Y. Teh

Yee Whye Teh, David A. Forsyth, Ryan White, 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..

Bethe free energy and contrastive divergence approximations for undirected graphical models

Yee Whye Teh, Geoffrey E. Hinton, Y. Teh, 2003 .

GCNU TR 2001 – 001 Passing And Bouncing Messages For Generalized Inference

Y. Teh, M. Welling, 2001 .

Stick-breaking Construction for the Indian Buffet Process

Yee Whye Teh, Dilan Görür, Zoubin Ghahramani, 2007, AISTATS.

The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables pdf

Yee Whye Teh, Andriy Mnih, Chris J. Maddison, 2016, ICLR.

Variational Inference for the Indian Buffet Process

Yee Whye Teh, Finale Doshi-Velez, Kurt Miller, 2009, AISTATS.

Spatial Normalized Gamma Processes

Yee Whye Teh, Vinayak A. Rao, Y. Teh, 2009, NIPS.

Hamiltonian Descent Methods pdf

Yee Whye Teh, Arnaud Doucet, Brendan O'Donoghue, 2018, ArXiv.

Dependent Dirichlet Process Spike Sorting

Yee Whye Teh, Dilan Görür, Frank D. Wood, 2008, NIPS.

Random Tessellation Forests

Yee Whye Teh, Lloyd T. Elliott, Shijia Wang, 2019, NeurIPS.

Indian Buffet Processes with Power-law Behavior

Y. Teh, Dilan Görür, 2009, NIPS.

A Hierarchical Bayesian Language Model Based On Pitman-Yor Processes

Yee Whye Teh, Y. Teh, 2006, ACL.

Bayesian nonparametric Plackett-Luce models for the analysis of preferences for college degree programmes

Yee Whye Teh, Thomas Brendan Murphy, Franccois Caron, 2015 .

MCMC for Normalized Random Measure Mixture Models

Y. Teh, S. Favaro, 2013, 1310.0595.

Augmented Neural ODEs

Yee Whye Teh, Arnaud Doucet, Emilien Dupont, 2019, NeurIPS.

Improving Word Sense Disambiguation Using Topic Features

Yee Whye Teh, Wee Sun Lee, Junfu Cai, 2007, EMNLP.

Fractional Underdamped Langevin Dynamics: Retargeting SGD with Momentum under Heavy-Tailed Gradient Noise

Yee Whye Teh, Umut Simsekli, Mert Gürbüzbalaban, 2020, ICML.

Dependent Normalized Random Measures

Yee Whye Teh, Vinayak A. Rao, Wray L. Buntine, 2013, ICML.

How Robust are the Estimated Effects of Nonpharmaceutical Interventions against COVID-19?

Yee Whye Teh, Yarin Gal, Leonid Chindelevitch, 2020, NeurIPS.

The effectiveness and perceived burden of nonpharmaceutical interventions against COVID-19 transmission: a modelling study with 41 countries

Y. Teh, Y. Gal, J. Salvatier, 2020, medRxiv.

On the robustness of effectiveness estimation of nonpharmaceutical interventions against COVID-19 transmission pdf

Yee Whye Teh, Yarin Gal, Leonid Chindelevitch, 2020, ArXiv.

Bayesian multi-population haplotype inference via a hierarchical dirichlet process mixture

Yee Whye Teh, Michael I. Jordan, Eric P. Xing, 2006, ICML.

Noise Contrastive Meta-Learning for Conditional Density Estimation using Kernel Mean Embeddings pdf

Yee Whye Teh, Jean-Francois Ton, Dino Sejdinovic, 2019, AISTATS.

AI for social good: unlocking the opportunity for positive impact

Yee Whye Teh, Mohammad Emtiyaz Khan, Frank Hutter, 2020, Nature Communications.

Bayesian Inference on Population Structure: From Parametric to Nonparametric Modeling

Yee Whye Teh, Maria De Iorio, Stefano Favaro, 2015 .

Modeling Population Structure Under Hierarchical Dirichlet Processes

Y. Teh, S. Favaro, L. Elliott, 2015, Bayesian Analysis.

Lottery Tickets in Linear Models: An Analysis of Iterative Magnitude Pruning

Y. Teh, Varun Kanade, Bryn Elesedy, 2020, ArXiv.

Bayesian Deep Ensembles via the Neural Tangent Kernel pdf

Yee Whye Teh, Bobby He, Balaji Lakshminarayanan, 2020, NeurIPS.

An Efficient Sequential Monte Carlo Algorithm for Coalescent Clustering

Yee Whye Teh, Dilan Görür, Y. Teh, 2008, NIPS.

Lossless Compression Based on the Sequence Memoizer

Yee Whye Teh, Frank D. Wood, Jan Gasthaus, 2010, 2010 Data Compression Conference.

Stochastic Gradient Riemannian Langevin Dynamics on the Probability Simplex

Yee Whye Teh, Sam Patterson, Y. Teh, 2013, NIPS.

Distributed Bayesian Learning with Stochastic Natural Gradient Expectation Propagation and the Posterior Server pdf

Yee Whye Teh, Thibaut Lienart, Charles Blundell, 2015, J. Mach. Learn. Res..

