Research : Projects
This page is last updated in 2014. To see an updated publication page, check ou t my MLCS page or my Google Schol ar page.Nonparametric Bayes
Bayesian Nonparametric Crowdsourcing.
P. G. Moreno, Y. W. Teh, F. Perez-Cruz, A. Artes-Rodriguez.
[bibtex] [arxiv]A Marginal Sampler for sigma-Stable Poisson-Kingman Mixture Models.
M. Lomeli, S. Favaro and Y. W. Teh.
[bibtex] [arxiv]Adaptive Reconfiguration Moves for Dirichlet Mixtures.
T. Herlau, M. Morup, Y. W. Teh and M. N. Schmidt.
[bibtex] [arxiv]Rediscovery of Good-Turing estimators via Bayesian nonparametrics.
S. Favaro, B. Nipoti and Y. W. Teh.
[bibtex] [arxiv]Mondrian Forests: Efficient Online Random Forests.
B. Lakshminarayanan, D. M. Roy and Y. W. Teh. NIPS 2014.
[bibtex] [arxiv] [code] [NIPS 2014]On a Class of sigma-Stable Poisson-Kingman Models and an Effective Marginalized Sampler.
S. Favaro, M. Lomeli and Y. W. Teh. Statistics and Computing 2014.
[bibtex] [euclid]On the Stick-Breaking Representation of sigma-Stable Poisson-Kingman Models.
S. Favaro, M. Lomeli, B. Nipoti and Y. W. Teh. Electronic Journal of Statistics 2014.
[bibtex] [doi] [euclid]Bayesian Nonparametric Plackett-Luce Models for the Analysis of Clustered Ranked Data.
F. Caron, Y. W. Teh and B. T. Murphy. Annals of Applied Statistics 2014.
[bibtex] [doi] [arxiv]Bayesian Hierarchical Community Discovery.
C. Blundell and Y. W. Teh. NIPS 2013.
[bibtex] [pdf]MCMC for Normalized Random Measure Mixture Models.
S. Favaro and Y. W. Teh. Statistical Science 28(3):335-359, 2013.
[bibtex] [pdf] [slides]Dependent Normalized Random Measures.
C. Chen, V. Rao, W. Buntine, Y. W. Teh. ICML 2013.
[bibtex] [pdf] [supplementary]Bayesian Nonparametric Models for Ranked Data.
F. Caron and Y. W. Teh. NIPS 2012.
[bibtex] [pdf] [supplementary] [NIPS 2012]Scalable Imputation of Genetic Data with a Discrete Fragmentation-Coagulation Process.
L. T. Elliott and Y. W. Teh. NIPS 2012.
[bibtex] [pdf] [NIPS 2012]Modelling Genetic Variations using Fragmentation-Coagulation Processes.
Y. W. Teh, C. Blundell and L. T. Elliott. NIPS 2011.
[bibtex] [pdf] [video] [NIPS 2011]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] [CACM]Based on the following conference papers:
Improvements to the Sequence Memoizer.
J. Gasthaus and Y.W. Teh. NIPS 2010.
[bibtex] [pdf] [supplemental] [NIPS 2010]Lossless Compression based on the Sequence Memoizer.
J. Gasthaus and F. Wood and Y.W. Teh. DCC 2010.
[bibtex] [pdf] [djvu] [DCC 2010]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]Bayesian Rose Trees.
C. Blundell, Y.W. Teh and K.A. Heller. UAI 2010.
[bibtex] [pdf] [djvu] [UAI 2010]Long version: 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] [Workshop] [Wiley]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]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]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]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] [presen tation]Bayesian Agglomerative Clustering with Coalescents.
Y.W. Teh, H. Daume III and D.M. Roy. NIPS 2007.
[bibtex] [pdf] [djvu] [supplemental] [NIPS 2007]Collapsed Variational Inference for HDP.
Y.W. Teh, K. Kurihara and M. Welling. NIPS 2007.
[bibtex] [pdf] [djvu] [NIPS 2007]Stick-breaking Construction for the Indian Buffet Process.
