Yee Whye Teh
Publication record assembled from the DBLP archive of ranked conferences.
Papers indexed
82
Venues
12
Active years
2001–2025
Best venue rank
A*
Where they publish
Papers
82 indexed papers, newest first.
| Year | Venue | Title | Authors |
|---|---|---|---|
| 2025 | ICLR | L3Ms - Lagrange Large Language Models. | Guneet S. Dhillon, Xingjian Shi, Yee Whye Teh, Alex Smola |
| 2025 | ICLR | 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 |
| 2025 | ICLR | SymDiff: Equivariant Diffusion via Stochastic Symmetrisation. | Leo Zhang, Kianoosh Ashouritaklimi, Yee Whye Teh, Rob Cornish |
| 2024 | ICLR | SelfCheck: Using LLMs to Zero-Shot Check Their Own Step-by-Step Reasoning. | Ning Miao, Yee Whye Teh, Tom Rainforth |
| 2024 | ICLR | Kalman Filter for Online Classification of Non-Stationary Data. | Michalis K. Titsias, Alexandre Galashov, Amal Rannen-Triki, Razvan Pascanu, Yee Whye Teh, Jrg Bornschein |
| 2024 | ICML | 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 |
| 2024 | ICML | Context-Guided Diffusion for Out-of-Distribution Molecular and Protein Design. | Leo Klarner, Tim G. J. Rudner, Garrett M. Morris, Charlotte M. Deane, Yee Whye Teh |
| 2024 | ICML | Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI. | Theodore Papamarkou, Maria Skoularidou, Konstantina Palla, Laurence Aitchison, Julyan Arbel, David B. Dunson, Maurizio Filippone, Vincent Fortuin, Philipp Hennig, Jos Miguel Hernndez-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 Rgamer, Yee Whye Teh, Max Welling, Andrew Gordon Wilson, Ruqi Zhang |
| 2024 | ICML | EvIL: Evolution Strategies for Generalisable Imitation Learning. | Silvia Sapora, Gokul Swamy, Chris Lu, Yee Whye Teh, Jakob Nicolaus Foerster |
| 2023 | ICLR | 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 |
| 2023 | ICLR | Pre-training via Denoising for Molecular Property Prediction. | Sheheryar Zaidi, Michael Schaarschmidt, James Martens, Hyunjik Kim, Yee Whye Teh, Alvaro Sanchez-Gonzalez, Peter W. Battaglia, Razvan Pascanu, Jonathan Godwin |
| 2023 | ICML | 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 M. Deane, Yee Whye Teh |
| 2023 | ICML | Learning Instance-Specific Augmentations by Capturing Local Invariances. | Ning Miao, Tom Rainforth, Emile Mathieu, Yann Dubois, Yee Whye Teh, Adam Foster, Hyunjik Kim |
| 2023 | ICML | Modality-Agnostic Variational Compression of Implicit Neural Representations. | Jonathan Richard Schwarz, Jihoon Tack, Yee Whye Teh, Jaeho Lee, Jinwoo Shin |
| 2022 | AISTATS | Generative Models as Distributions of Functions. | Emilien Dupont, Yee Whye Teh, Arnaud Doucet |
| 2022 | AISTATS | Amortized Rejection Sampling in Universal Probabilistic Programming. | Saeid Naderiparizi, Adam Scibior, Andreas Munk, Mehrdad Ghadiri, Atilim Gunes Baydin, Bradley J. Gram-Hansen, Christian A. Schrder de Witt, Robert Zinkov, Philip H. S. Torr, Tom Rainforth, Yee Whye Teh, Frank Wood |
| 2022 | ICLR | On Incorporating Inductive Biases into VAEs. | Ning Miao, Emile Mathieu, Siddharth N, Yee Whye Teh, Tom Rainforth |