Learning to Parse Images

Yee Whye Teh, Geoffrey E. Hinton, Zoubin Ghahramani, 1999, NIPS.

A stochastic memoizer for sequence data

Yee Whye Teh, Frank D. Wood, Cédric Archambeau, 2009, ICML '09.

Mondrian Forests for Large-Scale Regression when Uncertainty Matters pdf

Yee Whye Teh, Daniel M. Roy, Balaji Lakshminarayanan, 2015, AISTATS.

On Improving the Efficiency of the Iterative Proportional Fitting Procedure

Yee Whye Teh, Max Welling, Y. Teh, 2003, AISTATS.

Automatic Alignment of Local Representations

Yee Whye Teh, Sam T. Roweis, S. Roweis, 2002, NIPS.

A New View of ICA

Yee Whye Teh, Geoffrey E. Hinton, Max Welling, 2001 .

Attentive Neural Processes pdf

Yee Whye Teh, Oriol Vinyals, Jonathan Schwarz, 2019, ICLR.

Consistency and Fluctuations For Stochastic Gradient Langevin Dynamics

Yee Whye Teh, Alexandre H. Thiery, Sebastian Vollmer, 2014, J. Mach. Learn. Res..

Collapsed Variational Inference for HDP

Yee Whye Teh, Max Welling, Kenichi Kurihara, 2007, NIPS.

Statistical Verification of Neural Networks

Y. Teh, M. P. Kumar, Tom Rainforth, 2018 .

Bootstrapping Neural Processes pdf

Yee Whye Teh, Eunho Yang, Yoonho Lee, 2020, NeurIPS.

Making Forward Chaining Relevant

Yee Whye Teh, Fahiem Bacchus, Y. Teh, 1998, AIPS.

Conditional Neural Processes

Yee Whye Teh, Murray Shanahan, David Saxton, 2018, ICML.

Beam sampling for the infinite hidden Markov model

Yee Whye Teh, Zoubin Ghahramani, Yunus Saatci, 2008, ICML '08.

Variational Bayesian Approach to Movie Rating Prediction

Yee Whye Teh, Yew Jin Lim, Y. Teh, 2007, KDD 2007.

Sharing Clusters among Related Groups: Hierarchical Dirichlet Processes

Yee Whye Teh, Michael I. Jordan, David M. Blei, 2004, NIPS.

Continuous Hierarchical Representations with Poincaré Variational Auto-Encoders

Yee Whye Teh, Ryota Tomioka, Emile Mathieu, 2019, NeurIPS.

Top-down particle filtering for Bayesian decision trees

Yee Whye Teh, Daniel M. Roy, Balaji Lakshminarayanan, 2013, ICML.

Faces and names in the news

Yee Whye Teh, Tim Miller, Alexander C. Berg, 2004, CVPR 2004.

A Collapsed Variational Bayesian Inference Algorithm for Latent Dirichlet Allocation

Yee Whye Teh, Max Welling, David Newman, 2006, NIPS.

Linear Response Algorithms for Approximate Inference in Graphical Models

Yee Whye Teh, Max Welling, Y. Teh, 2004, Neural Computation.

Collapsed Variational Dirichlet Process Mixture Models

Yee Whye Teh, Max Welling, Kenichi Kurihara, 2007, IJCAI.

Dual Space Preconditioning for Gradient Descent

Yee Whye Teh, Arnaud Doucet, Chris J. Maddison, 2019, SIAM J. Optim..

Belief Optimization for Binary Networks: A Stable Alternative to Loopy Belief Propagation pdf

Yee Whye Teh, Max Welling, Y. Teh, 2001, UAI.

Poisson Random Fields for Dynamic Feature Models

Yee Whye Teh, Valerio Perrone, Paul A. Jenkins, 2016, J. Mach. Learn. Res..

Making Latin Manuscripts Searchable using gHMMs

Yee Whye Teh, David A. Forsyth, Michael Maire, 2004, NIPS.

Linear Response for Approximate Inference

Yee Whye Teh, Max Welling, Y. Teh, 2003, NIPS.

Poisson intensity estimation with reproducing kernels

Yee Whye Teh, Dino Sejdinovic, Seth Flaxman, 2016, AISTATS.

Sampling and inference for discrete random probability measures in probabilistic programs

Yee Whye Teh, Tom Rainforth, Emile Mathieu, 2017 .

Scaled subordinators and generalizations of the Indian buffet process

Lancelot F. James, Y. Teh, Peter Orbanz, 2015, 1510.07309.

DR-ABC: Approximate Bayesian Computation with Kernel-Based Distribution Regression pdf

Yee Whye Teh, Dino Sejdinovic, Jovana Mitrovic, 2016, ICML.

Discovering Nonbinary Hierarchical Structures with Bayesian Rose Trees

Yee Whye Teh, Katherine A. Heller, Charles Blundell, 2011 .

On Smoothing and Inference for Topic Models

Yee Whye Teh, Max Welling, Padhraic Smyth, 2009, UAI.

Bayesian Inference with Big Data : A Snapshot from a Workshop

Jakub K. Kominiarczuk, Y. Teh, M. Welling, 2014 .

