Yee Whye Teh

ORCID

Publication Activity (10 Years)

Years Active: 1998-2024
Publications (10 Years): 209

Top Venues

Publications

Soham De, Samuel L. Smith, Anushan Fernando, Aleksandar Botev, George-Cristian Muraru, Albert Gu, Ruba Haroun, Leonard Berrada, Yutian Chen, Srivatsan Srinivasan, Guillaume Desjardins, Arnaud Doucet, David Budden, Yee Whye Teh, Razvan Pascanu, Nando de Freitas, Caglar Gulcehre
Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models. CoRR (2024)
Leo Klarner, Tim G. J. Rudner, Garrett M. Morris, Charlotte M. Deane, Yee Whye Teh
Context-Guided Diffusion for Out-of-Distribution Molecular and Protein Design. CoRR (2024)
Ning Miao, Yee Whye Teh, Tom Rainforth
SelfCheck: Using LLMs to Zero-Shot Check Their Own Step-by-Step Reasoning. ICLR (2024)
Silvia Sapora, Gokul Swamy, Chris Lu, Yee Whye Teh, Jakob Nicolaus Foerster
EvIL: Evolution Strategies for Generalisable Imitation Learning. CoRR (2024)
Mrinank Sharma, Tom Rainforth, Yee Whye Teh, Vincent Fortuin
Incorporating Unlabelled Data into Bayesian Neural Networks. Trans. Mach. Learn. Res. 2024 (2024)
Aleksandar Botev, Soham De, Samuel L. Smith, Anushan Fernando, George-Cristian Muraru, Ruba Haroun, Leonard Berrada, Razvan Pascanu, Pier Giuseppe Sessa, Robert Dadashi, Léonard Hussenot, Johan Ferret, Sertan Girgin, Olivier Bachem, Alek Andreev, Kathleen Kenealy, Thomas Mesnard, Cassidy Hardin, Surya Bhupatiraju, Shreya Pathak, Laurent Sifre, Morgane Rivière, Mihir Sanjay Kale, Juliette Love, Pouya Tafti, Armand Joulin, Noah Fiedel, Evan Senter, Yutian Chen, Srivatsan Srinivasan, Guillaume Desjardins, David Budden, Arnaud Doucet, Sharad Vikram, Adam Paszke, Trevor Gale, Sebastian Borgeaud, Charlie Chen, Andy Brock, Antonia Paterson, Jenny Brennan, Meg Risdal, Raj Gundluru, Nesh Devanathan, Paul Mooney, Nilay Chauhan, Phil Culliton, Luiz GUStavo Martins, Elisa Bandy, David Huntsperger, Glenn Cameron, Arthur Zucker, Tris Warkentin, Ludovic Peran, Minh Giang, Zoubin Ghahramani, Clément Farabet, Koray Kavukcuoglu, Demis Hassabis, Raia Hadsell, Yee Whye Teh, Nando de Frietas
RecurrentGemma: Moving Past Transformers for Efficient Open Language Models. CoRR (2024)
Jihoon Tack, Jaehyung Kim, Eric Mitchell, Jinwoo Shin, Yee Whye Teh, Jonathan Richard Schwarz
Online Adaptation of Language Models with a Memory of Amortized Contexts. CoRR (2024)
Anya Sims, Cong Lu, Yee Whye Teh
The Edge-of-Reach Problem in Offline Model-Based Reinforcement Learning. CoRR (2024)
Theodore Papamarkou, Maria Skoularidou, Konstantina Palla, Laurence Aitchison, Julyan Arbel, David B. 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
Position Paper: Bayesian Deep Learning in the Age of Large-Scale AI. CoRR (2024)
Michalis K. Titsias, Alexandre Galashov, Amal Rannen-Triki, Razvan Pascanu, Yee Whye Teh, Jörg Bornschein
Kalman Filter for Online Classification of Non-Stationary Data. ICLR (2024)
Amal Rannen-Triki, Jörg Bornschein, Razvan Pascanu, Marcus Hutter, András György, Alexandre Galashov, Yee Whye Teh, Michalis K. Titsias
