Yee Whye Teh : Research : Papers
In Submission/Print
2011
Modelling Genetic Variations using Fragmentation-Coagulation Processes.
Y. W. Teh, C. Blundell and L. T. Elliott. NIPS 2011.
[bibtex] [pdf] [slides] [NIPS 2011]Gaussian Process Modulated Renewal Processes.
V. Rao and Y. W. Teh. NIPS 2011.
[bibtex] [pdf] [supplementary] [slides] [NIPS 2011]Fast MCMC sampling for Markov jump processes and continuous time Bayesian networks.
V. Rao and Y. W. Teh. UAI 2011.
[bibtex] [pdf] [djvu] [slides] [UAI 2011]Bayesian Learning via Stochastic Gradient Langevin Dynamics.
M. Welling and Y. W. Teh. ICML 2011.
[bibtex] [pdf] [djvu] [slides] [ICML 2011]Mixed Cumulative Distribution Networks.
R. Silva, C. Blundell and Y. W. Teh. AISTATS 2011.
[bibtex] [pdf] [djvu] [supplemental] [AISTATS 2011]Concave-Convex Adaptive Rejection Sampling.
D. Gorur and Y.W. Teh. JCGS, 2011.
[bibtex] [pdf] [djvu] [JCGS]The Sequence Memoizer.
F. Wood, C. Archambeau, J. Gasthaus, L. F. James and Y.W. Teh. CACM, 54(2):91-98, Feb 2011.
[bibtex] [pdf] [djvu] [slides] [CACM]Discovering Non-binary Hierarchical Structures with Bayesian Rose Trees.
C. Blundell, Y.W. Teh and K.A. Heller. In Mixture Estimation and Applications.
[bibtex] [pdf] [djvu] [slides] [Workshop] [Wiley] [UAI version]
2010
Improvements to the Sequence Memoizer.
J. Gasthaus and Y.W. Teh. NIPS 2010.
[bibtex] [pdf] [djvu] [supplemental] [slides] [NIPS 2010]Bayesian Rose Trees.
C. Blundell, Y.W. Teh and K.A. Heller. UAI 2010.
[bibtex] [pdf] [djvu] [slides] [UAI 2010] [Book chapter version]Bayesian Nonparametric Models.
P. Orbanz and Y.W. Teh. Encyclopedia of Machine Learning, to appear. Springer.
[bibtex] [pdf] [djvu] [Encyclopedia of Machine Learning]Dirichlet Processes.
Y.W. Teh. Encyclopedia of Machine Learning, to appear. Springer.
[bibtex] [pdf] [djvu] [Encyclopedia of Machine Learning]Lossless Compression based on the Sequence Memoizer.
J. Gasthaus and F. Wood and Y.W. Teh. DCC 2010.
[bibtex] [pdf] [djvu] [slides] [DCC 2010]
2009
Indian Buffet Processes with Power-law Behavior.
Y.W. Teh and D. Gorur. NIPS 2009.
[bibtex] [pdf] [djvu] [NIPS 2009]Spatial Normalized Gamma Processes.
V. Rao and Y.W. Teh. NIPS 2009.
[bibtex] [pdf] [djvu] [NIPS 2009]Hierarchical Bayesian Nonparametric Models with Applications .
Y.W. Teh and M.I. Jordan. Bayesian Nonparametrics, 2010. Cambridge University Press.
[bibtex] [pdf] [djvu] [Cambridge University Press]On Smoothing and Inference for Topic Models.
A. Asuncion, M. Welling, P. Smyth and Y.W. Teh. UAI 2009.
[bibtex] [pdf] [UAI 2009]A Stochastic Memoizer for Sequence Data.
F. Wood, C. Archambeau, J. Gasthaus, L. F. James and Y.W. Teh. ICML 2009.
[bibtex] [pdf] [ICML 2009]Variational Inference for the Indian Buffet Process.
F. Doshi, K. T. Miller, J. Van Gael and Y.W. Teh. AISTATS 2009.
[bibtex] [pdf] [AISTATS 2009]Infinite Hierarchical Hidden Markov Models.
K. Heller, Y.W. Teh and D. Gorur. AISTATS 2009.
[bibtex] [pdf] [AISTATS 2009]A Hierarchical Nonparametric Bayesian Approach to Statistical Language Model Domain Adaptation.