Y.W. Teh, D. Gorur and Z. Ghahramani. AISTATS 2007.
[bibtex] [pdf] [djvu] [AISTATS 2007]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] [djvu] [ICML 2006]A Hierarchical Bayesian Language Model based on Pitman-Yor Processes.
Y.W. Teh. Coling/ACL 2006.
[bibtex] [pdf] [djvu] [Coling/ACL 2006]Long version: A Bayesian Interpretation of Interpolated Kneser-Ney.
Y.W. Teh. Technical Report TRA2/06, School of Computing, NUS, revised 2006.
[bibtex] [pdf] [djvu] [School of Computing, NUS]Semiparametric Latent Factor Models.
Y.W. Teh, M. Seeger and M.I. Jordan. AISTATS 2005.
[bibtex] [pdf] [djvu] [AISTATS 2005]Long version: Semiparametric Latent Factor Models.
M. Seeger, Y.W. Teh and M.I. Jordan. Technical Report, Computer Science, UC Berkeley, 2005.
[bibtex] [pdf] [djvu] [Computer Science, UC Berkeley]Hierarchical Dirichlet Processes.
Y.W. Teh, M.I. Jordan, M.J. Beal and D.M. Blei. JASA 101(476):1566-1581, 2006.
[bibtex] [pdf] [djvu] [JASA]Old version: 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] [djvu] [Statistics, UC Berkeley]Short version: 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] [djvu] [NIPS 2004]
Computational Statistics
Consistency and Fluctuations for Stochastic Gradient Langevin Dynamics.
Y. W. Teh, A. Thiery and S. Vollmer.
[bibtex] [arxiv]Asynchronous Anytime Sequential Monte Carlo.
B. Paige, F. Wood, A. Doucet and Y. W. Teh. NIPS 2014.
[bibtex] [arxiv] [NIPS 2014]Distributed Context-Aware Bayesian Posterior Sampling via Expectation Propagation.
M. Xu, Y. W. Teh, J. Zhu and B. Zhang. NIPS 2014.
[bibtex] [NIPS 2014]Fast MCMC Sampling for Markov Jump Processes and Extensions.
V. Rao and Y. W. Teh. JMLR 14:3207-3232, 2013.
[bibtex] [pdf]Stochastic Gradient Riemannian Langevin Dynamics on the Probability Simplex.
S. Patterson and Y. W. Teh. NIPS 2013.
[bibtex] [pdf] [code]Top-down Particle Filtering for Bayesian Decision Trees.
B. Lakshminarayanan, D. Roy, Y. W. Teh. ICML 2013.
[bibtex] [arxiv] [code]MCMC for Normalized Random Measure Mixture Models.
S. Favaro and Y. W. Teh. Statistical Science 28(3):335-359, 2013.
[bibtex] [pdf] [slides]Gaussian Process Modulated Renewal Processes.
V. Rao and Y. W. Teh. NIPS 2011.
[bibtex] [pdf] [supplementary] [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] [UAI 2011]Bayesian Learning via Stochastic Gradient Langevin Dynamics.
M. Welling and Y. W. Teh. ICML 2011.
[bibtex] [pdf] [djvu] [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]
Approximate Inference
On Smoothing and Inference for Topic Models.
A. Asuncion, M. Welling, P. Smyth and Y.W. Teh. UAI 2009.
[bibtex] [pdf] [UAI 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]Hybrid Variational/Gibbs Inference in Topic Models.
Max Welling, Y.W. Teh and B. Kappen UAI 2008.
[bibtex] [pdf] [djvu] [UAI 2008]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]Collapsed Variational Dirichlet Process Mixture Models.
K. Kurihara, M. Welling and Y.W. Teh. IJCAI 2007.
[bibtex] [pdf] [djvu] [IJCAI 2007]A Collapsed Variational Bayesian Inference Algorithm for Latent Dirichlet Allocation.
Y.W. Teh, D. Newman and M. Welling. NIPS 2006.