DeepC: Predicting chromatin interactions using megabase scaled deep neural networks and transfer learning

Yee Whye Teh, Jelena Telenius, Gerton Lunter, 2019, bioRxiv.

A Bayesian Interpretation of Interpolated Kneser-Ney

Yee Whye Teh, Y. Teh, 2006 .

Modelling sparsity, heterogeneity, reciprocity and community structure in temporal interaction data

Yee Whye Teh, François Caron, Xenia Miscouridou, 2018, NeurIPS.

Importance Weighted Policy Learning and Adaptation

N. Heess, Y. Teh, Leonard Hasenclever, 2020, 2009.04875.

Mix & Match – Agent Curricula for Reinforcement Learning [ Appendix ]

Siddhant M. Jayakumar, Wojciech M. Czarnecki, Max Jaderberg, 2018 .

A TTENTIVE N EURAL P ROCESSES

Oriol Vinyals, Y. Teh, A. Mnih, 2019 .

Uncertainty Quantification in End-to-End Implicit Neural Representations for Medical Imaging

Y. Teh, Bobby He, F. Vasconcelos, 2021 .

Simple and Scalable Epistemic Uncertainty Estimation Using a Single Deep Deterministic Neural Network

Joost R. van Amersfoort, Y. Teh, Y. Gal, 2020, ICML 2020.

Understanding the 2016 US Presidential Election using ecological inference and distribution regression with census microdata

Yee Whye Teh, Yu-Xiang Wang, Seth Flaxman, 2016, 1611.03787.

Inferring the effectiveness of government interventions against COVID-19

Y. Teh, Y. Gal, J. Salvatier, 2020, Science.

Multi-Armed Bandit for Species Discovery: A Bayesian Nonparametric Approach

Y. Teh, S. Favaro, M. Battiston, 2018 .

Proceedings of the Workshop on Prior Knowledge for Text and Language Processing

Y. Teh, Guillaume Bouchard, Hal Daumé, 2008 .

Hierarchical Dirichlet Processes Author ( s ) :

Michael I. Jordan, Matthew J. Beal, Y. Teh, 2011 .

On Statistical Thinking in Deep Learning

Y. Teh, A. Post, 2019 .

Equivariant Conditional Neural Processes

Y. Teh, M. Hutchinson, Peter Holderrieth, 2020, ArXiv.

Adaptive Reconfiguration Moves for MCMC inference in Dirichlet Process Mixtures

Mikkel N. Schmidt, Y. Teh, M. Mørup, 2014 .

Deep Kernel Machines via the Kernel Reparametrization Trick

Yee Whye Teh, Dino Sejdinovic, Jovana Mitrovic, 2017, ICLR.

Bayesian nonparametric inference for discovery probabilities: credible intervals and large sample asymptotics

Y. Teh, S. Favaro, J. Arbel, 2015, 1506.04915.

TR A 2 / 0 6 A Bayesian Interpretation of Interpolated Kneser

Y. Teh, 2004 .

On the Stick-Breaking Representation for Homogeneous NRMIs

Y. Teh, A. Lijoi, S. Favaro, 2016 .

Random variate generation for Laguerre-type exponentially tilted $\alpha$-stable distributions

Y. Teh, S. Favaro, Bernardo Nipoti, 2015 .

neural compression workshop COIN : CO MPRESSION WITH I MPLICIT N EURAL REPRESENTATIONS

Y. Teh, A. Doucet, Emilien Dupont, 2021 .

Hierarchical Dirichlet Processes

Michael I. Jordan, Matthew J. Beal, Y. Teh, 2006 .

On the stick-breaking representation of $\sigma$-stable Poisson-Kingman models

Yee Whye Teh, Stefano Favaro, Maria Lomeli, 2014 .

A Marginal Sampler for σ-Stable Poisson–Kingman Mixture Models

Yee Whye Teh, Stefano Favaro, Y. Teh, 2014, 1407.4211.

Unsupervised Object Discovery via Capsule Decoders

Yee Whye Teh, Geoffrey E. Hinton, Adam R. Kosiorek, 2019 .

Transferring Task Goals via Hierarchical Reinforcement Learning

Yee Whye Teh, Razvan Pascanu, Shaobo Hou, 2018 .

Bayesian Nonparametric Models

Yee Whye Teh, Peter Orbanz, Y. Teh, 2010, Encyclopedia of Machine Learning.

Simple and Scalable Epistemic Uncertainty Estimation Using a Single Deep Deterministic Neural Network pdf

Yee Whye Teh, Yarin Gal, Lewis Smith, 2020, ICML.

A characterization of product-form exchangeable feature probability functions

Daniel M. Roy, Y. Teh, S. Favaro, 2016, The Annals of Applied Probability.

epidemics/COVIDNPIs: Inferring the effectiveness of government interventions against COVID-19

Y. Teh, Y. Gal, J. Salvatier, 2020 .

Neural Ensemble Search for Uncertainty Estimation and Dataset Shift

Yee Whye Teh, Frank Hutter, Thomas Elsken, 2020, NeurIPS.

Names and faces in the news

Tamara L. Berg, A. Berg, Y. Teh, 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..

