Teh, Yee Whye

143 publications

ICLR 2025 L3Ms — Lagrange Large Language Models Guneet S. Dhillon, Xingjian Shi, Yee Whye Teh, Alex Smola

ICLR 2025 Learning to Contextualize Web Pages for Enhanced Decision Making by LLM Agents Dongjun Lee, Juyong Lee, Kyuyoung Kim, Jihoon Tack, Jinwoo Shin, Yee Whye Teh, Kimin Lee

NeurIPS 2025 Meta-Learning Objectives for Preference Optimization Carlo Alfano, Silvia Sapora, Jakob Nicolaus Foerster, Patrick Rebeschini, Yee Whye Teh

NeurIPS 2025 Rao-Blackwellised Reparameterisation Gradients Kevin H. Lam, Thang D Bui, George Deligiannidis, Yee Whye Teh

ICLRW 2025 StochasTok: Improving Fine-Grained Subword Understanding in LLMs Anya Sims, Cong Lu, Klara Kaleb, Jakob Nicolaus Foerster, Yee Whye Teh

ICLR 2025 SymDiff: Equivariant Diffusion via Stochastic Symmetrisation Leo Zhang, Kianoosh Ashouritaklimi, Yee Whye Teh, Rob Cornish

ICMLW 2024 Amortized Probabilistic Detection of Communities in Graphs Yueqi Wang, Yoonho Lee, Pallab Basu, Juho Lee, Yee Whye Teh, Liam Paninski, Ari Pakman

ICML 2024 Context-Guided Diffusion for Out-of-Distribution Molecular and Protein Design Leo Klarner, Tim G. J. Rudner, Garrett M Morris, Charlotte Deane, Yee Whye Teh

ICML 2024 EvIL: Evolution Strategies for Generalisable Imitation Learning Silvia Sapora, Gokul Swamy, Chris Lu, Yee Whye Teh, Jakob Nicolaus Foerster

TMLR 2024 Incorporating Unlabelled Data into Bayesian Neural Networks Mrinank Sharma, Tom Rainforth, Yee Whye Teh, Vincent Fortuin

ICLR 2024 Kalman Filter for Online Classification of Non-Stationary Data Michalis Titsias, Alexandre Galashov, Amal Rannen-Triki, Razvan Pascanu, Yee Whye Teh, Jorg Bornschein

NeurIPS 2024 Non-Stationary Learning of Neural Networks with Automatic Soft Parameter Reset Alexandre Galashov, Michalis K. Titsias, András György, Clare Lyle, Razvan Pascanu, Yee Whye Teh, Maneesh Sahani

NeurIPS 2024 Online Adaptation of Language Models with a Memory of Amortized Contexts Jihoon Tack, Jaehyung Kim, Eric Mitchell, Jinwoo Shin, Yee Whye Teh, Jonathan Richard Schwarz

ICML 2024 Position: Bayesian Deep Learning Is Needed in the Age of Large-Scale AI Theodore Papamarkou, Maria Skoularidou, Konstantina Palla, Laurence Aitchison, Julyan Arbel, David Dunson, Maurizio Filippone, Vincent Fortuin, Philipp Hennig, José Miguel Hernández-Lobato, Aliaksandr Hubin, Alexander Immer, Theofanis Karaletsos, Mohammad Emtiyaz Khan, Agustinus Kristiadi, Yingzhen Li, Stephan Mandt, Christopher Nemeth, Michael A Osborne, Tim G. J. Rudner, David Rügamer, Yee Whye Teh, Max Welling, Andrew Gordon Wilson, Ruqi Zhang

ICLR 2024 SelfCheck: Using LLMs to Zero-Shot Check Their Own Step-by-Step Reasoning Ning Miao, Yee Whye Teh, Tom Rainforth

NeurIPS 2024 The Edge-of-Reach Problem in Offline Model-Based Reinforcement Learning Anya Sims, Cong Lu, Jakob N. Foerster, Yee Whye Teh

ICML 2024 Unleashing the Power of Meta-Tuning for Few-Shot Generalization Through Sparse Interpolated Experts Shengzhuang Chen, Jihoon Tack, Yunqiao Yang, Yee Whye Teh, Jonathan Richard Schwarz, Ying Wei