F. Wood and Y.W. Teh. AISTATS 2009.
[bibtex] [pdf] [AISTATS 2009]Hierarchical Dirichlet Trees for Information Retrieval.
G.R. Haffari and Y.W. Teh. NAACL-HLT 2009.
[bibtex] [pdf] [NAACL-HLT 2009]
2008
The Mondrian Process.
D.M. Roy and Y.W. Teh. NIPS 2008.
[bibtex] [pdf] [djvu] [NIPS 2008]An Efficient Sequential Monte-Carlo Algorithm for Coalescent Clustering.
D. Gorur and Y.W. Teh. NIPS 2008.
[bibtex] [pdf] [djvu] [NIPS 2008]The Infinite Factorial Hidden Markov Model.
J. Van Gael, Y.W. Teh and Z. Ghahramani. NIPS 2008.
[bibtex] [pdf] [djvu] [NIPS 2008]Dependent Dirichlet Process Spike Sorting.
J. Gasthaus, F. Wood, D. Gorur and Y.W. Teh. NIPS 2008.
[bibtex] [pdf] [djvu] [NIPS 2008]A Mixture Model for the Evolution of Gene Expression in Non-homogeneous Datasets.
G. Quon, Y.W. Teh, E. Chan, M. Brudno, T. Hughes and Q.D. Morris. NIPS 2008.
[bibtex] [pdf] [djvu] [NIPS 2008]Hybrid Variational/Gibbs Inference in Topic Models.
M. Welling, Y.W. Teh and B. Kappen UAI 2008.
[bibtex] [pdf] [djvu] [UAI 2008]Beam Sampling for the Infinite Hidden Markov Model.
J. Van Gael, Y. Saatci, Y.W. Teh and Z. Ghahramani. ICML 2008.
[bibtex] [pdf] [djvu] [ICML 2008] [code] [presentation]
2007
Names and Faces.
T.L. Berg, A.C. Berg, J. Edwards, M. Maire, R. White, Y.W. Teh, E. Learned-Miller, D.A. Forsyth. Submitted.
[bibtex] [pdf]Bayesian Agglomerative Clustering with Coalescents.
Y.W. Teh, H. Daume III and D.M. Roy. NIPS 2007.
[bibtex] [pdf] [djvu] [NIPS 2007]Collapsed Variational Inference for HDP.
Y.W. Teh, K. Kurihara and M. Welling. NIPS 2007.
[bibtex] [pdf] [djvu] [NIPS 2007]Cooled and Relaxed Survey Propagation for MRFs.
H.L. Chieu, W.S. Lee and Y.W. Teh. NIPS 2007.
[bibtex] [pdf] [djvu] [NIPS 2007] [proof.pdf] [proof.djvu]Variational Bayesian Approach to Movie Rating Prediction.
Y.J. Lim and Y.W. Teh. KDD Cup and Workshop 2007.
[bibtex] [pdf] [djvu] [KDD Cup 2007]Improving Word Sense Disambiguation Using Topic Features.
J.F. Cai, W.S. Lee and Y.W. Teh. EMNLP 2007.
[bibtex] [pdf] [djvu] [EMNLP 2007] [SemEval version]NUS-ML: Improving Word Sense Disambiguation Using Topic Features.
J.F. Cai, W.S. Lee and Y.W. Teh. SemEval 2007.
[bibtex] [pdf] [djvu] [SemEval 2007] [EMNLP version]Stick-breaking Construction for the Indian Buffet Process.
Y.W. Teh, D. Gorur and Z. Ghahramani. AISTATS 2007.
[bibtex] [pdf] [ps.gz] [djvu] [AISTATS 2007]Collapsed Variational Dirichlet Process Mixture Models.
K. Kurihara, M. Welling and Y.W. Teh. IJCAI 2007.
[bibtex] [pdf] [ps.gz] [djvu] [IJCAI 2007]
2006
Hierarchical Dirichlet Processes.
Y.W. Teh, M.I. Jordan, M.J. Beal and D.M. Blei. JASA 101(476):1566-1581, 2006.
