Yee Whye Teh

According to our database1, Yee Whye Teh authored at least 168 papers between 1998 and 2020.

Collaborative distances:

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
Other 

Links

Homepages:

On csauthors.net:

Bibliography

2020
On the robustness of effectiveness estimation of nonpharmaceutical interventions against COVID-19 transmission.
CoRR, 2020

Lottery Tickets in Linear Models: An Analysis of Iterative Magnitude Pruning.
CoRR, 2020

Bayesian Deep Ensembles via the Neural Tangent Kernel.
CoRR, 2020

Neural Ensemble Search for Performant and Calibrated Predictions.
CoRR, 2020

Simple and Scalable Epistemic Uncertainty Estimation Using a Single Deep Deterministic Neural Network.
CoRR, 2020

Pruning untrained neural networks: Principles and Analysis.
CoRR, 2020

Fractional Underdamped Langevin Dynamics: Retargeting SGD with Momentum under Heavy-Tailed Gradient Noise.
CoRR, 2020

Functional Regularisation for Continual Learning with Gaussian Processes.
Proceedings of the 8th International Conference on Learning Representations, 2020

Multiplicative Interactions and Where to Find Them.
Proceedings of the 8th International Conference on Learning Representations, 2020

A Unified Stochastic Gradient Approach to Designing Bayesian-Optimal Experiments.
Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

Non-exchangeable feature allocation models with sublinear growth of the feature sizes.
Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019
MetaFun: Meta-Learning with Iterative Functional Updates.
CoRR, 2019

Divide, Conquer, and Combine: a New Inference Strategy for Probabilistic Programs with Stochastic Support.
CoRR, 2019

Amortized Rejection Sampling in Universal Probabilistic Programming.
CoRR, 2019

Deep Amortized Clustering.
CoRR, 2019

Task Agnostic Continual Learning via Meta Learning.
CoRR, 2019

Detecting Out-of-Distribution Inputs to Deep Generative Models Using a Test for Typicality.
CoRR, 2019

Noise Contrastive Meta-Learning for Conditional Density Estimation using Kernel Mean Embeddings.
CoRR, 2019

Hijacking Malaria Simulators with Probabilistic Programming.
CoRR, 2019

Meta reinforcement learning as task inference.
CoRR, 2019

Meta-learning of Sequential Strategies.
CoRR, 2019

Meta-Learning surrogate models for sequential decision making.
CoRR, 2019

Exploiting Hierarchy for Learning and Transfer in KL-regularized RL.
CoRR, 2019

Variational Estimators for Bayesian Optimal Experimental Design.
CoRR, 2019

Functional Regularisation for Continual Learning using Gaussian Processes.
CoRR, 2019

Probabilistic symmetry and invariant neural networks.
CoRR, 2019

Hierarchical Representations with Poincaré Variational Auto-Encoders.
CoRR, 2019

Revisiting Reweighted Wake-Sleep for Models with Stochastic Control Flow.
Proceedings of the Thirty-Fifth Conference on Uncertainty in Artificial Intelligence, 2019

Continual Unsupervised Representation Learning.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Continuous Hierarchical Representations with Poincaré Variational Auto-Encoders.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Stacked Capsule Autoencoders.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Random Tessellation Forests.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Variational Bayesian Optimal Experimental Design.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Augmented Neural ODEs.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Hybrid Models with Deep and Invertible Features.
Proceedings of the 36th International Conference on Machine Learning, 2019

Disentangling Disentanglement in Variational Autoencoders.
Proceedings of the 36th International Conference on Machine Learning, 2019

Set Transformer: A Framework for Attention-based Permutation-Invariant Neural Networks.
Proceedings of the 36th International Conference on Machine Learning, 2019

A Statistical Approach to Assessing Neural Network Robustness.
Proceedings of the 7th International Conference on Learning Representations, 2019

Do Deep Generative Models Know What They Don't Know?
Proceedings of the 7th International Conference on Learning Representations, 2019

Neural Probabilistic Motor Primitives for Humanoid Control.
Proceedings of the 7th International Conference on Learning Representations, 2019