TMLR 2023 Challenges and Opportunities in Offline Reinforcement Learning from Visual Observations Cong Lu, Philip J. Ball, Tim G. J. Rudner, Jack Parker-Holder, Michael A Osborne, Yee Whye Teh

CoLLAs 2023 Continually Learning Representations at Scale Alexandre Galashov, Jovana Mitrovic, Dhruva Tirumala, Yee Whye Teh, Timothy Nguyen, Arslan Chaudhry, Razvan Pascanu

NeurIPS 2023 Deep Stochastic Processes via Functional Markov Transition Operators Jin Xu, Emilien Dupont, Kaspar Märtens, Thomas Rainforth, Yee Whye Teh

ICLR 2023 Deep Transformers Without Shortcuts: Modifying Self-Attention for Faithful Signal Propagation Bobby He, James Martens, Guodong Zhang, Aleksandar Botev, Andrew Brock, Samuel L Smith, Yee Whye Teh

ICML 2023 Drug Discovery Under Covariate Shift with Domain-Informed Prior Distributions over Functions Leo Klarner, Tim G. J. Rudner, Michael Reutlinger, Torsten Schindler, Garrett M Morris, Charlotte Deane, Yee Whye Teh

NeurIPS 2023 Geometric Neural Diffusion Processes Emile Mathieu, Vincent Dutordoir, Michael Hutchinson, Valentin De Bortoli, Yee Whye Teh, Richard Turner

ICML 2023 Learning Instance-Specific Augmentations by Capturing Local Invariances Ning Miao, Tom Rainforth, Emile Mathieu, Yann Dubois, Yee Whye Teh, Adam Foster, Hyunjik Kim

ICML 2023 Modality-Agnostic Variational Compression of Implicit Neural Representations Jonathan Richard Schwarz, Jihoon Tack, Yee Whye Teh, Jaeho Lee, Jinwoo Shin

JMLR 2023 Nevis'22: A Stream of 100 Tasks Sampled from 30 Years of Computer Vision Research Jorg Bornschein, Alexandre Galashov, Ross Hemsley, Amal Rannen-Triki, Yutian Chen, Arslan Chaudhry, Xu Owen He, Arthur Douillard, Massimo Caccia, Qixuan Feng, Jiajun Shen, Sylvestre-Alvise Rebuffi, Kitty Stacpoole, Diego de las Casas, Will Hawkins, Angeliki Lazaridou, Yee Whye Teh, Andrei A. Rusu, Razvan Pascanu, Marc’Aurelio Ranzato

ICLR 2023 Pre-Training via Denoising for Molecular Property Prediction Sheheryar Zaidi, Michael Schaarschmidt, James Martens, Hyunjik Kim, Yee Whye Teh, Alvaro Sanchez-Gonzalez, Peter Battaglia, Razvan Pascanu, Jonathan Godwin

NeurIPSW 2023 Revisiting Dynamic Evaluation: Online Adaptation for Large Language Models Amal Rannen-Triki, Jorg Bornschein, Razvan Pascanu, Alexandre Galashov, Michalis Titsias, Marcus Hutter, András György, Yee Whye Teh

NeurIPSW 2023 Stochastic Linear Dynamics in Parameters to Deal with Neural Networks Plasticity Loss Alexandre Galashov, Michalis Titsias, Razvan Pascanu, Yee Whye Teh, Maneesh Sahani

NeurIPS 2023 Synthetic Experience Replay Cong Lu, Philip Ball, Yee Whye Teh, Jack Parker-Holder

ICMLW 2023 Synthetic Experience Replay Cong Lu, Philip J. Ball, Yee Whye Teh, Jack Parker-Holder

NeurIPSW 2023 The Edge-of-Reach Problem in Offline Model-Based Reinforcement Learning Anya Sims, Cong Lu, Yee Whye Teh

TMLR 2023 UncertaINR: Uncertainty Quantification of End-to-End Implicit Neural Representations for Computed Tomography Francisca Vasconcelos, Bobby He, Nalini M Singh, Yee Whye Teh