Meta-Learning Sparse Compression Networks

Y. Teh, Jonathan Schwarz, 2022, 2205.08957.

Challenges and Opportunities in Offline Reinforcement Learning from Visual Observations

Michael A. Osborne, Philip J. Ball, Tim G. J. Rudner, 2022, arXiv.org.

UncertaINR: Uncertainty Quantification of End-to-End Implicit Neural Representations for Computed Tomography

Y. Teh, Nalini M. Singh, Bobby He, 2022, ArXiv.

Learning Instance-Specific Data Augmentations

Y. Teh, Hyunjik Kim, Tom Rainforth, 2022, ArXiv.

Pre-training via Denoising for Molecular Property Prediction

James Martens, Y. Teh, P. Battaglia, 2022, ArXiv.

Riemannian Score-Based Generative Modeling

Valentin De Bortoli, Y. Teh, A. Doucet, 2022, ArXiv.

Group Equivariant Subsampling pdf

Yee Whye Teh, Tom Rainforth, Hyunjik Kim, 2021, NeurIPS.

Hierarchical Dririchlet tree for information retrieval

Yee Whye Teh, Gholamreza Haffari, Y. Teh, 2009 .

Dirichlet Process

Yee Whye Teh, Y. Teh, 2010, Encyclopedia of Machine Learning.

Vector-valued Gaussian Processes on Riemannian Manifolds via Gauge Independent Projected Kernels

Yee Whye Teh, Marc Peter Deisenroth, Viacheslav Borovitskiy, 2021, NeurIPS.

LieTransformer: Equivariant self-attention for Lie Groups

Yee Whye Teh, Charline Le Lan, Sheheryar Zaidi, 2020, ICML.

An Exact Auxiliary Variable Gibbs Sampler for a Class of Diffusions

Yee Whye Teh, Vinayak A. Rao, Qi Wang, 2019, J. Comput. Graph. Stat..

Interoperability of statistical models in pandemic preparedness: principles and reality.

Yee Whye Teh, Peter J. Diggle, Tor Erlend Fjelde, 2021, Statistical science : a review journal of the Institute of Mathematical Statistics.

Equivariant Learning of Stochastic Fields: Gaussian Processes and Steerable Conditional Neural Processes

Yee Whye Teh, Peter Holderrieth, Michael Hutchinson, 2020, ICML.

Vector-valued Gaussian Processes on Riemannian Manifolds via Gauge Equivariant Projected Kernels pdf

Yee Whye Teh, Marc Peter Deisenroth, Viacheslav Borovitskiy, 2021, ArXiv.

A nonparametric HMM for genetic imputation and coalescent inference

Yee Whye Teh, Lloyd T. Elliott, Y. Teh, 2016, 1611.00544.

Infinite Hidden Markov Models via the Hierarchical Dirichlet Process

Michael I. Jordan, Matthew J. Beal, Y. Teh, 2005 .

for Normalized Random Measure Mixture Models

Y. Teh, S. Favaro, 2013 .

Challenges and Opportunities in Offline Reinforcement Learning from Visual Observations

Philip J. Ball, Tim G. J. Rudner, Y. Teh, 2022, ArXiv.

Continuous Hierarchical Representations with Poincaré Variational Auto-Encoders

Charline Le Lan, Chris J. Maddison, Y. Teh, 2019, NeurIPS.

Bayesian Nonparametric Modeling and the Ubiquitous Ewens Sampling Formula

Y. Teh, 2016 .

Conformal Off-Policy Prediction in Contextual Bandits

Y. Teh, A. Doucet, Jean-Francois Ton, 2022, ArXiv.

Dec . 7 , 2018 Critiquing and Correcting Trends in Machine Learning

Matt J. Kusner, Y. Teh, R. Caruana, 2022 .

Riemannian Diffusion Schr\"odinger Bridge

Valentin De Bortoli, Y. Teh, A. Doucet, 2022, 2207.03024.

When Does Re-initialization Work?

Y. Teh, Razvan Pascanu, C. Clopath, 2022, ArXiv.

Posterior Consistency for a Non-parametric Survival Model under a Gaussian Process Prior

Y. Teh, Tamara Fern'andez, 2016, 1611.02335.

Instance-Specific Augmentation: Capturing Local Invariances

Y. Teh, Tom Rainforth, Emile Mathieu, 2022, 2206.00051.

Conformal Off-Policy Prediction in Contextual Bandits

Y. Teh, A. Doucet, Jean-Francois Ton, 2022, NeurIPS.

Pre-training via Denoising for Molecular Property Prediction

James Martens, Y. Teh, P. Battaglia, 2022, ICLR.

Continual Learning via Sequential Function-Space Variational Inference

Tim G. J. Rudner, Y. Teh, Y. Gal, 2022, ICML.

COIN++: Neural Compression Across Modalities

Y. Teh, A. Doucet, Emilien Dupont, 2022, 2201.12904.

NEVIS'22: A Stream of 100 Tasks Sampled from 30 Years of Computer Vision Research

Diego de Las Casas, Andrei A. Rusu, Marc'Aurelio Ranzato, 2022, ArXiv.