| 2022 | ICML | Continual Learning via Sequential Function-Space Variational Inference. | Tim G. J. Rudner, Freddie Bickford Smith, Qixuan Feng, Yee Whye Teh, Yarin Gal |
| 2021 | AISTATS | Noise Contrastive Meta-Learning for Conditional Density Estimation using Kernel Mean Embeddings. | Jean-Francois Ton, Lucian Chan, Yee Whye Teh, Dino Sejdinovic |
| 2021 | ICLR | Robust Pruning at Initialization. | Soufiane Hayou, Jean-Francois Ton, Arnaud Doucet, Yee Whye Teh |
| 2021 | ICML | Equivariant Learning of Stochastic Fields: Gaussian Processes and Steerable Conditional Neural Processes. | Peter Holderrieth, Michael J. Hutchinson, Yee Whye Teh |
| 2021 | ICML | LieTransformer: Equivariant Self-Attention for Lie Groups. | Michael J. Hutchinson, Charline Le Lan, Sheheryar Zaidi, Emilien Dupont, Yee Whye Teh, Hyunjik Kim |
| 2020 | AISTATS | Non-exchangeable feature allocation models with sublinear growth of the feature sizes. | Giuseppe Di Benedetto, Francois Caron, Yee Whye Teh |
| 2020 | AISTATS | A Unified Stochastic Gradient Approach to Designing Bayesian-Optimal Experiments. | Adam Foster, Martin Jankowiak, Matthew O'Meara, Yee Whye Teh, Tom Rainforth |
| 2020 | ICLR | Multiplicative Interactions and Where to Find Them. | Siddhant M. Jayakumar, Wojciech M. Czarnecki, Jacob Menick, Jonathan Schwarz, Jack W. Rae, Simon Osindero, Yee Whye Teh, Tim Harley, Razvan Pascanu |
| 2020 | ICLR | Functional Regularisation for Continual Learning with Gaussian Processes. | Michalis K. Titsias, Jonathan Schwarz, Alexander G. de G. Matthews, Razvan Pascanu, Yee Whye Teh |
| 2020 | ICML | Uncertainty Estimation Using a Single Deep Deterministic Neural Network. | Joost van Amersfoort, Lewis Smith, Yee Whye Teh, Yarin Gal |
| 2020 | ICML | Fractional Underdamped Langevin Dynamics: Retargeting SGD with Momentum under Heavy-Tailed Gradient Noise. | Umut Simsekli, Lingjiong Zhu, Yee Whye Teh, Mert Grbzbalaban |
| 2020 | ICML | MetaFun: Meta-Learning with Iterative Functional Updates. | Jin Xu, Jean-Francois Ton, Hyunjik Kim, Adam R. Kosiorek, Yee Whye Teh |
| 2020 | ICML | Divide, Conquer, and Combine: a New Inference Strategy for Probabilistic Programs with Stochastic Support. | Yuan Zhou, Hongseok Yang, Yee Whye Teh, Tom Rainforth |
| 2019 | ICLR | 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 |
| 2019 | ICLR | Attentive Neural Processes. | Hyunjik Kim, Andriy Mnih, Jonathan Schwarz, Marta Garnelo, S. M. Ali Eslami, Dan Rosenbaum, Oriol Vinyals, Yee Whye Teh |
| 2019 | ICLR | Neural Probabilistic Motor Primitives for Humanoid Control. | Josh Merel, Leonard Hasenclever, Alexandre Galashov, Arun Ahuja, Vu Pham, Greg Wayne, Yee Whye Teh, Nicolas Heess |
| 2019 | ICLR | Do Deep Generative Models Know What They Don't Know? | Eric T. Nalisnick, Akihiro Matsukawa, Yee Whye Teh, Dilan Grr, Balaji Lakshminarayanan |
| 2019 | ICLR | A Statistical Approach to Assessing Neural Network Robustness. | Stefan Webb, Tom Rainforth, Yee Whye Teh, M. Pawan Kumar |
| 2019 | ICML | Set Transformer: A Framework for Attention-based Permutation-Invariant Neural Networks. | Juho Lee, Yoonho Lee, Jungtaek Kim, Adam R. Kosiorek, Seungjin Choi, Yee Whye Teh |
