Yee W. Teh, Ph.D. - Publications

Affiliations: 
2003 University of Toronto, Toronto, ON, Canada 
Area:
machine learning

76 high-probability publications. We are testing a new system for linking publications to authors. You can help! If you notice any inaccuracies, please sign in and mark papers as correct or incorrect matches. If you identify any major omissions or other inaccuracies in the publication list, please let us know.

Year Citation  Score
2016 Teh YW. Bayesian nonparametric modeling and the ubiquitous ewens sampling formula Statistical Science. 31: 34-36. DOI: 10.1214/15-STS540  1
2016 Teh YW, Thiery AH, Vollmer SJ. Consistency and fluctuations for stochastic gradient Langevin dynamics Journal of Machine Learning Research. 17.  1
2015 Adams RP, Fox EB, Sudderth EB, Teh YW. Guest Editors' Introduction to the Special Issue on Bayesian Nonparametrics. Ieee Trans Pattern Anal Mach Intell. 37: 209-11. PMID 26598765  0.36
2015 Favaro S, Nipoti B, Teh YW. Rediscovery of good-turing estimators via Bayesian nonparametrics. Biometrics. PMID 26224325 DOI: 10.1111/biom.12366  1
2015 Favaro S, Nipoti B, Teh YW. Random variate generation for Laguerre-type exponentially tilted α-stable distributions Electronic Journal of Statistics. 9: 1230-1242. DOI: 10.1214/15-EJS1033  1
2015 Adams RP, Fox EB, Sudderth EB, Teh YW. Guest editors' introduction to the special issue on bayesian nonparametrics Ieee Transactions On Pattern Analysis and Machine Intelligence. 37: 209-211. DOI: 10.1109/TPAMI.2014.2380478  1
2015 De Iorio M, Favaro S, Teh YW. Bayesian inference on population structure: From parametric to nonparametric modeling Nonparametric Bayesian Inference in Biostatistics. 135-152. DOI: 10.1007/978-3-319-19518-6_7  1
2015 Moreno PG, Artes-Rodríguez A, Teh YW, Perez-Cruz F. Bayesian nonparametric crowdsourcing Journal of Machine Learning Research. 16: 1607-1627.  1
2015 Lomelí M, Favaro S, Teh YW. A hybrid sampler for Poisson-Kingman mixture models Advances in Neural Information Processing Systems. 2015: 2161-2169.  1
2015 Lienart T, Teh YW, Doucet A. Expectation particle belief propagation Advances in Neural Information Processing Systems. 2015: 3609-3617.  1
2014 Caron F, Teh YW, Murphy TB. Bayesian nonparametric Plackett-Luce models for the analysis of preferences for college degree programmes Annals of Applied Statistics. 8: 1145-1181. DOI: 10.1214/14-AOAS717  1
2014 Favaro S, Lomeli M, Teh YW. On a class of (Formula presented.) -stable Poisson–Kingman models and an effective marginalized sampler Statistics and Computing. 25: 67-78. DOI: 10.1007/s11222-014-9499-4  1
2014 Xu M, Lakshminarayanan B, Teh YW, Zhu J, Zhang B. Distributed Bayesian posterior sampling via moment sharing Advances in Neural Information Processing Systems. 4: 3356-3364.  1
2014 Paige B, Wood F, Doucet A, Teh YW. Asynchronous anytime sequential Monte Carlo Advances in Neural Information Processing Systems. 4: 3410-3418.  1
2014 Lakshminarayanan B, Roy DM, Teh YW. Mondrian forests: Efficient online random forests Advances in Neural Information Processing Systems. 4: 3140-3148.  1
2013 Favaro S, Teh YW. MCMC for normalized random measure mixture models Statistical Science. 28: 335-359. DOI: 10.1214/13-STS422  1
2013 Rao V, Teh YW. Fast MCMC sampling for Markov jump processes and extensions Journal of Machine Learning Research. 14: 3295-3320.  1
2013 Patterson S, Teh YW. Stochastic gradient Riemannian Langevin dynamics on the probability simplex Advances in Neural Information Processing Systems 1
2013 Lakshminarayanan B, Roy DM, Teh YW. Top-down particle filtering for Bayesian decision trees 30th International Conference On Machine Learning, Icml 2013. 1317-1325.  1
2013 Chen C, Rao V, Buntine W, Teh Y. Dependent normalized random measures 30th International Conference On Machine Learning, Icml 2013. 2006-2014.  1
2012 Rao V, Teh YW. MCMC for continuous-time discrete-state systems Advances in Neural Information Processing Systems. 1: 701-709.  1
2012 Caron F, Teh YW. Bayesian nonparametric models for ranked data Advances in Neural Information Processing Systems. 2: 1520-1528.  1
2012 Mnih A, Teh YW. Learning label trees for probabilistic modelling of implicit feedback Advances in Neural Information Processing Systems. 4: 2816-2824.  1
2012 Elliott LT, Teh YW. Scalable imputation of genetic data with a discrete fragmentation- coagulation process Advances in Neural Information Processing Systems. 4: 2852-2860.  1
2012 Alexe B, Heess N, Teh YW, Ferrari V. Searching for objects driven by context Advances in Neural Information Processing Systems. 2: 881-889.  1