Revisiting Dynamic Evaluation: Online Adaptation for Large Language Models. CoRR (2024)
Shengzhuang Chen, Jihoon Tack, Yunqiao Yang, Yee Whye Teh, Jonathan Richard Schwarz, Ying Wei
Unleashing the Power of Meta-tuning for Few-shot Generalization Through Sparse Interpolated Experts. CoRR (2024)
Sheheryar Zaidi, Michael Schaarschmidt, James Martens, Hyunjik Kim, Yee Whye Teh, Alvaro Sanchez-Gonzalez, Peter W. Battaglia, Razvan Pascanu, Jonathan Godwin
Pre-training via Denoising for Molecular Property Prediction. ICLR (2023)
Bobby He, James Martens, Guodong Zhang, Aleksandar Botev, Andrew Brock, Samuel L. Smith, Yee Whye Teh
Deep Transformers without Shortcuts: Modifying Self-attention for Faithful Signal Propagation. CoRR (2023)
Cong Lu, Philip J. Ball, Yee Whye Teh, Jack Parker-Holder
Synthetic Experience Replay. NeurIPS (2023)
Emile Mathieu, Vincent Dutordoir, Michael J. Hutchinson, Valentin De Bortoli, Yee Whye Teh, Richard E. Turner
Geometric Neural Diffusion Processes. CoRR (2023)
Mrinank Sharma, Tom Rainforth, Yee Whye Teh, Vincent Fortuin
Incorporating Unlabelled Data into Bayesian Neural Networks. CoRR (2023)
Ning Miao, Tom Rainforth, Emile Mathieu, Yann Dubois, Yee Whye Teh, Adam Foster, Hyunjik Kim
Learning Instance-Specific Augmentations by Capturing Local Invariances. ICML (2023)
Bobby He, James Martens, Guodong Zhang, Aleksandar Botev, Andrew Brock, Samuel L. Smith, Yee Whye Teh
Deep Transformers without Shortcuts: Modifying Self-attention for Faithful Signal Propagation. ICLR (2023)
Leo Klarner, Tim G. J. Rudner, Michael Reutlinger, Torsten Schindler, Garrett M. Morris, Charlotte M. Deane, Yee Whye Teh
Drug Discovery under Covariate Shift with Domain-Informed Prior Distributions over Functions. ICML (2023)
Jonathan Richard Schwarz, Jihoon Tack, Yee Whye Teh, Jaeho Lee, Jinwoo Shin
Modality-Agnostic Variational Compression of Implicit Neural Representations. CoRR (2023)
Emile Mathieu, Vincent Dutordoir, Michael Hutchinson, Valentin De Bortoli, Yee Whye Teh, Richard E. Turner
Geometric Neural Diffusion Processes. NeurIPS (2023)
Tim G. J. Rudner, Zonghao Chen, Yee Whye Teh, Yarin Gal
Tractable Function-Space Variational Inference in Bayesian Neural Networks. CoRR (2023)
Jin Xu, Emilien Dupont, Kaspar Märtens, Thomas Rainforth, Yee Whye Teh
Deep Stochastic Processes via Functional Markov Transition Operators. NeurIPS (2023)
Tim G. J. Rudner, Freddie Bickford Smith, Qixuan Feng, Yee Whye Teh, Yarin Gal
Continual Learning via Sequential Function-Space Variational Inference. CoRR (2023)
Francisca Vasconcelos, Bobby He, Nalini M. Singh, Yee Whye Teh
UncertaINR: Uncertainty Quantification of End-to-End Implicit Neural Representations for Computed Tomography. Trans. Mach. Learn. Res. 2023 (2023)
Michalis K. Titsias, Alexandre Galashov, Amal Rannen-Triki, Razvan Pascanu, Yee Whye Teh, Jörg Bornschein
Kalman Filter for Online Classification of Non-Stationary Data. CoRR (2023)
Cong Lu, Philip J. Ball, Tim G. J. Rudner, Jack Parker-Holder, Michael A. Osborne, Yee Whye Teh