| 2019 | ICML | Disentangling Disentanglement in Variational Autoencoders. | Emile Mathieu, Tom Rainforth, N. Siddharth, Yee Whye Teh |
| 2019 | ICML | Hybrid Models with Deep and Invertible Features. | Eric T. Nalisnick, Akihiro Matsukawa, Yee Whye Teh, Dilan Grr, Balaji Lakshminarayanan |
| 2019 | UAI | Revisiting Reweighted Wake-Sleep for Models with Stochastic Control Flow. | Tuan Anh Le, Adam R. Kosiorek, N. Siddharth, Yee Whye Teh, Frank Wood |
| 2018 | AISTATS | Scaling up the Automatic Statistician: Scalable Structure Discovery using Gaussian Processes. | Hyunjik Kim, Yee Whye Teh |
| 2018 | AISTATS | An Analysis of Categorical Distributional Reinforcement Learning. | Mark Rowland, Marc G. Bellemare, Will Dabney, Rmi Munos, Yee Whye Teh |
| 2018 | ICML | Mix & Match Agent Curricula for Reinforcement Learning. | Wojciech Marian Czarnecki, Siddhant M. Jayakumar, Max Jaderberg, Leonard Hasenclever, Yee Whye Teh, Nicolas Heess, Simon Osindero, Razvan Pascanu |
| 2018 | ICML | Conditional Neural Processes. | Marta Garnelo, Dan Rosenbaum, Christopher Maddison, Tiago Ramalho, David Saxton, Murray Shanahan, Yee Whye Teh, Danilo Jimenez Rezende, S. M. Ali Eslami |
| 2018 | ICML | Tighter Variational Bounds are Not Necessarily Better. | Tom Rainforth, Adam R. Kosiorek, Tuan Anh Le, Chris J. Maddison, Maximilian Igl, Frank Wood, Yee Whye Teh |
| 2018 | ICML | Progress & Compress: A scalable framework for continual learning. | Jonathan Schwarz, Wojciech Czarnecki, Jelena Luketina, Agnieszka Grabska-Barwinska, Yee Whye Teh, Razvan Pascanu, Raia Hadsell |
| 2018 | KDD | On Big Data Learning for Small Data Problems. | Yee Whye Teh |
| 2018 | UAI | Sampling and Inference for Beta Neutral-to-the-Left Models of Sparse Networks. | Benjamin Bloem-Reddy, Adam Foster, Emile Mathieu, Yee Whye Teh |
| 2017 | AISTATS | Poisson intensity estimation with reproducing kernels. | Seth R. Flaxman, Yee Whye Teh, Dino Sejdinovic |
| 2017 | AISTATS | Relativistic Monte Carlo. | Xiaoyu Lu, Valerio Perrone, Leonard Hasenclever, Yee Whye Teh, Sebastian J. Vollmer |
| 2017 | ICLR | Particle Value Functions. | Chris J. Maddison, Dieterich Lawson, George Tucker, Nicolas Heess, Arnaud Doucet, Andriy Mnih, Yee Whye Teh |
| 2017 | ICLR | The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables. | Chris J. Maddison, Andriy Mnih, Yee Whye Teh |
| 2017 | ICLR | Deep Kernel Machines via the Kernel Reparametrization Trick. | Jovana Mitrovic, Dino Sejdinovic, Yee Whye Teh |
| 2016 | AISTATS | Mondrian Forests for Large-Scale Regression when Uncertainty Matters. | Balaji Lakshminarayanan, Daniel M. Roy, Yee Whye Teh |
| 2016 | ICML | Scalable Structure Discovery in Regression using Gaussian Processes. | Hyunjik Kim, Yee Whye Teh |
| 2016 | ICML | DR-ABC: Approximate Bayesian Computation with Kernel-Based Distribution Regression. | Jovana Mitrovic, Dino Sejdinovic, Yee Whye Teh |
| 2016 | UAI | The Mondrian Kernel. | Matej Balog, Balaji Lakshminarayanan, Zoubin Ghahramani, Daniel M. Roy, Yee Whye Teh |
