| 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 |
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| 2016 |
Teh YW, Thiery AH, Vollmer SJ. Consistency and fluctuations for stochastic gradient Langevin dynamics Journal of Machine Learning Research. 17. |
1 |
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| 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 |
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| 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 |
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| 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 |
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| 2015 |
Moreno PG, Artes-Rodríguez A, Teh YW, Perez-Cruz F. Bayesian nonparametric crowdsourcing Journal of Machine Learning Research. 16: 1607-1627. |
1 |
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| 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 |
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| 2015 |
Lienart T, Teh YW, Doucet A. Expectation particle belief propagation Advances in Neural Information Processing Systems. 2015: 3609-3617. |
1 |
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| 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 |
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| 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 |
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| 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 |
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| 2014 |
Paige B, Wood F, Doucet A, Teh YW. Asynchronous anytime sequential Monte Carlo Advances in Neural Information Processing Systems. 4: 3410-3418. |
1 |
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| 2014 |
Lakshminarayanan B, Roy DM, Teh YW. Mondrian forests: Efficient online random forests Advances in Neural Information Processing Systems. 4: 3140-3148. |
1 |
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| 2013 |
Favaro S, Teh YW. MCMC for normalized random measure mixture models Statistical Science. 28: 335-359. DOI: 10.1214/13-STS422 |
1 |
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| 2013 |
Rao V, Teh YW. Fast MCMC sampling for Markov jump processes and extensions Journal of Machine Learning Research. 14: 3295-3320. |
1 |
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| 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 |
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| 2013 |
Chen C, Rao V, Buntine W, Teh Y. Dependent normalized random measures 30th International Conference On Machine Learning, Icml 2013. 2006-2014. |
1 |
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| 2012 |
Rao V, Teh YW. MCMC for continuous-time discrete-state systems Advances in Neural Information Processing Systems. 1: 701-709. |
1 |
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| 2012 |
Caron F, Teh YW. Bayesian nonparametric models for ranked data Advances in Neural Information Processing Systems. 2: 1520-1528. |
1 |
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| 2012 |
Mnih A, Teh YW. Learning label trees for probabilistic modelling of implicit feedback Advances in Neural Information Processing Systems. 4: 2816-2824. |
1 |
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| 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 |
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| 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 |
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| 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 |
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| 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 |
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| 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 |
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| 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 |
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| 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 |
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| 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 |
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| 2011 |
Silva R, Blundell C, Teh YW. Mixed cumulative distribution networks Journal of Machine Learning Research. 15: 670-678. |
1 |
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| 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 |
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| 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 |
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| 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 |
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| 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 |
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| 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 |
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| 2010 |
Teh YW, Titterington M. Preface Journal of Machine Learning Research. 9. |
1 |
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| 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 |
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| 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 |
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| 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 |
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| 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 |
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| 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 |
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| 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 |
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| 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 |
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| 2009 |
Roy DM, Teh YW. The Mondrian process Advances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference. 1377-1384. |
1 |
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| 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 |
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| 2009 |
Heller KA, Teh YW, Görür D. Infinite hierarchical hidden Markov models Journal of Machine Learning Research. 5: 224-231. |
1 |
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| 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 |
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| 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 |
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| 2009 |
Rao V, Teh YW. Spatial normalized gamma processes Advances in Neural Information Processing Systems 22 - Proceedings of the 2009 Conference. 1554-1562. |
1 |
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| 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 |
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| 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 |
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| 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 |
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| 2007 |
Teh YW, Görür D, Ghahramani Z. Stick-breaking construction for the Indian buffet process Journal of Machine Learning Research. 2: 556-563. |
1 |
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| 2007 |
Kurihara K, Welling M, Teh YW. Collapsed variational dirichlet process mixture models Ijcai International Joint Conference On Artificial Intelligence. 2796-2801. |
1 |
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| 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 |
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| 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 |
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| 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 |
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| 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 |
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| 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 |
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| 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 |
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| 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 |
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| 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 |
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| 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 |
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| 2005 |
Teh YW, Jordan MI, Beal MJ, Blei DM. Sharing clusters among related groups: Hierarchical dirichlet processes Advances in Neural Information Processing Systems. |
1 |
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| 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 |
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| 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 |
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| 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 |
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| 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 |
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| 2004 |
Welling M, Teh YW. Linear response for approximate inference Advances in Neural Information Processing Systems. |
1 |
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| 2003 |
Welling M, Teh YW. Approximate inference in Boltzmann machines Artificial Intelligence. 143: 19-50. DOI: 10.1016/S0004-3702(02)00361-2 |
1 |
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| 2003 |
Teh YW, Roweis S. Automatic alignment of local representations Advances in Neural Information Processing Systems. |
1 |
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| 2002 |
Teh YW, Welling M. The unified propagation and scaling algorithm Advances in Neural Information Processing Systems. |
1 |
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| 2001 |
Teh YW, Hinton GE. Rate-coded restricted boltzmann machines for face recognition Advances in Neural Information Processing Systems. |
1 |
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| 2000 |
Hinton GE, Ghahramani Z, Teh YW. Learning to parse images Advances in Neural Information Processing Systems. 463-469. |
1 |
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