Challenges and Opportunities in Offline Reinforcement Learning from Visual Observations. Trans. Mach. Learn. Res. 2023 (2023)
Ning Miao, Yee Whye Teh, Tom Rainforth
SelfCheck: Using LLMs to Zero-Shot Check Their Own Step-by-Step Reasoning. CoRR (2023)
Jin Xu, Emilien Dupont, Kaspar Märtens, Tom Rainforth, Yee Whye Teh
Deep Stochastic Processes via Functional Markov Transition Operators. CoRR (2023)
Jonathan Richard Schwarz, Jihoon Tack, Yee Whye Teh, Jaeho Lee, Jinwoo Shin
Modality-Agnostic Variational Compression of Implicit Neural Representations. ICML (2023)
Leo Klarner, Tim G. J. Rudner, Michael Reutlinger, Torsten Schindler, Garrett M. Morris, Charlotte M. Deane, Yee Whye Teh
Drug Discovery under Covariate Shift with Domain-Informed Prior Distributions over Functions. CoRR (2023)
Alexandre Galashov, Jovana Mitrovic, Dhruva Tirumala, Yee Whye Teh, Timothy Nguyen, Arslan Chaudhry, Razvan Pascanu
Continually learning representations at scale. CoLLAs (2023)
Sheheryar Zaidi, Michael Schaarschmidt, James Martens, Hyunjik Kim, Yee Whye Teh, Alvaro Sanchez-Gonzalez, Peter W. Battaglia, Razvan Pascanu, Jonathan Godwin
Pre-training via Denoising for Molecular Property Prediction. CoRR (2022)
Cian Naik, François Caron, Judith Rousseau, Yee Whye Teh, Konstantina Palla
Bayesian Nonparametrics for Sparse Dynamic Networks. ECML/PKDD (5) (2022)
Muhammad Faaiz Taufiq, Jean-Francois Ton, Robert Cornish, Yee Whye Teh, Arnaud Doucet
Conformal Off-Policy Prediction in Contextual Bandits. CoRR (2022)
Ning Miao, Emile Mathieu, Siddharth N, Yee Whye Teh, Tom Rainforth
On Incorporating Inductive Biases into VAEs. ICLR (2022)
Ning Miao, Emile Mathieu, Yann Dubois, Tom Rainforth, Yee Whye Teh, Adam Foster, Hyunjik Kim
Learning Instance-Specific Data Augmentations. CoRR (2022)
Jörg Bornschein, Alexandre Galashov, Ross Hemsley, Amal Rannen-Triki, Yutian Chen, Arslan Chaudhry, Xu Owen He, Arthur Douillard, Massimo Caccia, Qixuang 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
NEVIS'22: A Stream of 100 Tasks Sampled from 30 Years of Computer Vision Research. CoRR (2022)
Sheheryar Zaidi, Tudor Berariu, Hyunjik Kim, Jörg Bornschein, Claudia Clopath, Yee Whye Teh, Razvan Pascanu
When Does Re-initialization Work? CoRR (2022)
James Thornton, Michael Hutchinson, Emile Mathieu, Valentin De Bortoli, Yee Whye Teh, Arnaud Doucet
Riemannian Diffusion Schrödinger Bridge. CoRR (2022)
Dhruva Tirumala, Alexandre Galashov, Hyeonwoo Noh, Leonard Hasenclever, Razvan Pascanu, Jonathan Schwarz, Guillaume Desjardins, Wojciech Marian Czarnecki, Arun Ahuja, Yee Whye Teh, Nicolas Heess
Behavior Priors for Efficient Reinforcement Learning. J. Mach. Learn. Res. 23 (2022)
Tim G. J. Rudner, Freddie Bickford Smith, Qixuan Feng, Yee Whye Teh, Yarin Gal
Continual Learning via Sequential Function-Space Variational Inference. ICML (2022)
Jonathan Schwarz, Yee Whye Teh
Meta-Learning Sparse Compression Networks. Trans. Mach. Learn. Res. 2022 (2022)
Emilien Dupont, Yee Whye Teh, Arnaud Doucet
