Yee Whye Teh
According to our database^{1},
Yee Whye Teh
authored at least 112 papers
between 1998 and 2018.
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Bibliography
2018
Sampling and Inference for Beta NeutraltotheLeft Models of Sparse Networks.
Proceedings of the ThirtyFourth Conference on Uncertainty in Artificial Intelligence, 2018
Faithful Inversion of Generative Models for Effective Amortized Inference.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018
Causal Inference via Kernel Deviance Measures.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018
Modelling sparsity, heterogeneity, reciprocity and community structure in temporal interaction data.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018
Sequential Attend, Infer, Repeat: Generative Modelling of Moving Objects.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018
Stochastic Expectation Maximization with Variance Reduction.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018
On Big Data Learning for Small Data Problems.
Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2018
Progress & Compress: A scalable framework for continual learning.
Proceedings of the 35th International Conference on Machine Learning, 2018
Tighter Variational Bounds are Not Necessarily Better.
Proceedings of the 35th International Conference on Machine Learning, 2018
Conditional Neural Processes.
Proceedings of the 35th International Conference on Machine Learning, 2018
Mix & Match Agent Curricula for Reinforcement Learning.
Proceedings of the 35th International Conference on Machine Learning, 2018
An Analysis of Categorical Distributional Reinforcement Learning.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2018
Scaling up the Automatic Statistician: Scalable Structure Discovery using Gaussian Processes.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2018
2017
Dirichlet Process.
Proceedings of the Encyclopedia of Machine Learning and Data Mining, 2017
Bayesian Nonparametric Models.
Proceedings of the Encyclopedia of Machine Learning and Data Mining, 2017
Poisson Random Fields for Dynamic Feature Models.
Journal of Machine Learning Research, 2017
Distributed Bayesian Learning with Stochastic Natural Gradient Expectation Propagation and the Posterior Server.
Journal of Machine Learning Research, 2017
Distral: Robust multitask reinforcement learning.
Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017
Filtering Variational Objectives.
Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017
Deep Kernel Machines via the Kernel Reparametrization Trick.
Proceedings of the 5th International Conference on Learning Representations, 2017
The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables.
Proceedings of the 5th International Conference on Learning Representations, 2017
Particle Value Functions.
Proceedings of the 5th International Conference on Learning Representations, 2017
Relativistic Monte Carlo.
Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, 2017
Poisson intensity estimation with reproducing kernels.
Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, 2017
2016
Exploration of the (Non)Asymptotic Bias and Variance of Stochastic Gradient Langevin Dynamics.
Journal of Machine Learning Research, 2016
Consistency and Fluctuations For Stochastic Gradient Langevin Dynamics.
Journal of Machine Learning Research, 2016
The Mondrian Kernel.
Proceedings of the ThirtySecond Conference on Uncertainty in Artificial Intelligence, 2016
Gaussian Processes for Survival Analysis.
Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016
DRABC: Approximate Bayesian Computation with KernelBased Distribution Regression.
Proceedings of the 33nd International Conference on Machine Learning, 2016
Scalable Structure Discovery in Regression using Gaussian Processes.
Proceedings of the 2016 Workshop on Automatic Machine Learning, 2016
Mondrian Forests for LargeScale Regression when Uncertainty Matters.
Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, 2016
2015
On a class of σstable PoissonKingman models and an effective marginalized sampler.
Statistics and Computing, 2015
Guest Editors' Introduction to the Special Issue on Bayesian Nonparametrics.
IEEE Trans. Pattern Anal. Mach. Intell., 2015
Bayesian nonparametric crowdsourcing.
Journal of Machine Learning Research, 2015
A hybrid sampler for PoissonKingman mixture models.
Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015
Expectation Particle Belief Propagation.
Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015
Particle Gibbs for Bayesian Additive Regression Trees.
Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics, 2015
2014
Distributed Bayesian Posterior Sampling via Moment Sharing.
Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014
Asynchronous Anytime Sequential Monte Carlo.
Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014
Mondrian Forests: Efficient Online Random Forests.
Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014
2013
Fast MCMC sampling for Markov jump processes and extensions.
Journal of Machine Learning Research, 2013
Learning with Invariance via Linear Functionals on Reproducing Kernel Hilbert Space.
Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 58, 2013
Stochastic Gradient Riemannian Langevin Dynamics on the Probability Simplex.
Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 58, 2013
Bayesian Hierarchical Community Discovery.
Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 58, 2013
Topdown particle filtering for Bayesian decision trees.
Proceedings of the 30th International Conference on Machine Learning, 2013
Dependent Normalized Random Measures.
Proceedings of the 30th International Conference on Machine Learning, 2013
2012
MCMC for continuoustime discretestate systems.
Proceedings of the Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 36, 2012
Learning Label Trees for Probabilistic Modelling of Implicit Feedback.
Proceedings of the Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 36, 2012
Scalable imputation of genetic data with a discrete fragmentationcoagulation process.
Proceedings of the Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 36, 2012
Bayesian nonparametric models for ranked data.
Proceedings of the Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 36, 2012
Searching for objects driven by context.
Proceedings of the Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 36, 2012
A fast and simple algorithm for training neural probabilistic language models.
Proceedings of the 29th International Conference on Machine Learning, 2012
ActorCritic Reinforcement Learning with EnergyBased Policies.
Proceedings of the Tenth European Workshop on Reinforcement Learning, 2012
2011
Mixed Cumulative Distribution Networks.
Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, 2011
The sequence memoizer.
Commun. ACM, 2011
Fast MCMC sampling for Markov jump processes and continuous time Bayesian networks.
Proceedings of the UAI 2011, 2011
Modelling Genetic Variations using FragmentationCoagulation Processes.
Proceedings of the Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011. Proceedings of a meeting held 1214 December 2011, 2011
Gaussian process modulated renewal processes.
Proceedings of the Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011. Proceedings of a meeting held 1214 December 2011, 2011
Bayesian Learning via Stochastic Gradient Langevin Dynamics.
Proceedings of the 28th International Conference on Machine Learning, 2011
(Invited talk) Bayesian Tools for Natural Language Learning.
Proceedings of the Fifteenth Conference on Computational Natural Language Learning, 2011
2010
Dirichlet Process.
Proceedings of the Encyclopedia of Machine Learning, 2010
Bayesian Nonparametric Models.
Proceedings of the Encyclopedia of Machine Learning, 2010
Preface.
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, 2010
Bayesian Rose Trees.
Proceedings of the UAI 2010, 2010
Improvements to the Sequence Memoizer.
Proceedings of the Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010. Proceedings of a meeting held 69 December 2010, 2010
Lossless Compression Based on the Sequence Memoizer.
Proceedings of the 2010 Data Compression Conference (DCC 2010), 2010
2009
A Hierarchical Nonparametric Bayesian Approach to Statistical Language Model Domain Adaptation.
Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics, 2009
Infinite Hierarchical Hidden Markov Models.
Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics, 2009
Variational Inference for the Indian Buffet Process.
Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics, 2009
On Smoothing and Inference for Topic Models.
Proceedings of the UAI 2009, 2009
Indian Buffet Processes with Powerlaw Behavior.
Proceedings of the Advances in Neural Information Processing Systems 22: 23rd Annual Conference on Neural Information Processing Systems 2009. Proceedings of a meeting held 710 December 2009, 2009
Spatial Normalized Gamma Processes.
Proceedings of the Advances in Neural Information Processing Systems 22: 23rd Annual Conference on Neural Information Processing Systems 2009. Proceedings of a meeting held 710 December 2009, 2009
Hierarchical Dirichlet Trees for Information Retrieval.
Proceedings of the Human Language Technologies: Conference of the North American Chapter of the Association of Computational Linguistics, Proceedings, May 31, 2009
A stochastic memoizer for sequence data.
Proceedings of the 26th Annual International Conference on Machine Learning, 2009
2008
Hybrid Variational/Gibbs Collapsed Inference in Topic Models.
Proceedings of the UAI 2008, 2008
The Mondrian Process.
Proceedings of the Advances in Neural Information Processing Systems 21, 2008
A mixture model for the evolution of gene expression in nonhomogeneous datasets.
Proceedings of the Advances in Neural Information Processing Systems 21, 2008
An Efficient Sequential Monte Carlo Algorithm for Coalescent Clustering.
Proceedings of the Advances in Neural Information Processing Systems 21, 2008
Dependent Dirichlet Process Spike Sorting.