Riemannian Score-Based Generative Modelling

Valentin De Bortoli, Y. Teh, A. Doucet, 2022, NeurIPS.

Meta-Learning Sparse Compression Networks

Y. Teh, Jonathan Schwarz, 2022, Trans. Mach. Learn. Res..

Concave-Convex Adaptive Rejection Sampling

Y. Teh, Dilan Görür, 2011 .

On a class of σ -stable Poisson–Kingman models and an effective marginalized sampler

Y. Teh, S. Favaro, M. Lomeli, 2015 .

Probabilistic symmetry and invariant neural networks

Yee Whye Teh, Benjamin Bloem-Reddy, Y. Teh, 2019, J. Mach. Learn. Res..

The effectiveness of eight nonpharmaceutical interventions against COVID-19 in 41 countries

Y. Teh, Y. Gal, J. Salvatier, 2020 .

Mix&Match - Agent Curricula for Reinforcement Learning pdf

Yee Whye Teh, Razvan Pascanu, Max Jaderberg, 2018, ICML.

MetaFun: Meta-Learning with Iterative Functional Updates

Adam R. Kosiorek, Y. Teh, Hyunjik Kim, 2019, ICML.

Functional Regularisation for Continual Learning

Yee Whye Teh, Razvan Pascanu, Jonathan Schwarz, 2019 .

Particle Gibbs for Bayesian Additive Regression Trees pdf

Yee Whye Teh, Daniel M. Roy, Balaji Lakshminarayanan, 2015, AISTATS.

Efficient Bayesian inference of instantaneous reproduction numbers at fine spatial scales, with an application to mapping and nowcasting the Covid‐19 epidemic in British local authorities

Y. Teh, U. Paquet, Nenad Tomašev, 2022, Journal of the Royal Statistical Society. Series A, (Statistics in Society).

Technical Document 3: E↵ectiveness and Resource Requirements of Test, Trace and Isolate Strategies

Y. Teh, Bryn Elesedy, Bobby He, 2020 .

Semi-supervised Learning in Reproducing Kernel Hilbert Spaces Using Local Invariances

Yee Whye Teh, Wee Sun Lee, Xinhua Zhang, 2006 .

Sequential Attend, Infer, Repeat: Generative Modelling of Moving Objects

Yee Whye Teh, Ingmar Posner, Adam R. Kosiorek, 2018, NeurIPS.

Amortized Probabilistic Detection of Communities in Graphs

Y. Teh, L. Paninski, Juho Lee, 2020, 2010.15727.

DeepC: predicting 3D genome folding using megabase-scale transfer learning

Yee Whye Teh, Gerton Lunter, Jim R Hughes, 2020, Nature Methods.

Mondrian Forests: Efficient Online Random Forests

Yee Whye Teh, Daniel M. Roy, Balaji Lakshminarayanan, 2014, NIPS.

Deep Transformers without Shortcuts: Modifying Self-attention for Faithful Signal Propagation

James Martens, Y. Teh, Samuel L. Smith, 2023, ICLR.

On Contrastive Representations of Stochastic Processes

Y. Teh, Emile Mathieu, Adam Foster, 2021, NeurIPS.

BayesIMP: Uncertainty Quantification for Causal Data Fusion pdf

Yee Whye Teh, Dino Sejdinovic, Siu Lun Chau, 2021, NeurIPS.

Hijacking Malaria Simulators with Probabilistic Programming pdf

Yee Whye Teh, Philip H. S. Torr, Tom Rainforth, 2019, ArXiv.

Hierarchical Representations with Poincaré Variational Auto-Encoders pdf

Yee Whye Teh, Ryota Tomioka, Emile Mathieu, 2019, ArXiv.

Variational Bayesian Optimal Experimental Design

Yee Whye Teh, Noah D. Goodman, Tom Rainforth, 2019, NeurIPS.

Bayesian Nonparametric Models

Yee Whye Teh, Peter Orbanz, Y. Teh, 2010, Encyclopedia of Machine Learning.

Deep Amortized Clustering pdf

Yee Whye Teh, Yoonho Lee, Juho Lee, 2019, ArXiv.

A hybrid sampler for Poisson-Kingman mixture models

Yee Whye Teh, Stefano Favaro, Maria Lomeli, 2015, NIPS.

On a class of $$\sigma $$σ-stable Poisson–Kingman models and an effective marginalized sampler

Yee Whye Teh, Stefano Favaro, Maria Lomeli, 2015, Stat. Comput..

A Tractable Class of σ-stable Poisson-Kingman Processes and an Effective Marginalized Sampler

Y. Teh, S. Favaro, M. Lomeli, 2014 .

Disentangling Disentanglement in Variational Autoencoders

Yee Whye Teh, Tom Rainforth, Emile Mathieu, 2018, ICML.

A mixture model for the evolution of gene expression in non-homogeneous datasets

Yee Whye Teh, Gerald T. Quon, Michael Brudno, 2008, NIPS.

Bayesian nonparametric models for ranked data

Yee Whye Teh, François Caron, Y. Teh, 2012, NIPS.