Generative Models as Distributions of Functions. AISTATS (2022)
Saeid Naderiparizi, Adam Scibior, Andreas Munk, Mehrdad Ghadiri, Atilim Gunes Baydin, Bradley J. Gram-Hansen, Christian A. Schröder de Witt, Robert Zinkov, Philip H. S. Torr, Tom Rainforth, Yee Whye Teh, Frank Wood
Amortized Rejection Sampling in Universal Probabilistic Programming. AISTATS (2022)
Emilien Dupont, Hrushikesh Loya, Milad Alizadeh, Adam Golinski, Yee Whye Teh, Arnaud Doucet
COIN++: Data Agnostic Neural Compression. CoRR (2022)
Muhammad Faaiz Taufiq, Jean-Francois Ton, Rob Cornish, Yee Whye Teh, Arnaud Doucet
Conformal Off-Policy Prediction in Contextual Bandits. NeurIPS (2022)
Francisca Vasconcelos, Bobby He, Nalini Singh, Yee Whye Teh
UncertaINR: Uncertainty Quantification of End-to-End Implicit Neural Representations for Computed Tomography. CoRR (2022)
Valentin De Bortoli, Emile Mathieu, Michael Hutchinson, James Thornton, Yee Whye Teh, Arnaud Doucet
Riemannian Score-Based Generative Modeling. CoRR (2022)
Cong Lu, Philip J. Ball, Tim G. J. Rudner, Jack Parker-Holder, Michael A. Osborne, Yee Whye Teh
Challenges and Opportunities in Offline Reinforcement Learning from Visual Observations. CoRR (2022)
Sheheryar Zaidi, Tudor Berariu, Hyunjik Kim, Jörg Bornschein, Claudia Clopath, Yee Whye Teh, Razvan Pascanu
When Does Re-initialization Work? ICBINB (2022)
Emilien Dupont, Hrushikesh Loya, Milad Alizadeh, Adam Golinski, Yee Whye Teh, Arnaud Doucet
COIN++: Neural Compression Across Modalities. Trans. Mach. Learn. Res. 2022 (2022)
Jonathan Richard Schwarz, Yee Whye Teh
Meta-Learning Sparse Compression Networks. CoRR (2022)
Tim G. J. Rudner, Cong Lu, Michael A. Osborne, Yarin Gal, Yee Whye Teh
On Pathologies in KL-Regularized Reinforcement Learning from Expert Demonstrations. CoRR (2022)
Tim G. J. Rudner, Zonghao Chen, Yee Whye Teh, Yarin Gal
Tractable Function-Space Variational Inference in Bayesian Neural Networks. NeurIPS (2022)
Valentin De Bortoli, Emile Mathieu, Michael Hutchinson, James Thornton, Yee Whye Teh, Arnaud Doucet
Riemannian Score-Based Generative Modelling. NeurIPS (2022)
Jean-Francois Ton, Lucian Chan, Yee Whye Teh, Dino Sejdinovic
Noise Contrastive Meta-Learning for Conditional Density Estimation using Kernel Mean Embeddings. AISTATS (2021)
Tim G. J. Rudner, Cong Lu, Michael A. Osborne, Yarin Gal, Yee Whye Teh
On Pathologies in KL-Regularized Reinforcement Learning from Expert Demonstrations. NeurIPS (2021)
Emile Mathieu, Adam Foster, Yee Whye Teh
On Contrastive Representations of Stochastic Processes. CoRR (2021)
Sheheryar Zaidi, Arber Zela, Thomas Elsken, Chris C. Holmes, Frank Hutter, Yee Whye Teh
Neural Ensemble Search for Uncertainty Estimation and Dataset Shift. NeurIPS (2021)
Soufiane Hayou, Jean-Francois Ton, Arnaud Doucet, Yee Whye Teh
Robust Pruning at Initialization. ICLR (2021)
Peter Holderrieth, Michael Hutchinson, Yee Whye Teh
Equivariant Learning of Stochastic Fields: Gaussian Processes and Steerable Conditional Neural Processes. ICML (2021)