ECML-PKDD 2022 Bayesian Nonparametrics for Sparse Dynamic Networks Cian Naik, François Caron, Judith Rousseau, Yee Whye Teh, Konstantina Palla

JMLR 2022 Behavior Priors for Efficient Reinforcement Learning Dhruva Tirumala, Alexandre Galashov, Hyeonwoo Noh, Leonard Hasenclever, Razvan Pascanu, Jonathan Schwarz, Guillaume Desjardins, Wojciech Marian Czarnecki, Arun Ahuja, Yee Whye Teh, Nicolas Heess

TMLR 2022 COIN++: Neural Compression Across Modalities Emilien Dupont, Hrushikesh Loya, Milad Alizadeh, Adam Golinski, Yee Whye Teh, Arnaud Doucet

ICMLW 2022 Challenges and Opportunities in Offline Reinforcement Learning from Visual Observations Cong Lu, Philip J. Ball, Tim G. J. Rudner, Jack Parker-Holder, Michael A Osborne, Yee Whye Teh

NeurIPS 2022 Conformal Off-Policy Prediction in Contextual Bandits Muhammad Faaiz Taufiq, Jean-Francois Ton, Rob Cornish, Yee Whye Teh, Arnaud Doucet

ICML 2022 Continual Learning via Sequential Function-Space Variational Inference Tim G. J. Rudner, Freddie Bickford Smith, Qixuan Feng, Yee Whye Teh, Yarin Gal

TMLR 2022 Meta-Learning Sparse Compression Networks Jonathan Schwarz, Yee Whye Teh

ICLR 2022 On Incorporating Inductive Biases into VAEs Ning Miao, Emile Mathieu, Siddharth N, Yee Whye Teh, Tom Rainforth

NeurIPSW 2022 Pre-Training via Denoising for Molecular Property Prediction Sheheryar Zaidi, Michael Schaarschmidt, James Martens, Hyunjik Kim, Yee Whye Teh, Alvaro Sanchez-Gonzalez, Peter Battaglia, Razvan Pascanu, Jonathan Godwin

NeurIPS 2022 Riemannian Score-Based Generative Modelling Valentin De Bortoli, Emile Mathieu, Michael Hutchinson, James Thornton, Yee Whye Teh, Arnaud Doucet

NeurIPS 2022 Tractable Function-Space Variational Inference in Bayesian Neural Networks Tim G. J. Rudner, Zonghao Chen, Yee Whye Teh, Yarin Gal

NeurIPSW 2022 When Does Re-Initialization Work? Sheheryar Zaidi, Tudor Berariu, Hyunjik Kim, Jorg Bornschein, Claudia Clopath, Yee Whye Teh, Razvan Pascanu

ICLRW 2021 COIN: COmpression with Implicit Neural Representations Emilien Dupont, Adam Golinski, Milad Alizadeh, Yee Whye Teh, Arnaud Doucet

ICML 2021 Equivariant Learning of Stochastic Fields: Gaussian Processes and Steerable Conditional Neural Processes Peter Holderrieth, Michael J Hutchinson, Yee Whye Teh

ICML 2021 LieTransformer: Equivariant Self-Attention for Lie Groups Michael J Hutchinson, Charline Le Lan, Sheheryar Zaidi, Emilien Dupont, Yee Whye Teh, Hyunjik Kim

ICLR 2021 Robust Pruning at Initialization Soufiane Hayou, Jean-Francois Ton, Arnaud Doucet, Yee Whye Teh

AISTATS 2020 A Unified Stochastic Gradient Approach to Designing Bayesian-Optimal Experiments Adam Foster, Martin Jankowiak, Matthew O’Meara, Yee Whye Teh, Tom Rainforth

NeurIPS 2020 Bayesian Deep Ensembles via the Neural Tangent Kernel Bobby He, Balaji Lakshminarayanan, Yee Whye Teh

NeurIPS 2020 Bootstrapping Neural Processes Juho Lee, Yoonho Lee, Jungtaek Kim, Eunho Yang, Sung Ju Hwang, Yee Whye Teh

