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All publications by Yee W. Teh
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A hybrid sampler for Poisson-Kingman mixture models
Maria Lomeli, Stefano Favaro and Yee W. Teh
Advances in Neural Information Processing Systems 28, 2015


Expectation Particle Belief Propagation
Thibaut Lienart, Yee W. Teh and Arnaud Doucet
Advances in Neural Information Processing Systems 28, 2015


Distributed Bayesian Posterior Sampling via Moment Sharing
Minjie Xu, Balaji Lakshminarayanan, Yee W. Teh, Jun Zhu and Bo Zhang
Advances in Neural Information Processing Systems 27, 2014


Asynchronous Anytime Sequential Monte Carlo
Brooks Paige, Frank Wood, Arnaud Doucet and Yee W. Teh
Advances in Neural Information Processing Systems 27, 2014


Mondrian Forests: Efficient Online Random Forests
Balaji Lakshminarayanan, Daniel M. Roy and Yee W. Teh
Advances in Neural Information Processing Systems 27, 2014


Top-down particle filtering for Bayesian decision trees
Balaji Lakshminarayanan, Daniel Roy and Yee W. Teh
Proceedings of the 30th International Conference on Machine Learning (ICML-13), 2013


Dependent Normalized Random Measures
Changyou Chen, Vinayak Rao, Wray Buntine and Yee W. Teh
Proceedings of the 30th International Conference on Machine Learning (ICML-13), 2013


Bayesian Hierarchical Community Discovery
Charles Blundell and Yee W. Teh
Advances in Neural Information Processing Systems 26, 2013


Learning with Invariance via Linear Functionals on Reproducing Kernel Hilbert Space
Xinhua Zhang, Wee S. Lee and Yee W. Teh
Advances in Neural Information Processing Systems 26, 2013


Stochastic Gradient Riemannian Langevin Dynamics on the Probability Simplex
Sam Patterson and Yee W. Teh
Advances in Neural Information Processing Systems 26, 2013


Scalable imputation of genetic data with a discrete fragmentation-coagulation process
Lloyd Elliott and Yee W. Teh
Advances in Neural Information Processing Systems 25, 2012


Learning Label Trees for Probabilistic Modelling of Implicit Feedback
Andriy Mnih and Yee W. Teh
Advances in Neural Information Processing Systems 25, 2012


Bayesian nonparametric models for ranked data
Francois Caron and Yee W. Teh
Advances in Neural Information Processing Systems 25, 2012


Searching for objects driven by context
Bogdan Alexe, Nicolas Heess, Yee W. Teh and Vittorio Ferrari
Advances in Neural Information Processing Systems 25, 2012


MCMC for continuous-time discrete-state systems
Vinayak Rao and Yee W. Teh
Advances in Neural Information Processing Systems 25, 2012


A fast and simple algorithm for training neural probabilistic language models
Andriy Mnih and Yee W. Teh
Proceedings of the 29th International Conference on Machine Learning (ICML-12), 2012


Gaussian process modulated renewal processes
Yee W. Teh and Vinayak Rao
Advances in Neural Information Processing Systems 24, 2011


Modelling Genetic Variations using Fragmentation-Coagulation Processes
Yee W. Teh, Charles Blundell and Lloyd Elliott
Advances in Neural Information Processing Systems 24, 2011


Bayesian Learning via Stochastic Gradient Langevin Dynamics
Max Welling and Yee W. Teh
Proceedings of the 28th International Conference on Machine Learning (ICML-11), 2011


Mixed Cumulative Distribution Networks
Ricardo Silva, Charles Blundell and Yee W. Teh
Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics (AISTATS-11), 2011


Improvements to the Sequence Memoizer
Jan Gasthaus and Yee W. Teh
Advances in Neural Information Processing Systems 23, 2010


A stochastic memoizer for sequence data
Frank Wood, Cédric Archambeau, Jan Gasthaus, Lancelot James and Yee W. Teh
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Spatial Normalized Gamma Processes
Vinayak Rao and Yee W. Teh
Advances in Neural Information Processing Systems 22, 2009


Indian Buffet Processes with Power-law Behavior
Yee W. Teh and Dilan Gorur
Advances in Neural Information Processing Systems 22, 2009