[bibtex] [pdf] [ps.gz] [djvu] [JASA] [NIPS version] [tech report version]A Collapsed Variational Bayesian Inference Algorithm for Latent Dirichlet Allocation.
Y.W. Teh, D. Newman and M. Welling. NIPS 2006.
[bibtex] [pdf] [ps.gz] [djvu] [NIPS 2006]A Fast Learning Algorithm For Deep Belief Networks.
G.E. Hinton, S. Osindero and Y.W. Teh. Neural Computation 18(7):1527-1554, 2006.
[bibtex] [pdf] [ps.gz] [djvu] [Neural Computation]Unsupervised Discovery of Non-Linear Structure using Contrastive Backpropagation.
G.E. Hinton, S. Osindero, M. Welling and Y.W. Teh. Cognitive Science 30:4, 2006.
[bibtex] [pdf] [ps.gz] [djvu] [Cognitive Science]A Hierarchical Bayesian Language Model based on Pitman-Yor Processes.
Y.W. Teh. Coling/ACL 2006.
[bibtex] [pdf] [ps.gz] [djvu] [Coling/ACL 2006] [tech report version]Bayesian Multi-Population Haplotype Inference via a Hierarchical Dirichlet Process Mixture.
E.P. Xing, K.-A. Sohn, M.I. Jordan and Y.W. Teh. ICML 2006.
[bibtex] [pdf] [ps.gz] [djvu] [ICML 2006]Semi-supervised Learning in Reproducing Kernel Hilbert Spaces Using Local Invariances.
W.S. Lee, X. Zhang and Y.W. Teh. Technical Report TRB3/06, School of Computing, NUS, 2006.
[bibtex] [pdf] [ps.gz] [djvu] [School of Computing, NUS]A Bayesian Interpretation of Interpolated Kneser-Ney.
Y.W. Teh. Technical Report TRA2/06, School of Computing, NUS, revised 2006.
[bibtex] [pdf] [ps.gz] [djvu] [School of Computing, NUS] [Coling/ACL version]
2005
Structured Region Graphs: Morphing EP into GBP.
M. Welling, T. Minka and Y.W. Teh. UAI 2005. Extended version with proofs.
[bibtex] [pdf] [ps.gz] [djvu] [UAI 2005]Semiparametric Latent Factor Models.
Y.W. Teh, M. Seeger and M.I. Jordan. AISTATS 2005.
[bibtex] [pdf] [ps.gz] [djvu] [AISTATS 2005] [tech report version]Semiparametric Latent Factor Models.
M. Seeger, Y.W. Teh and M.I. Jordan. Technical Report, Computer Science, UC Berkeley, 2005.
[bibtex] [pdf] [ps.gz] [djvu] [Computer Science, UC Berkeley] [AISTATS version]
2004
Sharing Clusters among Related Groups: Hierarchical Dirichlet Processes.
Y.W. Teh, M.I. Jordan, M.J. Beal and D.M. Blei. NIPS 2004.
[bibtex] [pdf] [ps.gz] [djvu] [NIPS 2004] [JASA version]Making Latin Manuscripts Searchable using gHMM's.
J. Edwards, Y.W. Teh, D.A. Forsyth, M. Maire, R. Bock and G. Vesom. NIPS 2004.
[bibtex] [pdf] [ps.gz] [djvu] [NIPS 2004]Faces and Names in the News.
T. Miller, A.C. Berg, J. Edwards, M. Maire, R. White, Y.W. Teh, E. Learned-Miller, D.A. Forsyth. CVPR 2004.
[bibtex] [pdf] [djvu] [CVPR 2004]Approximate Inference by Markov Chains on Union Spaces.
M. Welling, M. Rosen-Zvi and Y.W. Teh. ICML 2004.
[bibtex] [pdf] [ps.gz] [djvu] [ICML 2004]Hierarchical Dirichlet Processes.
Y.W. Teh, M.I. Jordan, M.J. Beal and D.M. Blei. Technical Report 653, Statistics, UC Berkeley, 2004.
[bibtex] [pdf] [ps.gz] [djvu] [Statistics, UC Berkeley] [JASA version]Linear Response Algorithms for Approximate Inference in Graphical Models.
M. Welling and Y.W. Teh. Neural Computation 16:197-221, 2004.
[bibtex] [pdf] [ps.gz] [djvu] [Neural Computation] [NIPS version]
2003
Linear Response Algorithms for Approximate Inference.