ICLR 2020 Deep Amortized Clustering Juho Lee, Yoonho Lee, Yee Whye Teh

ICML 2020 Divide, Conquer, and Combine: A New Inference Strategy for Probabilistic Programs with Stochastic Support Yuan Zhou, Hongseok Yang, Yee Whye Teh, Tom Rainforth

ICML 2020 Fractional Underdamped Langevin Dynamics: Retargeting SGD with Momentum Under Heavy-Tailed Gradient Noise Umut Simsekli, Lingjiong Zhu, Yee Whye Teh, Mert Gurbuzbalaban

ICLR 2020 Functional Regularisation for Continual Learning with Gaussian Processes Michalis K. Titsias, Jonathan Schwarz, Alexander G. de G. Matthews, Razvan Pascanu, Yee Whye Teh

NeurIPS 2020 How Robust Are the Estimated Effects of Nonpharmaceutical Interventions Against COVID-19? Mrinank Sharma, Sören Mindermann, Jan Brauner, Gavin Leech, Anna Stephenson, Tomáš Gavenčiak, Jan Kulveit, Yee Whye Teh, Leonid Chindelevitch, Yarin Gal

ICML 2020 MetaFun: Meta-Learning with Iterative Functional Updates Jin Xu, Jean-Francois Ton, Hyunjik Kim, Adam Kosiorek, Yee Whye Teh

ICLR 2020 Multiplicative Interactions and Where to Find Them Siddhant M. Jayakumar, Wojciech M. Czarnecki, Jacob Menick, Jonathan Schwarz, Jack Rae, Simon Osindero, Yee Whye Teh, Tim Harley, Razvan Pascanu

AISTATS 2020 Non-Exchangeable Feature Allocation Models with Sublinear Growth of the Feature Sizes Giuseppe Di Benedetto, Francois Caron, Yee Whye Teh

JMLR 2020 Probabilistic Symmetries and Invariant Neural Networks Benjamin Bloem-Reddy, Yee Whye Teh

ICMLW 2020 Task Agnostic Continual Learning via Meta Learning Xu He, Jakub Sygnowski, Alexandre Galashov, Andrei Alex Rusu, Yee Whye Teh, Razvan Pascanu

ICML 2020 Uncertainty Estimation Using a Single Deep Deterministic Neural Network Joost Van Amersfoort, Lewis Smith, Yee Whye Teh, Yarin Gal

ICLR 2019 A Statistical Approach to Assessing Neural Network Robustness Stefan Webb, Tom Rainforth, Yee Whye Teh, M. Pawan Kumar

ICLR 2019 Attentive Neural Processes Hyunjik Kim, Andriy Mnih, Jonathan Schwarz, Marta Garnelo, Ali Eslami, Dan Rosenbaum, Oriol Vinyals, Yee Whye Teh

NeurIPS 2019 Augmented Neural ODEs Emilien Dupont, Arnaud Doucet, Yee Whye Teh

NeurIPS 2019 Continual Unsupervised Representation Learning Dushyant Rao, Francesco Visin, Andrei Rusu, Razvan Pascanu, Yee Whye Teh, Raia Hadsell

NeurIPS 2019 Continuous Hierarchical Representations with Poincaré Variational Auto-Encoders Emile Mathieu, Charline Le Lan, Chris J. Maddison, Ryota Tomioka, Yee Whye Teh

ICML 2019 Disentangling Disentanglement in Variational Autoencoders Emile Mathieu, Tom Rainforth, N Siddharth, Yee Whye Teh

ICLR 2019 Do Deep Generative Models Know What They Don't Know? Eric Nalisnick, Akihiro Matsukawa, Yee Whye Teh, Dilan Gorur, Balaji Lakshminarayanan

ICML 2019 Hybrid Models with Deep and Invertible Features Eric Nalisnick, Akihiro Matsukawa, Yee Whye Teh, Dilan Gorur, Balaji Lakshminarayanan

ICLR 2019 Information Asymmetry in KL-Regularized RL Alexandre Galashov, Siddhant M. Jayakumar, Leonard Hasenclever, Dhruva Tirumala, Jonathan Schwarz, Guillaume Desjardins, Wojciech M. Czarnecki, Yee Whye Teh, Razvan Pascanu, Nicolas Heess