Jonathan Schwarz, Siddhant M. Jayakumar, Razvan Pascanu, Peter E. Latham, Yee Whye Teh
Powerpropagation: A sparsity inducing weight reparameterisation. NeurIPS (2021)
Michael Hutchinson, Charline Le Lan, Sheheryar Zaidi, Emilien Dupont, Yee Whye Teh, Hyunjik Kim
LieTransformer: Equivariant Self-Attention for Lie Groups. ICML (2021)
Michael Hutchinson, Alexander Terenin, Viacheslav Borovitskiy, So Takao, Yee Whye Teh, Marc Peter Deisenroth
Vector-valued Gaussian Processes on Riemannian Manifolds via Gauge Independent Projected Kernels. NeurIPS (2021)
Emilien Dupont, Yee Whye Teh, Arnaud Doucet
Generative Models as Distributions of Functions. CoRR (2021)
Emilien Dupont, Adam Golinski, Milad Alizadeh, Yee Whye Teh, Arnaud Doucet
COIN: COmpression with Implicit Neural representations. CoRR (2021)
Chris J. Maddison, Daniel Paulin, Yee Whye Teh, Arnaud Doucet
Dual Space Preconditioning for Gradient Descent. SIAM J. Optim. 31 (1) (2021)
Jin Xu, Hyunjik Kim, Thomas Rainforth, Yee Whye Teh
Group Equivariant Subsampling. NeurIPS (2021)
Siu Lun Chau, Jean-Francois Ton, Javier González, Yee Whye Teh, Dino Sejdinovic
BayesIMP: Uncertainty Quantification for Causal Data Fusion. NeurIPS (2021)
Michael Hutchinson, Alexander Terenin, Viacheslav Borovitskiy, So Takao, Yee Whye Teh, Marc Peter Deisenroth
Vector-valued Gaussian Processes on Riemannian Manifolds via Gauge Equivariant Projected Kernels. CoRR (2021)
Emile Mathieu, Adam Foster, Yee Whye Teh
On Contrastive Representations of Stochastic Processes. NeurIPS (2021)
Siu Lun Chau, Jean-François Ton, Javier González, Yee Whye Teh, Dino Sejdinovic
BayesIMP: Uncertainty Quantification for Causal Data Fusion. CoRR (2021)
Ning Miao, Emile Mathieu, N. Siddharth, Yee Whye Teh, Tom Rainforth
InteL-VAEs: Adding Inductive Biases to Variational Auto-Encoders via Intermediary Latents. CoRR (2021)
Qi Wang, Vinayak Rao, Yee Whye Teh
An Exact Auxiliary Variable Gibbs Sampler for a Class of Diffusions. J. Comput. Graph. Stat. 30 (2) (2021)
Jin Xu, Hyunjik Kim, Tom Rainforth, Yee Whye Teh
Group Equivariant Subsampling. CoRR (2021)
Jonathan Schwarz, Siddhant M. Jayakumar, Razvan Pascanu, Peter E. Latham, Yee Whye Teh
Powerpropagation: A sparsity inducing weight reparameterisation. CoRR (2021)
Mrinank Sharma, Sören Mindermann, Jan Markus Brauner, Gavin Leech, Anna B. Stephenson, Tomas Gavenciak, Jan Kulveit, Yee Whye Teh, Leonid Chindelevitch, Yarin Gal
How Robust are the Estimated Effects of Nonpharmaceutical Interventions against COVID-19? NeurIPS (2020)
Jin Xu, Jean-Francois Ton, Hyunjik Kim, Adam R. Kosiorek, Yee Whye Teh
MetaFun: Meta-Learning with Iterative Functional Updates. ICML (2020)
Juho Lee, Yoonho Lee, Jungtaek Kim, Eunho Yang, Sung Ju Hwang, Yee Whye Teh
Bootstrapping neural processes. NeurIPS (2020)
Giuseppe Di Benedetto, Francois Caron, Yee Whye Teh
Non-exchangeable feature allocation models with sublinear growth of the feature sizes. AISTATS (2020)
Mrinank Sharma, Sören Mindermann, Jan Markus Brauner, Gavin Leech, Anna B. Stephenson, Tomas Gavenciak, Jan Kulveit, Yee Whye Teh, Leonid Chindelevitch, Yarin Gal