| 2015 | AISTATS | Particle Gibbs for Bayesian Additive Regression Trees. | Balaji Lakshminarayanan, Daniel M. Roy, Yee Whye Teh |
| 2013 | ICML | Dependent Normalized Random Measures. | Changyou Chen, Vinayak A. Rao, Wray L. Buntine, Yee Whye Teh |
| 2013 | ICML | Top-down particle filtering for Bayesian decision trees. | Balaji Lakshminarayanan, Daniel M. Roy, Yee Whye Teh |
| 2012 | ICML | A fast and simple algorithm for training neural probabilistic language models. | Andriy Mnih, Yee Whye Teh |
| 2011 | CoNLL | (Invited talk) Bayesian Tools for Natural Language Learning. | Yee Whye Teh |
| 2011 | ICML | Bayesian Learning via Stochastic Gradient Langevin Dynamics. | Max Welling, Yee Whye Teh |
| 2011 | UAI | Fast MCMC sampling for Markov jump processes and continuous time Bayesian networks. | Vinayak A. Rao, Yee Whye Teh |
| 2010 | DCC | Lossless Compression Based on the Sequence Memoizer. | Jan Gasthaus, Frank D. Wood, Yee Whye Teh |
| 2010 | UAI | Bayesian Rose Trees. | Charles Blundell, Yee Whye Teh, Katherine A. Heller |
| 2009 | ICML | A stochastic memoizer for sequence data. | Frank D. Wood, Cdric Archambeau, Jan Gasthaus, Lancelot James, Yee Whye Teh |
| 2009 | NAACL | Hierarchical Dirichlet Trees for Information Retrieval. | Gholamreza Haffari, Yee Whye Teh |
| 2009 | UAI | On Smoothing and Inference for Topic Models. | Arthur U. Asuncion, Max Welling, Padhraic Smyth, Yee Whye Teh |
| 2008 | ICML | Beam sampling for the infinite hidden Markov model. | Jurgen Van Gael, Yunus Saatci, Yee Whye Teh, Zoubin Ghahramani |
| 2008 | UAI | Hybrid Variational/Gibbs Collapsed Inference in Topic Models. | Max Welling, Yee Whye Teh, Bert Kappen |
| 2007 | EMNLP | Improving Word Sense Disambiguation Using Topic Features. | Junfu Cai, Wee Sun Lee, Yee Whye Teh |
| 2007 | IJCAI | Collapsed Variational Dirichlet Process Mixture Models. | Kenichi Kurihara, Max Welling, Yee Whye Teh |
| 2006 | ACL | A Hierarchical Bayesian Language Model Based On Pitman-Yor Processes. | Yee Whye Teh |
| 2006 | ICML | Bayesian multi-population haplotype inference via a hierarchical dirichlet process mixture. | Eric P. Xing, Kyung-Ah Sohn, Michael I. Jordan, Yee Whye Teh |
| 2005 | AISTATS | Semiparametric latent factor models. | Yee Whye Teh, Matthias W. Seeger, Michael I. Jordan |
| 2005 | UAI | Structured Region Graphs: Morphing EP into GBP. | Max Welling, Thomas P. Minka, Yee Whye Teh |
| 2004 | CVPR | 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 |
| 2004 | ICML | Approximate inference by Markov chains on union spaces. | Max Welling, Michal Rosen-Zvi, Yee Whye Teh |
| 2003 | AISTATS | On Improving the Efficiency of the Iterative Proportional Fitting Procedure. | Yee Whye Teh, Max Welling |
| 2002 | ICML | An Alternate Objective Function for Markovian Fields. | Sham M. Kakade, Yee Whye Teh, Sam T. Roweis |
| 2001 | UAI | Discovering Multiple Constraints that are Frequently Approximately Satisfied. | Geoffrey E. Hinton, Yee Whye Teh |
| 2001 | UAI | Belief Optimization for Binary Networks: A Stable Alternative to Loopy Belief Propagation. | Max Welling, Yee Whye Teh |