ICLR 2019 Neural Probabilistic Motor Primitives for Humanoid Control Josh Merel, Leonard Hasenclever, Alexandre Galashov, Arun Ahuja, Vu Pham, Greg Wayne, Yee Whye Teh, Nicolas Heess

NeurIPS 2019 Random Tessellation Forests Shufei Ge, Shijia Wang, Yee Whye Teh, Liangliang Wang, Lloyd Elliott

UAI 2019 Revisiting Reweighted Wake-Sleep for Models with Stochastic Control Flow Tuan Anh Le, Adam R. Kosiorek, N. Siddharth, Yee Whye Teh, Frank Wood

ICML 2019 Set Transformer: A Framework for Attention-Based Permutation-Invariant Neural Networks Juho Lee, Yoonho Lee, Jungtaek Kim, Adam Kosiorek, Seungjin Choi, Yee Whye Teh

NeurIPS 2019 Stacked Capsule Autoencoders Adam Kosiorek, Sara Sabour, Yee Whye Teh, Geoffrey E. Hinton

NeurIPS 2019 Variational Bayesian Optimal Experimental Design Adam Foster, Martin Jankowiak, Elias Bingham, Paul Horsfall, Yee Whye Teh, Thomas Rainforth, Noah Goodman

AISTATS 2018 An Analysis of Categorical Distributional Reinforcement Learning Mark Rowland, Marc G. Bellemare, Will Dabney, Rémi Munos, Yee Whye Teh

NeurIPS 2018 Causal Inference via Kernel Deviance Measures Jovana Mitrovic, Dino Sejdinovic, Yee Whye Teh

ICML 2018 Conditional Neural Processes Marta Garnelo, Dan Rosenbaum, Christopher Maddison, Tiago Ramalho, David Saxton, Murray Shanahan, Yee Whye Teh, Danilo Rezende, S. M. Ali Eslami

NeurIPS 2018 Faithful Inversion of Generative Models for Effective Amortized Inference Stefan Webb, Adam Golinski, Rob Zinkov, Siddharth N, Tom Rainforth, Yee Whye Teh, Frank Wood

ICML 2018 Mix & Match Agent Curricula for Reinforcement Learning Wojciech Czarnecki, Siddhant Jayakumar, Max Jaderberg, Leonard Hasenclever, Yee Whye Teh, Nicolas Heess, Simon Osindero, Razvan Pascanu

NeurIPS 2018 Modelling Sparsity, Heterogeneity, Reciprocity and Community Structure in Temporal Interaction Data Xenia Miscouridou, Francois Caron, Yee Whye Teh

ICML 2018 Progress & Compress: A Scalable Framework for Continual Learning Jonathan Schwarz, Wojciech Czarnecki, Jelena Luketina, Agnieszka Grabska-Barwinska, Yee Whye Teh, Razvan Pascanu, Raia Hadsell

UAI 2018 Sampling and Inference for Beta Neutral-to-the-Left Models of Sparse Networks Benjamin Bloem-Reddy, Adam Foster, Emile Mathieu, Yee Whye Teh

AISTATS 2018 Scaling up the Automatic Statistician: Scalable Structure Discovery Using Gaussian Processes Hyunjik Kim, Yee Whye Teh

NeurIPS 2018 Sequential Attend, Infer, Repeat: Generative Modelling of Moving Objects Adam Kosiorek, Hyunjik Kim, Yee Whye Teh, Ingmar Posner

NeurIPS 2018 Stochastic Expectation Maximization with Variance Reduction Jianfei Chen, Jun Zhu, Yee Whye Teh, Tong Zhang

ICML 2018 Tighter Variational Bounds Are Not Necessarily Better Tom Rainforth, Adam Kosiorek, Tuan Anh Le, Chris Maddison, Maximilian Igl, Frank Wood, Yee Whye Teh

ICLR 2017 Deep Kernel Machines via the Kernel Reparametrization Trick Jovana Mitrovic, Dino Sejdinovic, Yee Whye Teh