Meta-Learning surrogate models for sequential decision making pdf

Yee Whye Teh, Pushmeet Kohli, Jonathan Schwarz, 2019, ArXiv.

Asynchronous Anytime Sequential Monte Carlo

Yee Whye Teh, Arnaud Doucet, Frank D. Wood, 2014, NIPS.

Nonexchangeable random partition models for microclustering

Yee Whye Teh, Franccois Caron, Giuseppe Di Benedetto, 2017, The Annals of Statistics.

Task Agnostic Continual Learning via Meta Learning pdf

Yee Whye Teh, Razvan Pascanu, Xu He, 2019, ArXiv.

On Exploration, Exploitation and Learning in Adaptive Importance Sampling pdf

Yee Whye Teh, Yuan Zhou, Xiaoyu Lu, 2018, ArXiv.

Detecting Out-of-Distribution Inputs to Deep Generative Models Using a Test for Typicality pdf

Yee Whye Teh, Eric Nalisnick, Eric T. Nalisnick, 2019, ArXiv.

Do Deep Generative Models Know What They Don't Know? pdf

Yee Whye Teh, Dilan Görür, Eric T. Nalisnick, 2018, ICLR.

Bayesian Nonparametrics Rough Notes

Y. Teh, Gatsby, 2012 .

Modelling Genetic Variations using Fragmentation-Coagulation Processes

Yee Whye Teh, Lloyd T. Elliott, Charles Blundell, 2011, NIPS.

An Introduction to Bayesian Nonparametric Modelling

Yee Whye Teh, Y. Teh, 2009 .

Dirichlet Processes: Tutorial and Practical Course

Yee Whye Teh, Y. Teh, 2007 .

Expectation Particle Belief Propagation

Yee Whye Teh, Arnaud Doucet, Thibaut Lienart, 2015, NIPS.

Behavior Priors for Efficient Reinforcement Learning pdf

Yee Whye Teh, Razvan Pascanu, Hyeonwoo Noh, 2020, J. Mach. Learn. Res..

Exploiting Hierarchy for Learning and Transfer in KL-regularized RL pdf

Yee Whye Teh, Razvan Pascanu, Hyeonwoo Noh, 2019, ArXiv.

COIN: COmpression with Implicit Neural representations pdf

Yee Whye Teh, Arnaud Doucet, Emilien Dupont, 2021, ICLR 2021.

Set Transformer

Yee Whye Teh, Seungjin Choi, Juho Lee, 2018, ICML.

Meta reinforcement learning as task inference pdf

Yee Whye Teh, Nicolas Heess, Alexandre Galashov, 2019, ArXiv.

Exploration of the (Non-)Asymptotic Bias and Variance of Stochastic Gradient Langevin Dynamics

Yee Whye Teh, Sebastian Vollmer, Konstantinos C. Zygalakis, 2016, J. Mach. Learn. Res..

Powerpropagation: A sparsity inducing weight reparameterisation pdf

Yee Whye Teh, Razvan Pascanu, Peter E. Latham, 2021, NeurIPS.

Revisiting Reweighted Wake-Sleep for Models with Stochastic Control Flow

Yee Whye Teh, Tuan Anh Le, Frank Wood, 2018, UAI.

Tighter Variational Bounds are Not Necessarily Better

Yee Whye Teh, Tuan Anh Le, Tom Rainforth, 2018, ICML.

Neural Processes pdf

Yee Whye Teh, Fabio Viola, Jonathan Schwarz, 2018, ArXiv.

Stacked Capsule Autoencoders

Yee Whye Teh, Geoffrey E. Hinton, Adam R. Kosiorek, 2019, NeurIPS.

Neural Ensemble Search for Uncertainty Estimation and Dataset Shift

Chris C. Holmes, F. Hutter, Y. Teh, 2020, NeurIPS.

Approximate inference in Boltzmann machines

Yee Whye Teh, Max Welling, Y. Teh, 2003, Artif. Intell..

MCMC for continuous-time discrete-state systems

Yee Whye Teh, Vinayak A. Rao, Y. Teh, 2012, NIPS.

A Statistical Approach to Assessing Neural Network Robustness pdf

Yee Whye Teh, Tom Rainforth, M. Pawan Kumar, 2018, ICLR.

Bayesian Rose Trees

Yee Whye Teh, Katherine A. Heller, Charles Blundell, 2010, UAI.

UncertaINR: Uncertainty Quantification of End-to-End Implicit Neural Representations for Computed Tomography

Y. Teh, Bobby He, Francisca Vasconcelos, 2022, Trans. Mach. Learn. Res..

Functional Regularisation for Continual Learning using Gaussian Processes pdf

Yee Whye Teh, Razvan Pascanu, Jonathan Schwarz, 2019, ICLR.

Continual Unsupervised Representation Learning

Yee Whye Teh, Razvan Pascanu, Raia Hadsell, 2019, NeurIPS.

Generative Models as Distributions of Functions

Y. Teh, A. Doucet, Emilien Dupont, 2021, AISTATS.

COIN++: Neural Compression Across Modalities

Y. Teh, A. Doucet, Emilien Dupont, 2022, Trans. Mach. Learn. Res..

Modality-Agnostic Variational Compression of Implicit Neural Representations

Jinwoo Shin, Y. Teh, Jonathan Schwarz, 2023, ICML.