2012 Mnih A, Teh YW. A fast and simple algorithm for training neural probabilistic language models Proceedings of the 29th International Conference On Machine Learning, Icml 2012. 2: 1751-1758.  1
2011 Görür D, Teh YW. Concave-convex adaptive rejection sampling Journal of Computational and Graphical Statistics. 20: 670-691. DOI: 10.1198/jcgs.2011.09058  1
2011 Wood F, Gasthaus J, Archambeau C, James L, Teh YW. The sequence memoizer Communications of the Acm. 54: 91-98. DOI: 10.1145/1897816.1897842  1
2011 Blundell C, Teh YW, Heller KA. Discovering nonbinary hierarchical structures with Bayesian rose trees Mixtures: Estimation and Applications. 161-187. DOI: 10.1002/9781119995678.ch8  1
2011 Rao V, Teh YW. Gaussian process modulated renewal processes Advances in Neural Information Processing Systems 24: 25th Annual Conference On Neural Information Processing Systems 2011, Nips 2011 1
2011 Teh YW. Bayesian tools for natural language learning invited talk Conll 2011 - Fifteenth Conference On Computational Natural Language Learning, Proceedings of the Conference. 219.  1
2011 Silva R, Blundell C, Teh YW. Mixed cumulative distribution networks Journal of Machine Learning Research. 15: 670-678.  1
2011 Teh YW, Blundell C, Elliott LT. Modelling genetic variations with fragmentation-coagulation processes Advances in Neural Information Processing Systems 24: 25th Annual Conference On Neural Information Processing Systems 2011, Nips 2011 1
2011 Welling M, Teh YW. Bayesian learning via stochastic gradient langevin dynamics Proceedings of the 28th International Conference On Machine Learning, Icml 2011. 681-688.  1
2011 Rao V, Teh YW. Fast MCMC sampling for Markov jump processes and continuous time Bayesian networks Proceedings of the 27th Conference On Uncertainty in Artificial Intelligence, Uai 2011. 619-626.  1
2010 Gasthaus J, Teh YW. Improvements to the sequence memoizer Advances in Neural Information Processing Systems 23: 24th Annual Conference On Neural Information Processing Systems 2010, Nips 2010 1
2010 Blundell C, Teh YW, Heller KA. Bayesian rose trees Proceedings of the 26th Conference On Uncertainty in Artificial Intelligence, Uai 2010. 65-72.  1
2010 Teh YW, Titterington M. Preface Journal of Machine Learning Research. 9.  1
2009 Wood F, Archambeav C, Gasthaus J, James L, Teh YW. A stochastic memoizer for sequence data Proceedings of the 26th International Conference On Machine Learning, Icml 2009. 1129-1136. DOI: 10.1145/1553374.1553518  1
2009 Asuncion A, Welling M, Smyth P, Teh YW. On smoothing and inference for topic models Proceedings of the 25th Conference On Uncertainty in Artificial Intelligence, Uai 2009. 27-34.  1
2009 Chieu HL, Lee WS, Teh YW. Cooled and relaxed survey propagation for MRFs Advances in Neural Information Processing Systems 20 - Proceedings of the 2007 Conference 1
2009 Teh YW, Kurihara K, Welling M. Collapsed variational inference for HDP Advances in Neural Information Processing Systems 20 - Proceedings of the 2007 Conference 1
2009 Gasthaus J, Wood F, Görür D, Teh YW. Dependent Dirichlet process spike sorting Advances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference. 497-504.  1
2009 Teh YW, Daumé H, Roy D. Bayesian agglomerative clustering with coalescents Advances in Neural Information Processing Systems 20 - Proceedings of the 2007 Conference 1
2009 Quon G, Teh YW, Chan E, Hughes T, Brudno M, Morris Q. A mixture model for the evolution of gene expression in non-homogeneous datasets Advances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference. 1297-1304.  1
2009 Roy DM, Teh YW. The Mondrian process Advances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference. 1377-1384.  1
2009 Wood F, Teh YW. A hierarchical nonparametric Bayesian approach to statistical language model domain adaptation Journal of Machine Learning Research. 5: 607-614.  1
2009 Heller KA, Teh YW, Görür D. Infinite hierarchical hidden Markov models Journal of Machine Learning Research. 5: 224-231.  1
2009 Teh YW, Gorür D. Indian buffet processes with power-law behavior Advances in Neural Information Processing Systems 22 - Proceedings of the 2009 Conference. 1838-1846.  1
2009 Van Gael J, Teh YW, Ghahramani Z. The infinite factorial hidden Markov model Advances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference. 1697-1704.  1
2009 Rao V, Teh YW. Spatial normalized gamma processes Advances in Neural Information Processing Systems 22 - Proceedings of the 2009 Conference. 1554-1562.  1