JMLR 2017 Distributed Bayesian Learning with Stochastic Natural Gradient Expectation Propagation and the Posterior Server Leonard Hasenclever, Stefan Webb, Thibaut Lienart, Sebastian Vollmer, Balaji Lakshminarayanan, Charles Blundell, Yee Whye Teh

ICLR 2017 Particle Value Functions Chris J. Maddison, Dieterich Lawson, George Tucker, Nicolas Heess, Arnaud Doucet, Andriy Mnih, Yee Whye Teh

AISTATS 2017 Poisson Intensity Estimation with Reproducing Kernels Seth R. Flaxman, Yee Whye Teh, Dino Sejdinovic

JMLR 2017 Poisson Random Fields for Dynamic Feature Models Valerio Perrone, Paul A. Jenkins, Dario Spanò, Yee Whye Teh

AISTATS 2017 Relativistic Monte Carlo Xiaoyu Lu, Valerio Perrone, Leonard Hasenclever, Yee Whye Teh, Sebastian J. Vollmer

ICLR 2017 The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables Chris J. Maddison, Andriy Mnih, Yee Whye Teh

JMLR 2016 Consistency and Fluctuations for Stochastic Gradient Langevin Dynamics Yee Whye Teh, Alexandre H. Thiery, Sebastian J. Vollmer

ICML 2016 DR-ABC: Approximate Bayesian Computation with Kernel-Based Distribution Regression Jovana Mitrovic, Dino Sejdinovic, Yee-Whye Teh

JMLR 2016 Exploration of the (Non-)Asymptotic Bias and Variance of Stochastic Gradient Langevin Dynamics Sebastian J. Vollmer, Konstantinos C. Zygalakis, Yee Whye Teh

NeurIPS 2016 Gaussian Processes for Survival Analysis Tamara Fernandez, Nicolas Rivera, Yee Whye Teh

AISTATS 2016 Mondrian Forests for Large-Scale Regression When Uncertainty Matters Balaji Lakshminarayanan, Daniel M. Roy, Yee Whye Teh

AutoML 2016 Scalable Structure Discovery in Regression Using Gaussian Processes Hyunjik Kim, Yee Whye Teh

UAI 2016 The Mondrian Kernel Matej Balog, Balaji Lakshminarayanan, Zoubin Ghahramani, Daniel M. Roy, Yee Whye Teh

NeurIPS 2015 A Hybrid Sampler for Poisson-Kingman Mixture Models Maria Lomeli, Stefano Favaro, Yee Whye Teh

JMLR 2015 Bayesian Nonparametric Crowdsourcing Pablo G. Moreno, Antonio Artes-Rodriguez, Yee Whye Teh, Fernando Perez-Cruz

NeurIPS 2015 Expectation Particle Belief Propagation Thibaut Lienart, Yee Whye Teh, Arnaud Doucet

AISTATS 2015 Particle Gibbs for Bayesian Additive Regression Trees Balaji Lakshminarayanan, Daniel M. Roy, Yee Whye Teh

NeurIPS 2014 Asynchronous Anytime Sequential Monte Carlo Brooks Paige, Frank Wood, Arnaud Doucet, Yee Whye Teh

NeurIPS 2014 Distributed Bayesian Posterior Sampling via Moment Sharing Minjie Xu, Balaji Lakshminarayanan, Yee Whye Teh, Jun Zhu, Bo Zhang

NeurIPS 2014 Mondrian Forests: Efficient Online Random Forests Balaji Lakshminarayanan, Daniel M. Roy, Yee Whye Teh

NeurIPS 2013 Bayesian Hierarchical Community Discovery Charles Blundell, Yee Whye Teh

JMLR 2013 Fast MCMC Sampling for Markov Jump Processes and Extensions Vinayak Rao, Yee Whye Teh

NeurIPS 2013 Learning with Invariance via Linear Functionals on Reproducing Kernel Hilbert Space Xinhua Zhang, Wee Sun Lee, Yee Whye Teh

NeurIPS 2013 Stochastic Gradient Riemannian Langevin Dynamics on the Probability Simplex Sam Patterson, Yee Whye Teh