On the robustness of effectiveness estimation of nonpharmaceutical interventions against COVID-19 transmission. CoRR (2020)
Bryn Elesedy, Varun Kanade, Yee Whye Teh
Lottery Tickets in Linear Models: An Analysis of Iterative Magnitude Pruning. CoRR (2020)
Soufiane Hayou, Jean-Francois Ton, Arnaud Doucet, Yee Whye Teh
Pruning untrained neural networks: Principles and Analysis. CoRR (2020)
Joost van Amersfoort, Lewis Smith, Yee Whye Teh, Yarin Gal
Simple and Scalable Epistemic Uncertainty Estimation Using a Single Deep Deterministic Neural Network. CoRR (2020)
Adam Foster, Martin Jankowiak, Matthew O'Meara, Yee Whye Teh, Tom Rainforth
A Unified Stochastic Gradient Approach to Designing Bayesian-Optimal Experiments. AISTATS (2020)
Bobby He, Balaji Lakshminarayanan, Yee Whye Teh
Bayesian Deep Ensembles via the Neural Tangent Kernel. CoRR (2020)
Dhruva Tirumala, Alexandre Galashov, Hyeonwoo Noh, Leonard Hasenclever, Razvan Pascanu, Jonathan Schwarz, Guillaume Desjardins, Wojciech Marian Czarnecki, Arun Ahuja, Yee Whye Teh, Nicolas Heess
Behavior Priors for Efficient Reinforcement Learning. CoRR (2020)
Bobby He, Balaji Lakshminarayanan, Yee Whye Teh
Bayesian Deep Ensembles via the Neural Tangent Kernel. NeurIPS (2020)
Michalis K. Titsias, Jonathan Schwarz, Alexander G. de G. Matthews, Razvan Pascanu, Yee Whye Teh
Functional Regularisation for Continual Learning with Gaussian Processes. ICLR (2020)
Benjamin Bloem-Reddy, Yee Whye Teh
Probabilistic Symmetries and Invariant Neural Networks. J. Mach. Learn. Res. 21 (2020)
Giuseppe Di Benedetto, François Caron, Yee Whye Teh
Non-exchangeable feature allocation models with sublinear growth of the feature sizes. CoRR (2020)
Alexandre Galashov, Jakub Sygnowski, Guillaume Desjardins, Jan Humplik, Leonard Hasenclever, Rae Jeong, Yee Whye Teh, Nicolas Heess
Importance Weighted Policy Learning and Adaption. CoRR (2020)
Ari Pakman, Yueqi Wang, Yoonho Lee, Pallab Basu, Juho Lee, Yee Whye Teh, Liam Paninski
Attentive Clustering Processes. CoRR (2020)
Siddhant M. Jayakumar, Wojciech M. Czarnecki, Jacob Menick, Jonathan Schwarz, Jack W. Rae, Simon Osindero, Yee Whye Teh, Tim Harley, Razvan Pascanu
Multiplicative Interactions and Where to Find Them. ICLR (2020)
Peter Holderrieth, Michael Hutchinson, Yee Whye Teh
Equivariant Conditional Neural Processes. CoRR (2020)
Joost van Amersfoort, Lewis Smith, Yee Whye Teh, Yarin Gal
Uncertainty Estimation Using a Single Deep Deterministic Neural Network. ICML (2020)
Michael Hutchinson, Charline Le Lan, Sheheryar Zaidi, Emilien Dupont, Yee Whye Teh, Hyunjik Kim
LieTransformer: Equivariant self-attention for Lie Groups. CoRR (2020)
Umut Simsekli, Lingjiong Zhu, Yee Whye Teh, Mert Gürbüzbalaban
Fractional Underdamped Langevin Dynamics: Retargeting SGD with Momentum under Heavy-Tailed Gradient Noise. CoRR (2020)

Yee Whye Teh.css-eqlu8i{font-size:small;padding-left:4px;}

Publication Activity (10 Years)

Top Topics

Top Venues

Yee Whye Teh