COIN++: Data Agnostic Neural Compression

Y. Teh, A. Doucet, Emilien Dupont, 2022, ArXiv.

Multiplicative Interactions and Where to Find Them

Yee Whye Teh, Razvan Pascanu, Simon Osindero, 2020, ICLR.

NEVIS'22: A Stream of 100 Tasks Sampled from 30 Years of Computer Vision Research

Diego de Las Casas, Andrei A. Rusu, Marc'Aurelio Ranzato, 2022, ArXiv.

Relativistic Monte Carlo

Yee Whye Teh, Xiaoyu Lu, Leonard Hasenclever, 2016, AISTATS.

Set Transformer

Adam R. Kosiorek, Y. Teh, Seungjin Choi, 2018, ArXiv.

A Unified Stochastic Gradient Approach to Designing Bayesian-Optimal Experiments

Y. Teh, Tom Rainforth, Adam Foster, 2019, AISTATS.

Fast MCMC sampling for Markov jump processes and extensions

Yee Whye Teh, Vinayak A. Rao, Y. Teh, 2012, J. Mach. Learn. Res..

Incorporating Unlabelled Data into Bayesian Neural Networks

Y. Teh, Mrinank Sharma, Tom Rainforth, 2023, ArXiv.

On Incorporating Inductive Biases into VAEs

Y. Teh, Tom Rainforth, N. Siddharth, 2021, ICLR.

Inferring ground truth from multi-annotator ordinal data: a probabilistic approach pdf

Yee Whye Teh, Balaji Lakshminarayanan, Y. Teh, 2013, ArXiv.

Rediscovery of Good–Turing estimators via Bayesian nonparametrics

Yee Whye Teh, Stefano Favaro, Bernardo Nipoti, 2014, Biometrics.

Faithful Inversion of Generative Models for Effective Amortized Inference

Yee Whye Teh, Tom Rainforth, Frank Wood, 2017, NeurIPS.

An Analysis of Categorical Distributional Reinforcement Learning

Yee Whye Teh, Marc G. Bellemare, Rémi Munos, 2018, AISTATS.

Tucker Gaussian Process for Regression and Collaborative Filtering pdf

Yee Whye Teh, Xiaoyu Lu, Hyunjik Kim, 2016, ArXiv.

Detecting Out-of-Distribution Inputs to Deep Generative Models Using Typicality

Yee Whye Teh, Eric Nalisnick, Eric T. Nalisnick, 2019 .

Inference Trees: Adaptive Inference with Exploration

Yee Whye Teh, Hongseok Yang, Tom Rainforth, 2018, 1806.09550.

Filtering Variational Objectives

Yee Whye Teh, Arnaud Doucet, Mohammad Norouzi, 2017, NIPS.

Neural Ensemble Search for Performant and Calibrated Predictions pdf

Yee Whye Teh, Frank Hutter, Thomas Elsken, 2020, ArXiv.

Amortized Rejection Sampling in Universal Probabilistic Programming

Frank D. Wood, Philip H. S. Torr, Y. Teh, 2019, AISTATS.

Lottery Tickets in Linear Models: An Analysis of Iterative Magnitude Pruning

Y. Teh, Varun Kanade, Bryn Elesedy, 2020, ArXiv.

Robust Pruning at Initialization

Y. Teh, A. Doucet, Jean-Francois Ton, 2020, ICLR.

Infinite Hierarchical Hidden Markov Models

Yee Whye Teh, Dilan Görür, Katherine A. Heller, 2009, AISTATS.

Challenges and Opportunities in Offline Reinforcement Learning from Visual Observations

Philip J. Ball, Tim G. J. Rudner, Y. Teh, 2022, ArXiv.

A Bayesian Interpretation of Interpolated Kneser-Ney

Yee Whye Teh, Y. Teh, 2006 .

Attentive Clustering Processes pdf

Yee Whye Teh, Liam Paninski, Yoonho Lee, 2020, ArXiv.

Gaussian Processes for Survival Analysis

Yee Whye Teh, Nicolas Rivera, Tamara Fernandez, 2016, NIPS.

Bayesian Deep Ensembles via the Neural Tangent Kernel

Y. Teh, Balaji Lakshminarayanan, Bobby He, 2020, NeurIPS.

Mixed Cumulative Distribution Networks

Yee Whye Teh, Ricardo Bezerra de Andrade e Silva, Charles Blundell, 2010, AISTATS.

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

Yee Whye Teh, Hongseok Yang, Tom Rainforth, 2019, ICML.

P RE - TRAINING VIA D ENOISING FOR M OLECULAR P ROPERTY P REDICTION

James Martens, Y. Teh, P. Battaglia, 2023 .

Non-exchangeable feature allocation models with sublinear growth of the feature sizes pdf

Yee Whye Teh, Franccois Caron, Giuseppe Di Benedetto, 2020, AISTATS.