ICML 2012 A Fast and Simple Algorithm for Training Neural Probabilistic Language Models Andriy Mnih, Yee Whye Teh

ICML 2011 Bayesian Learning via Stochastic Gradient Langevin Dynamics Max Welling, Yee Whye Teh

UAI 2011 Fast MCMC Sampling for Markov Jump Processes and Continuous Time Bayesian Networks Vinayak A. Rao, Yee Whye Teh

AISTATS 2011 Mixed Cumulative Distribution Networks Ricardo Silva, Charles Blundell, Yee Whye Teh

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

AISTATS 2010 Preface Yee Whye Teh, Mike Titterington

AISTATS 2009 A Hierarchical Nonparametric Bayesian Approach to Statistical Language Model Domain Adaptation Frank Wood, Yee Whye Teh

ICML 2009 A Stochastic Memoizer for Sequence Data Frank D. Wood, Cédric Archambeau, Jan Gasthaus, Lancelot James, Yee Whye Teh

AISTATS 2009 Infinite Hierarchical Hidden Markov Models Katherine Heller, Yee Whye Teh, Dilan Gorur

UAI 2009 On Smoothing and Inference for Topic Models Arthur U. Asuncion, Max Welling, Padhraic Smyth, Yee Whye Teh

AISTATS 2009 Variational Inference for the Indian Buffet Process Finale Doshi, Kurt Miller, Jurgen Van Gael, Yee Whye Teh

ICML 2008 Beam Sampling for the Infinite Hidden Markov Model Jurgen Van Gael, Yunus Saatci, Yee Whye Teh, Zoubin Ghahramani

UAI 2008 Hybrid Variational/Gibbs Collapsed Inference in Topic Models Max Welling, Yee Whye Teh, Bert Kappen

IJCAI 2007 Collapsed Variational Dirichlet Process Mixture Models Kenichi Kurihara, Max Welling, Yee Whye Teh

AISTATS 2007 Stick-Breaking Construction for the Indian Buffet Process Yee Whye Teh, Dilan Grür, Zoubin Ghahramani

ICML 2006 Bayesian Multi-Population Haplotype Inference via a Hierarchical Dirichlet Process Mixture Eric P. Xing, Kyung-Ah Sohn, Michael I. Jordan, Yee Whye Teh

AISTATS 2005 Semiparametric Latent Factor Models Yee Whye Teh, Matthias Seeger, Michael I. Jordan

UAI 2005 Structured Region Graphs: Morphing EP into GBP Max Welling, Thomas P. Minka, Yee Whye Teh

ICML 2004 Approximate Inference by Markov Chains on Union Spaces Max Welling, Michal Rosen-Zvi, Yee Whye Teh

NeCo 2004 Linear Response Algorithms for Approximate Inference in Graphical Models Max Welling, Yee Whye Teh

CVPR 2004 Names and Faces in the News Tamara L. Berg, Alexander C. Berg, Jaety Edwards, Michael Maire, Ryan White, Yee Whye Teh, Erik G. Learned-Miller, David A. Forsyth

JMLR 2003 Energy-Based Models for Sparse Overcomplete Representations Yee Whye Teh, Max Welling, Simon Osindero, Geoffrey E. Hinton

AISTATS 2003 On Improving the Efficiency of the Iterative Proportional Fitting Procedure Yee Whye Teh, Max Welling

ICML 2002 An Alternate Objective Function for Markovian Fields Sham M. Kakade, Yee Whye Teh, Sam T. Roweis

UAI 2001 Belief Optimization for Binary Networks: A Stable Alternative to Loopy Belief Propagation Max Welling, Yee Whye Teh

UAI 2001 Discovering Multiple Constraints That Are Frequently Approximately Satisfied Geoffrey E. Hinton, Yee Whye Teh

NeurIPS 2000 Rate-Coded Restricted Boltzmann Machines for Face Recognition Yee Whye Teh, Geoffrey E. Hinton

NeurIPS 1999 Learning to Parse Images Geoffrey E. Hinton, Zoubin Ghahramani, Yee Whye Teh