M. Welling and Y.W. Teh. NIPS 2003.
[bibtex] [pdf] [ps.gz] [djvu] [NIPS 2003] [Neural Computation version]Energy-Based Models for Sparse Overcomplete Representations.
Y.W. Teh, M. Welling, S. Osindero and G.E. Hinton. JMLR 4(Dec):1235-1260, 2003.
[bibtex] [pdf] [ps.gz] [djvu] [JMLR]Bethe Free Energy and Contrastive Divergence Approximations for Undirected Graphical Models.
Y.W. Teh. Ph.D. Thesis, 2003. University of Toronto.
[bibtex] [pdf] [ps.gz] [djvu] [Computer Science, Toronto]On Improving the Efficiency of the Iterative Proportional Fitting Procedure.
Y.W. Teh and M. Welling. AISTATS 2003.
[bibtex] [pdf] [ps.gz] [djvu] [AISTATS 2003]Approximate Inference in Boltzmann Machines.
M. Welling and Y.W. Teh. Artificial Intelligence 143(1):19-50, 2003.
[bibtex] [pdf] [ps.gz] [djvu] [Artificial Intelligence] [UAI version]
2002
Automatic Alignment of Local Representations.
Y.W. Teh and S. Roweis. NIPS 2002.
[bibtex] [pdf] [ps.gz] [djvu] [NIPS 2002]An Alternate Objective Function for Markovian Fields.
S. Kakade, Y.W. Teh and S. Roweis. ICML 2002.
[bibtex] [pdf] [ps.gz] [djvu] [ICML 2002]
2001
A New View of ICA.
G.E. Hinton, M. Welling, Y.W. Teh and S. Osindero. ICA 2001.
[bibtex] [pdf] [ps.gz] [djvu] [ICA 2001] [JMLR version]Discovering Multiple Constraints that are Frequently Approximately Satisfied.
G.E. Hinton and Y.W. Teh. UAI 2001.
[bibtex] [pdf] [ps.gz] [djvu] [UAI 2001]The Unified Propagation and Scaling Algorithm.
Y.W. Teh and M. Welling. NIPS 2001.
[bibtex] [pdf] [ps.gz] [djvu] [NIPS 2001]Belief Optimization for Binary Networks: A Stable Alternative to Loopy Belief Propagation.
M. Welling and Y.W. Teh. UAI 2001.
[bibtex] [pdf] [ps.gz] [djvu] [UAI 2001] [Artificial Intelligence version]Passing and Bouncing Messages for Generalized Inference.
Y.W. Teh and M. Welling. Technical Report 2001-001, Gatsby Unit, UCL.
[bibtex] [pdf] [ps.gz] [djvu] [Gatsby Unit]
2000
Rate-coded Restricted Boltzmann Machines for Face Recognition.
Y.W. Teh and G.E. Hinton. NIPS 2000.
[bibtex] [pdf] [ps.gz] [djvu] [NIPS 2000]Learning to Parse Images.
Y.W. Teh, 2000. Master's thesis, University of Toronto.
[bibtex] [pdf] [ps.gz] [djvu] [Computer Science, Toronto] [NIPS version]
1999
Learning to Parse Images.
G.E. Hinton, Z. Ghahramani and Y.W. Teh. NIPS 1999.
[bibtex] [pdf] [ps.gz] [djvu] [NIPS 1999] [thesis version]
1998
Making Forward Chaining Relevant.
F. Bacchus and Y.W. Teh. AIPS 1998.
[bibtex] [pdf] [ps.gz] [djvu] [AIPS 1998]
Other Reports
Incremental conservative visibility with general occluders.
Y.W. Teh and H. Zhang. CSC2522F Project, 1999.
[pdf] [ps.gz] [djvu]Wagner's conjecture.
Y.W. Teh, CSC2410S Project, 1999.
[pdf] [ps.gz] [djvu]An attention model and steerable filters.
Y.W. Teh. CSC2523S Project, 1999.
[pdf] [ps.gz] [djvu]Representing coastlines with linear transforms.
Y.W. Teh. CSC2508S Project, 2000.
[pdf] [ps.gz] [djvu]