SelfCheck: Using LLMs to Zero-Shot Check Their Own Step-by-Step Reasoning

Y. Teh, Tom Rainforth, Ning Miao, 2023, 2308.00436.

Kalman Filter for Online Classification of Non-Stationary Data

Michalis K. Titsias, Y. Teh, Razvan Pascanu, 2023, ArXiv.

The Mondrian Kernel

Yee Whye Teh, Zoubin Ghahramani, Daniel M. Roy, 2016, UAI.

Tractable Function-Space Variational Inference in Bayesian Neural Networks

Tim G. J. Rudner, Y. Teh, Y. Gal, 2022, NeurIPS.

Pruning untrained neural networks: Principles and Analysis pdf

Yee Whye Teh, Arnaud Doucet, Jean-Francois Ton, 2020, ArXiv.

The sequence memoizer

Frank D. Wood, Cédric Archambeau, Frank Wood, 2011, Commun. ACM.

Drug Discovery under Covariate Shift with Domain-Informed Prior Distributions over Functions

Tim G. J. Rudner, Y. Teh, C. Deane, 2023, ICML.

A Hierarchical Nonparametric Bayesian Approach to Statistical Language Model Domain Adaptation

Yee Whye Teh, Frank D. Wood, Y. Teh, 2009, AISTATS.

Challenges and Opportunities in Offline Reinforcement Learning from Visual Observations

Michael A. Osborne, Philip J. Ball, Tim G. J. Rudner, 2022, ArXiv.

Scaling up the Automatic Statistician: Scalable Structure Discovery using Gaussian Processes

Yee Whye Teh, Hyunjik Kim, Y. Teh, 2017, AISTATS.

Revisiting Reweighted Wake-Sleep pdf

Yee Whye Teh, Tuan Anh Le, Frank Wood, 2018, ArXiv.

Sampling and Inference for Beta Neutral-to-the-Left Models of Sparse Networks pdf

Yee Whye Teh, Benjamin Bloem-Reddy, Adam Foster, 2018, UAI.

Deep Stochastic Processes via Functional Markov Transition Operators

Y. Teh, Emilien Dupont, Tom Rainforth, 2023, ArXiv.

Detecting Out-of-Distribution Inputs to Deep Generative Models Using Typicality

Eric T. Nalisnick, Y. Teh, Akihiro Matsukawa, 2019 .

Bayesian nonparametric Plackett-Luce models for the analysis of clustered ranked data pdf

Yee Whye Teh, Thomas Brendan Murphy, François Caron, 2012, ArXiv.

Variational Estimators for Bayesian Optimal Experimental Design pdf

Yee Whye Teh, Noah D. Goodman, Tom Rainforth, 2019, ArXiv.

Learning with Invariance via Linear Functionals on Reproducing Kernel Hilbert Space

Yee Whye Teh, Xinhua Zhang, Wee Sun Lee, 2013, NIPS.

Distributed Bayesian Posterior Sampling via Moment Sharing

Yee Whye Teh, Bo Zhang, Jun Zhu, 2014, NIPS.

Geometric Neural Diffusion Processes

Richard E. Turner, Valentin De Bortoli, Y. Teh, 2023, ArXiv.

Importance Weighted Policy Learning and Adaption pdf

Yee Whye Teh, Alexandre Galashov, Leonard Hasenclever, 2020, ArXiv.

Scalable imputation of genetic data with a discrete fragmentation-coagulation process

Yee Whye Teh, Lloyd T. Elliott, Y. Teh, 2012, NIPS.

When Does Re-initialization Work?

Y. Teh, Razvan Pascanu, C. Clopath, 2022, ICBINB.

Cooled and Relaxed Survey Propagation for MRFs

Yee Whye Teh, Hai Leong Chieu, Wee Sun Lee, 2007, NIPS.

Causal Inference via Kernel Deviance Measures

Yee Whye Teh, Dino Sejdinovic, Jovana Mitrovic, 2018, NeurIPS.

Riemannian Score-Based Generative Modeling

Valentin De Bortoli, Y. Teh, A. Doucet, 2022, ArXiv.

Bayesian multi-population haplotype inference via a hierarchical dirichlet process mixture

Michael I. Jordan, E. Xing, Y. Teh, 2006, ICML.

Fast MCMC sampling for Markov jump processes and continuous time Bayesian networks

Yee Whye Teh, Vinayak A. Rao, Y. Teh, 2011, UAI.

Statistica Sinica Preprint No : SS-2015-0250 R 2 Title Bayesian nonparametric inference for discovery probabilities : credible intervals and large sample asymptotics

Y. Teh, S. Favaro, Bernardo Nipoti, 2016 .

Sharing Clusters among Related Groups: Hierarchical Dirichlet Processes

Michael I. Jordan, Matthew J. Beal, Y. Teh, 2004, NIPS.

neural compression workshop COIN : CO MPRESSION WITH I MPLICIT N EURAL REPRESENTATIONS

Y. Teh, A. Doucet, Emilien Dupont, 2021 .

Bayesian nonparametric Plackett-Luce models for the analysis of preferences for college degree programmes

Y. Teh, T. B. Murphy, F. Caron, 2012, 1211.5037.

Constructing dependent random probability measures from completely random measures

Vinayak A. Rao, Wray L. Buntine, Y. Teh, 2013 .