Attentive Neural Processes.
Proceedings of the 7th International Conference on Learning Representations, 2019

Information asymmetry in KL-regularized RL.
Proceedings of the 7th International Conference on Learning Representations, 2019

2018
Disentangling Disentanglement.
CoRR, 2018

On Exploration, Exploitation and Learning in Adaptive Importance Sampling.
CoRR, 2018

Set Transformer.
CoRR, 2018

Hamiltonian Descent Methods.
CoRR, 2018

Neural Processes.
CoRR, 2018

Controllable Semantic Image Inpainting.
CoRR, 2018

Revisiting Reweighted Wake-Sleep.
CoRR, 2018

Sampling and Inference for Beta Neutral-to-the-Left Models of Sparse Networks.
Proceedings of the Thirty-Fourth 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.
J. Mach. Learn. Res., 2017

Distributed Bayesian Learning with Stochastic Natural Gradient Expectation Propagation and the Posterior Server.
J. Mach. Learn. Res., 2017

Faithful Model Inversion Substantially Improves Auto-encoding Variational Inference.
CoRR, 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.
J. Mach. Learn. Res., 2016

Consistency and Fluctuations For Stochastic Gradient Langevin Dynamics.
J. Mach. Learn. Res., 2016

Tucker Gaussian Process for Regression and Collaborative Filtering.
CoRR, 2016

Image Retrieval with a Bayesian Model of Relevance Feedback.
CoRR, 2016

The Mondrian Kernel.
Proceedings of the Thirty-Second 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

DR-ABC: Approximate Bayesian Computation with Kernel-Based 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 Large-Scale Regression when Uncertainty Matters.
Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, 2016

2015
On a class of σ-stable Poisson-Kingman models and an effective marginalized sampler.
Stat. Comput., 2015

Guest Editors' Introduction to the Special Issue on Bayesian Nonparametrics.
IEEE Trans. Pattern Anal. Mach. Intell., 2015

Bayesian nonparametric crowdsourcing.
J. Mach. Learn. Res., 2015

The Mondrian Process for Machine Learning.
CoRR, 2015

A hybrid sampler for Poisson-Kingman 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.
J. Mach. Learn. Res., 2013

Inferring ground truth from multi-annotator ordinal data: a probabilistic approach
CoRR, 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 5-8, 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 5-8, 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 5-8, 2013

Top-down 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
Bayesian nonparametric Plackett-Luce models for the analysis of clustered ranked data
CoRR, 2012

MCMC for continuous-time discrete-state 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 3-6, 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 3-6, 2012

Scalable imputation of genetic data with a discrete fragmentation-coagulation 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 3-6, 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 3-6, 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 3-6, 2012

A fast and simple algorithm for training neural probabilistic language models.
Proceedings of the 29th International Conference on Machine Learning, 2012

Actor-Critic Reinforcement Learning with Energy-Based 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

Learning Item Trees for Probabilistic Modelling of Implicit Feedback
CoRR, 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 Fragmentation-Coagulation 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 12-14 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 12-14 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 6-9 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 Power-law 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 7-10 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 7-10 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 non-homogeneous 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
Stick-breaking Construction for the Indian Buffet Process.
Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics, 2007

NUS-ML: 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 EMNLP-CoNLL 2007, 2007

2006
A Fast Learning Algorithm for Deep Belief Nets.
Neural Computation, 2006

Unsupervised Discovery of Nonlinear Structure Using Contrastive Backpropagation.
Cogn. Sci., 2006

A Collapsed Variational Bayesian Inference Algorithm for Latent Dirichlet Allocation.
Proceedings of the Advances in Neural Information Processing Systems 19, 2006

Bayesian multi-population haplotype inference via a hierarchical dirichlet process mixture.
Proceedings of the Machine Learning, 2006

A Hierarchical Bayesian Language Model Based On Pitman-Yor 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 CD-ROM, 27 June, 2004

2003
Energy-Based Models for Sparse Overcomplete Representations.
J. Mach. Learn. Res., 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
Rate-coded 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


  Loading...