A Hierarchical Nonparametric Bayesian Approach to Statistical Language Model Domain Adaptation
Frank Wood and Yee W. Teh
Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics (AISTATS-09), 2009


Infinite Hierarchical Hidden Markov Models
Katherine A. Heller, Yee W. Teh and Dilan Görür
Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics (AISTATS-09), 2009


Variational Inference for the Indian Buffet Process
Finale Doshi, Kurt Miller, Jurgen V. Gael and Yee W. Teh
Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics (AISTATS-09), 2009


Beam sampling for the infinite hidden Markov model
Jurgen V. Gael, Yunus Saatci, Yee W. Teh and Zoubin Ghahramani
Proceedings of the 25th International Conference on Machine Learning (ICML-08), 2008


An Efficient Sequential Monte Carlo Algorithm for Coalescent Clustering
Dilan Gorur and Yee W. Teh
Advances in Neural Information Processing Systems 21, 2008


Dependent Dirichlet Process Spike Sorting
Jan Gasthaus, Frank Wood, Dilan Gorur and Yee W. Teh
Advances in Neural Information Processing Systems 21, 2008


The Mondrian Process
Daniel M. Roy and Yee W. Teh
Advances in Neural Information Processing Systems 21, 2008


A mixture model for the evolution of gene expression in non-homogeneous datasets
Gerald Quon, Yee W. Teh, Esther Chan, Timothy Hughes, Michael Brudno and Quaid D. Morris
Advances in Neural Information Processing Systems 21, 2008


The Infinite Factorial Hidden Markov Model
Jurgen V. Gael, Yee W. Teh and Zoubin Ghahramani
Advances in Neural Information Processing Systems 21, 2008


Cooled and Relaxed Survey Propagation for MRFs
Hai L. Chieu, Wee S. Lee and Yee W. Teh
Advances in Neural Information Processing Systems 20, 2007


Collapsed Variational Inference for HDP
Yee W. Teh, Kenichi Kurihara and Max Welling
Advances in Neural Information Processing Systems 20, 2007


Bayesian Agglomerative Clustering with Coalescents
Yee W. Teh, Daniel J. Hsu and Hal Daume
Advances in Neural Information Processing Systems 20, 2007


Stick-breaking Construction for the Indian Buffet Process
Yee W. Teh, Dilan Görür and Zoubin Ghahramani
Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics (AISTATS-07), 2007


Bayesian multi-population haplotype inference via a hierarchical dirichlet process mixture
Eric P. Xing, Kyung-ah Sohn, Michael I. Jordan and Yee W. Teh
Proceedings of the 23th International Conference on Machine Learning (ICML-06), 2006


A Collapsed Variational Bayesian Inference Algorithm for Latent Dirichlet Allocation
Yee W. Teh, David Newman and Max Welling
Advances in Neural Information Processing Systems 19, 2006


Approximate inference by Markov chains on union spaces
Max Welling, Michal Rosen-zvi and Yee W. Teh
Proceedings of the 21st International Conference on Machine Learning (ICML-04), 2004


Sharing Clusters among Related Groups: Hierarchical Dirichlet Processes
Yee W. Teh, Michael I. Jordan, Matthew J. Beal and David M. Blei
Advances in Neural Information Processing Systems 17, 2004


Making Latin Manuscripts Searchable using gHMM's
Jaety Edwards, Yee W. Teh, Roger Bock, Michael Maire, Grace Vesom and David A. Forsyth
Advances in Neural Information Processing Systems 17, 2004


Energy-Based Models for Sparse Overcomplete Representations
Yee W. Teh, Max Welling, Simon Osindero and Geoffrey E. Hinton
Journal of Machine Learning Research, 2003


Linear Response for Approximate Inference
Max Welling and Yee W. Teh
Advances in Neural Information Processing Systems 16, 2003


Automatic Alignment of Local Representations
Yee W. Teh and Sam T. Roweis
Advances in Neural Information Processing Systems 15, 2002


The Unified Propagation and Scaling Algorithm
Yee W. Teh and Max Welling
Advances in Neural Information Processing Systems 14, 2001