2009 Haffari G, Teh YW. Hierarchical dirichlet trees for information retrieval Naacl Hlt 2009 - Human Language Technologies: the 2009 Annual Conference of the North American Chapter of the Association For Computational Linguistics, Proceedings of the Conference. 173-181.  1
2008 Welling M, Teh YW, Kappen B. Hybrid variational/Gibbs collapsed inference in topic models Proceedings of the 24th Conference On Uncertainty in Artificial Intelligence, Uai 2008. 587-594.  1
2008 Van Gael J, Saatci Y, Teh YW, Ghahramani Z. Beam sampling for the infinite hidden Markov model Proceedings of the 25th International Conference On Machine Learning. 1088-1095.  1
2007 Teh YW, Görür D, Ghahramani Z. Stick-breaking construction for the Indian buffet process Journal of Machine Learning Research. 2: 556-563.  1
2007 Kurihara K, Welling M, Teh YW. Collapsed variational dirichlet process mixture models Ijcai International Joint Conference On Artificial Intelligence. 2796-2801.  1
2007 Cai JF, Lee WS, Teh YW. Improving word sense disambiguation using topic features Emnlp-Conll 2007 - Proceedings of the 2007 Joint Conference On Empirical Methods in Natural Language Processing and Computational Natural Language Learning. 1015-1023.  1
2007 Teh YW, Newman D, Welling M. A collapsed variational Bayesian inference algorithm for latent Dirichlet allocation Advances in Neural Information Processing Systems. 1353-1360.  1
2006 Hinton G, Osindero S, Welling M, Teh YW. Unsupervised discovery of nonlinear structure using contrastive backpropagation. Cognitive Science. 30: 725-31. PMID 21702832 DOI: 10.1207/s15516709cog0000_76  1
2006 Hinton GE, Osindero S, Teh YW. A fast learning algorithm for deep belief nets. Neural Computation. 18: 1527-54. PMID 16764513 DOI: 10.1162/neco.2006.18.7.1527  1
2006 Teh YW, Jordan MI, Beal MJ, Blei DM. Hierarchical Dirichlet processes Journal of the American Statistical Association. 101: 1566-1581. DOI: 10.1198/016214506000000302  1
2006 Xing EP, Sohn KA, Jordan MI, Teh YW. Bayesian multi-population haplotype inference via a hierarchical dirichlet process mixture Acm International Conference Proceeding Series. 148: 1049-1056. DOI: 10.1145/1143844.1143976  1
2006 Teh YW. A hierarchical Bayesian language model based on Pitman-Yor processes Coling/Acl 2006 - 21st International Conference On Computational Linguistics and 44th Annual Meeting of the Association For Computational Linguistics, Proceedings of the Conference. 1: 985-992.  1
2005 Teh YW, Seeger M, Jordan MI. Semiparametric latent factor models Aistats 2005 - Proceedings of the 10th International Workshop On Artificial Intelligence and Statistics. 333-340.  1
2005 Welling M, Minka TP, Teh YW. Structured region graphs: Morphing EP into GBP Proceedings of the 21st Conference On Uncertainty in Artificial Intelligence, Uai 2005. 609-616.  1
2005 Teh YW, Jordan MI, Beal MJ, Blei DM. Sharing clusters among related groups: Hierarchical dirichlet processes Advances in Neural Information Processing Systems 1
2004 Welling M, Teh YW. Linear response algorithms for approximate inference in graphical models. Neural Computation. 16: 197-221. PMID 15006029 DOI: 10.1162/08997660460734056  1
2004 Welling M, Rosen-Zvi M, Teh YW. Approximate inference by Markov chains on union spaces Proceedings, Twenty-First International Conference On Machine Learning, Icml 2004. 847-854.  1
2004 Berg TL, Berg AC, Edwards J, Maire M, White R, Teh YW, Learned-Miller E, Forsyth DA. Names and faces in the news Proceedings of the Ieee Computer Society Conference On Computer Vision and Pattern Recognition. 2: II848-II854.  1
2004 Teh YW, Welling M, Osindero S, Hinton GE. Energy-based models for sparse overcomplete representations Journal of Machine Learning Research. 4: 1235-1260.  1
2004 Welling M, Teh YW. Linear response for approximate inference Advances in Neural Information Processing Systems 1
2003 Welling M, Teh YW. Approximate inference in Boltzmann machines Artificial Intelligence. 143: 19-50. DOI: 10.1016/S0004-3702(02)00361-2  1
2003 Teh YW, Roweis S. Automatic alignment of local representations Advances in Neural Information Processing Systems 1
2002 Teh YW, Welling M. The unified propagation and scaling algorithm Advances in Neural Information Processing Systems 1
2001 Teh YW, Hinton GE. Rate-coded restricted boltzmann machines for face recognition Advances in Neural Information Processing Systems 1
2000 Hinton GE, Ghahramani Z, Teh YW. Learning to parse images Advances in Neural Information Processing Systems. 463-469.  1
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