teh , A Bayesian nonparametric model for sparse dynamic networks, Jun-2016.
    Project: bigbayes
  • 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
  • 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 , Collaborative Filtering with Side Information: a Gaussian Process Perspective, 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

  • 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 (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. 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.
  • B. Alexe , N. Heess , Y. W. Teh , V. Ferrari , Searching for Objects Driven by Context, 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, 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 (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

2017

2016

  • V. Perrone , P. A. Jenkins , D. Spano , Y. W. Teh , NIPS 1987-2015 dataset. 2016.
    Project: bigbayes
  • 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 sgmcmc
  • 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