[bibtex] [pdf] [NIPS 2006]Structured Region Graphs: Morphing EP into GBP.
M. Welling, T. Minka and Y.W. Teh. UAI 2005. Extended version with proofs.
[bibtex] [pdf] [djvu] [UAI 2005]Approximate Inference by Markov Chains on Union Spaces.
M. Welling, M. Rosen-Zvi and Y.W. Teh. ICML 2004.
[bibtex] [pdf] [djvu] [ICML 2004]Linear Response Algorithms for Approximate Inference in Graphical Models.
M. Welling and Y.W. Teh. Neural Computation 16:197-221, 2004.
[bibtex] [pdf] [djvu] [Neural Computation]Short version: Linear Response Algorithms for Approximate Inference.
M. Welling and Y.W. Teh. NIPS 2003.
[bibtex] [pdf] [djvu] [NIPS 2003]On Improving the Efficiency of the Iterative Proportional Fitting Procedure.
Y.W. Teh and M. Welling. AISTATS 2003.
[bibtex] [pdf] [djvu] [AISTATS 2003]The Unified Propagation and Scaling Algorithm.
Y.W. Teh and M. Welling. NIPS 2001.
[bibtex] [pdf] [djvu] [NIPS 2001]Passing and Bouncing Messages for Generalized Inference.
Y.W. Teh and M. Welling. Technical Report 2001-001, Gatsby Unit, UCL.
[bibtex] [pdf] [djvu] [Gatsby Unit]Approximate Inference in Boltzmann Machines.
M. Welling and Y.W. Teh. Artificial Intelligence 143(1):19-50, 2003.
[bibtex] [pdf] [djvu] [Artificial Intelligence]Short version: Belief Optimization for Binary Networks: A Stable Alternative to Loopy Belief Propagation.
M. Welling and Y.W. Teh. UAI 2001.
[bibtex] [pdf] [djvu] [UAI 2001]Software
Easy BP - release 0.
Y.W. Teh. 2008. MATLAB and C code. Implements a MATLAB table class to make implementation of various message passing inference algorithms much simpler.
[readme] [tgz] [zip]
Representation Learning
A Fast and Simple Algorithm for Training Neural Probabilistic Language Models.
A. Mnih and Y. W. Teh. ICML 2012.
[bibtex] [pdf] [poster] [ICML 2012]Learning Label Trees for Probabilistic Modelling of Implicit Feedback.
A. Mnih and Y. W. Teh. NIPS 2012.
[bibtex] [pdf] [NIPS 2012]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] [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] [djvu] [Cognitive Science]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] [djvu] [Journal of Machine Learning Research]Short version: A New View of ICA.
G.E. Hinton, M. Welling, Y.W. Teh and S. Osindero. ICA 2001.
[bibtex] [pdf] [djvu] [ICA 2001]Discovering Multiple Constraints that are Frequently Approximately Satisfied.
G.E. Hinton and Y.W. Teh. UAI 2001.
[bibtex] [pdf] [djvu] [UAI 2001]Rate-coded Restricted Boltzmann Machines for Face Recognition.
Y.W. Teh and G.E. Hinton. NIPS 2000.
[bibtex] [pdf] [djvu] [NIPS 2000]
Reinforcement Learning, Planning, Control
Searching for Objects Driven by Context.
B. Alexe, N. Heess, Y. W. Teh and V. Ferrari. NIPS 2012.
[bibtex] [pdf] [NIPS 2012]Actor-Critic Reinforcement Learning with Energy-Based Policies.
N. Heess D. Silver and Y. W. Teh. Journal of Machine Learning Research Conference and Workshop Proceedings (European Workshop on Reinforcement Learning) 2012.
[bibtex] [pdf] [EWRL 2012]
Machine Vision
Searching for Objects Driven by Context.
B. Alexe, N. Heess, Y. W. Teh and V. Ferrari. NIPS 2012.
[bibtex] [pdf] [NIPS 2012]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]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] [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]
Language Technologies
Learning Label Trees for Probabilistic Modelling of Implicit Feedback.