Proceedings of the Advances in Neural Information Processing Systems 21, 2008
The Infinite Factorial Hidden Markov Model.
Proceedings of the Advances in Neural Information Processing Systems 21, 2008
Beam sampling for the infinite hidden Markov model.
Proceedings of the Machine Learning, 2008
2007
Stickbreaking Construction for the Indian Buffet Process.
Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics, 2007
NUSML: Improving Word Sense Disambiguation Using Topic Features.
Proceedings of the 4th International Workshop on Semantic Evaluations, 2007
Collapsed Variational Inference for HDP.
Proceedings of the Advances in Neural Information Processing Systems 20, 2007
Bayesian Agglomerative Clustering with Coalescents.
Proceedings of the Advances in Neural Information Processing Systems 20, 2007
Cooled and Relaxed Survey Propagation for MRFs.
Proceedings of the Advances in Neural Information Processing Systems 20, 2007
Collapsed Variational Dirichlet Process Mixture Models.
Proceedings of the IJCAI 2007, 2007
Improving Word Sense Disambiguation Using Topic Features.
Proceedings of the EMNLPCoNLL 2007, 2007
2006
A Fast Learning Algorithm for Deep Belief Nets.
Neural Computation, 2006
Unsupervised Discovery of Nonlinear Structure Using Contrastive Backpropagation.
Cognitive Science, 2006
A Collapsed Variational Bayesian Inference Algorithm for Latent Dirichlet Allocation.
Proceedings of the Advances in Neural Information Processing Systems 19, 2006
Bayesian multipopulation haplotype inference via a hierarchical dirichlet process mixture.
Proceedings of the Machine Learning, 2006
A Hierarchical Bayesian Language Model Based On PitmanYor Processes.
Proceedings of the ACL 2006, 2006
2005
Structured Region Graphs: Morphing EP into GBP.
Proceedings of the UAI '05, 2005
Semiparametric latent factor models.
Proceedings of the Tenth International Workshop on Artificial Intelligence and Statistics, 2005
2004
Linear Response Algorithms for Approximate Inference in Graphical Models.
Neural Computation, 2004
Sharing Clusters among Related Groups: Hierarchical Dirichlet Processes.
Proceedings of the Advances in Neural Information Processing Systems 17 [Neural Information Processing Systems, 2004
Making Latin Manuscripts Searchable using gHMMs.
Proceedings of the Advances in Neural Information Processing Systems 17 [Neural Information Processing Systems, 2004
Approximate inference by Markov chains on union spaces.
Proceedings of the Machine Learning, 2004
Names and Faces in the News.
Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2004), with CDROM, 27 June, 2004
2003
EnergyBased Models for Sparse Overcomplete Representations.
Journal of Machine Learning Research, 2003
Approximate inference in Boltzmann machines.
Artif. Intell., 2003
Linear Response for Approximate Inference.
Proceedings of the Advances in Neural Information Processing Systems 16 [Neural Information Processing Systems, 2003
On Improving the Efficiency of the Iterative Proportional Fitting Procedure.
Proceedings of the Ninth International Workshop on Artificial Intelligence and Statistics, 2003
2002
Automatic Alignment of Local Representations.
Proceedings of the Advances in Neural Information Processing Systems 15 [Neural Information Processing Systems, 2002
An Alternate Objective Function for Markovian Fields.
Proceedings of the Machine Learning, 2002
2001
Belief Optimization for Binary Networks: A Stable Alternative to Loopy Belief Propagation.
Proceedings of the UAI '01: Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence, 2001
Discovering Multiple Constraints that are Frequently Approximately Satisfied.
Proceedings of the UAI '01: Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence, 2001
The Unified Propagation and Scaling Algorithm.
Proceedings of the Advances in Neural Information Processing Systems 14 [Neural Information Processing Systems: Natural and Synthetic, 2001
2000
Ratecoded Restricted Boltzmann Machines for Face Recognition.
Proceedings of the Advances in Neural Information Processing Systems 13, 2000
1999
Learning to Parse Images.
Proceedings of the Advances in Neural Information Processing Systems 12, [NIPS Conference, Denver, Colorado, USA, November 29, 1999
1998
Making Forward Chaining Relevant.
Proceedings of the Fourth International Conference on Artificial Intelligence Planning Systems, 1998