A. Mnih and Y. W. Teh. NIPS 2012.
[bibtex] [pdf] [NIPS 2012]A Fast and Simple Algorithm for Training Neural Probabilistic Language Models.
A. Mnih and Y. W. Teh. ICML 2012.
[bibtex] [pdf] [poster] [ICML 2012]Improvements to the Sequence Memoizer.
J. Gasthaus and Y.W. Teh. NIPS 2010.
[bibtex] [pdf] [supplemental] [NIPS 2010]Lossless Compression based on the Sequence Memoizer.
J. Gasthaus and F. Wood and Y.W. Teh. DCC 2010.
[bibtex] [pdf] [djvu] [DCC 2010]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]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]A Hierarchical Bayesian Language Model based on Pitman-Yor Processes.
Y.W. Teh. Coling/ACL 2006.
[bibtex] [pdf] [djvu] [Coling/ACL 2006]Long version: A Bayesian Interpretation of Interpolated Kneser-Ney.
Y.W. Teh. Technical Report TRA2/06, School of Computing, NUS, revised 2006.
[bibtex] [pdf] [djvu] [School of Computing, NUS]
Biology, Genetics
Scalable Imputation of Genetic Data with a Discrete Fragmentation-Coagulation Process.
L. T. Elliott and Y. W. Teh. NIPS 2012.
[bibtex] [pdf] [NIPS 2012]Modelling Genetic Variations using Fragmentation-Coagulation Processes.
Y. W. Teh, C. Blundell and L. T. Elliott. NIPS 2011.
[bibtex] [pdf] [video] [NIPS 2011]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]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] [djvu] [ICML 2006]
Neuroscience
Dependent Dirichlet Process Spike Sorting.
J. Gasthaus, F. Wood, D. Gorur and Y.W. Teh. NIPS 2008.
[bibtex] [pdf] [djvu] [NIPS 2008]
Miscellaneous
Learning with Invariances via Linear Functionals on Reproducing Kernel Hilbert Space.
X. Zhang and W. S. Lee and Y. W. Teh. NIPS 2013.
[bibtex] [pdf]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] [djvu] [School of Computing, NUS]Automatic Alignment of Local Representations.
Y.W. Teh and S. Roweis. NIPS 2002.
[bibtex] [pdf] [djvu] [NIPS 2002]Locally Linear Coordination - release 1.
Y.W. Teh and S. Roweis. 2002. MATLAB code. Includes MFA/MPPCA code.
[readme] [tgz]An Alternate Objective Function for Markovian Fields.
S. Kakade, Y.W. Teh and S. Roweis. ICML 2002.
[bibtex] [pdf] [djvu] [ICML 2002]Making Forward Chaining Relevant.
F. Bacchus and Y.W. Teh. AIPS 1998.
[bibtex] [pdf] [djvu] [AIPS 1998]
Theses
Bethe Free Energy and Contrastive Divergence Approximations for Undirected Graphical Models.
Y.W. Teh. Ph.D. Thesis, 2003. University of Toronto.
[bibtex] [pdf] [djvu] [Computer Science, Toronto]Learning to Parse Images.
Y.W. Teh, 2000. Master's thesis, University of Toronto.
[bibtex] [pdf] [djvu] [Computer Science, Toronto]Short version: Learning to Parse Images.
G.E. Hinton, Z. Ghahramani and Y.W. Teh. NIPS 1999.
[bibtex] [pdf] [djvu] [NIPS 1999]
Course Projects
Incremental conservative visibility with general occluders.
Y.W. Teh and H. Zhang. CSC2522F Project, 1999.
[pdf] [djvu]Wagner's conjecture.
Y.W. Teh, CSC2410S Project, 1999.
[pdf] [djvu]An attention model and steerable filters.
Y.W. Teh. CSC2523S Project, 1999.
[pdf] [djvu]Representing coastlines with linear transforms.
Y.W. Teh. CSC2508S Project, 2000.
[pdf] [djvu]