Tim G. J. Rudner

Publication Activity (10 Years)

Years Active: 2018-2024
Publications (10 Years): 51

Top Topics

Variational Inference

Top Venues

Publications

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)
Yunzhen Feng, Tim G. J. Rudner, Nikolaos Tsilivis, Julia Kempe
Attacking Bayes: On the Adversarial Robustness of Bayesian Neural Networks. CoRR (2024)
Tim G. J. Rudner, Ya Shi Zhang, Andrew Gordon Wilson, Julia Kempe
Mind the GAP: Improving Robustness to Subpopulation Shifts with Group-Aware Priors. 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)
Tim G. J. Rudner, Ya Shi Zhang, Andrew Gordon Wilson, Julia Kempe
Mind the GAP: Improving Robustness to Subpopulation Shifts with Group-Aware Priors. AISTATS (2024)
Yucen Lily Li, Tim G. J. Rudner, Andrew Gordon Wilson
A Study of Bayesian Neural Network Surrogates for Bayesian Optimization. ICLR (2024)
Gunshi Gupta, Karmesh Yadav, Yarin Gal, Dhruv Batra, Zsolt Kira, Cong Lu, Tim G. J. Rudner
Pre-trained Text-to-Image Diffusion Models Are Versatile Representation Learners for Control. CoRR (2024)
L. Julian Lechuga Lopez, Tim G. J. Rudner, Farah E. Shamout
Informative Priors Improve the Reliability of Multimodal Clinical Data Classification. CoRR (2023)
Tim G. J. Rudner, Sanyam Kapoor, Shikai Qiu, Andrew Gordon Wilson
Function-Space Regularization in Neural Networks: A Probabilistic Perspective. ICML (2023)
Ravid Shwartz-Ziv, Randall Balestriero, Kenji Kawaguchi, Tim G. J. Rudner, Yann LeCun
An Information-Theoretic Perspective on Variance-Invariance-Covariance Regularization. CoRR (2023)
Nate Gruver, Samuel Stanton, Nathan C. Frey, Tim G. J. Rudner, Isidro Hötzel, Julien Lafrance-Vanasse, Arvind Rajpal, Kyunghyun Cho, Andrew Gordon Wilson
Protein Design with Guided Discrete Diffusion. NeurIPS (2023)
Shikai Qiu, Tim G. J. Rudner, Sanyam Kapoor, Andrew Gordon Wilson
Should We Learn Most Likely Functions or Parameters? NeurIPS (2023)
Shikai Qiu, Tim G. J. Rudner, Sanyam Kapoor, Andrew Gordon Wilson
Should We Learn Most Likely Functions or Parameters? CoRR (2023)
Nate Gruver, Samuel Stanton, Nathan C. Frey, Tim G. J. Rudner, Isidro Hötzel, Julien Lafrance-Vanasse, Arvind Rajpal, Kyunghyun Cho, Andrew Gordon Wilson
Protein Design with Guided Discrete Diffusion. CoRR (2023)
Samuel Kessler, Adam D. Cobb, Tim G. J. Rudner, Stefan Zohren, Stephen J. Roberts
On Sequential Bayesian Inference for Continual Learning. CoRR (2023)
Sanae Lotfi, Marc Finzi, Yilun Kuang, Tim G. J. Rudner, Micah Goldblum, Andrew Gordon Wilson
Non-Vacuous Generalization Bounds for Large Language Models. CoRR (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)
Tim G. J. Rudner, Zonghao Chen, Yee Whye Teh, Yarin Gal
Tractable Function-Space Variational Inference in Bayesian Neural Networks. CoRR (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)
Ravid Shwartz-Ziv, Randall Balestriero, Kenji Kawaguchi, Tim G. J. Rudner, Yann LeCun
An Information Theory Perspective on Variance-Invariance-Covariance Regularization. NeurIPS (2023)
Samuel Kessler, Adam D. Cobb, Tim G. J. Rudner, Stefan Zohren, Stephen J. Roberts
On Sequential Bayesian Inference for Continual Learning. Entropy 25 (6) (2023)
Gunshi Gupta, Tim G. J. Rudner, Rowan Thomas McAllister, Adrien Gaidon, Yarin Gal
Can Active Sampling Reduce Causal Confusion in Offline Reinforcement Learning? CoRR (2023)
Ying Wang, Tim G. J. Rudner, Andrew Gordon Wilson
Visual Explanations of Image-Text Representations via Multi-Modal Information Bottleneck Attribution. NeurIPS (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)
Yucen Lily Li, Tim G. J. Rudner, Andrew Gordon Wilson
A Study of Bayesian Neural Network Surrogates for Bayesian Optimization. CoRR (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)
Ying Wang, Tim G. J. Rudner, Andrew Gordon Wilson
Visual Explanations of Image-Text Representations via Multi-Modal Information Bottleneck Attribution. CoRR (2023)
Tim G. J. Rudner, Sanyam Kapoor, Shikai Qiu, Andrew Gordon Wilson
Function-Space Regularization in Neural Networks: A Probabilistic Perspective. CoRR (2023)
Gunshi Gupta, Tim G. J. Rudner, Rowan Thomas McAllister, Adrien Gaidon, Yarin Gal
Can Active Sampling Reduce Causal Confusion in Offline Reinforcement Learning? CLeaR (2023)
Tim G. J. Rudner, Freddie Bickford Smith, Qixuan Feng, Yee Whye Teh, Yarin Gal
Continual Learning via Sequential Function-Space Variational Inference. ICML (2022)
Neil Band, Tim G. J. Rudner, Qixuan Feng, Angelos Filos, Zachary Nado, Michael W. Dusenberry, Ghassen Jerfel, Dustin Tran, Yarin Gal
Benchmarking Bayesian Deep Learning on Diabetic Retinopathy Detection Tasks. CoRR (2022)
Dustin Tran, Jeremiah Liu, Michael W. Dusenberry, Du Phan, Mark Collier, Jie Ren, Kehang Han, Zi Wang, Zelda Mariet, Huiyi Hu, Neil Band, Tim G. J. Rudner, Karan Singhal, Zachary Nado, Joost van Amersfoort, Andreas Kirsch, Rodolphe Jenatton, Nithum Thain, Honglin Yuan, Kelly Buchanan, Kevin Murphy, D. Sculley, Yarin Gal, Zoubin Ghahramani, Jasper Snoek, Balaji Lakshminarayanan
Plex: Towards Reliability using Pretrained Large Model Extensions. 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)
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)
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)
Tim G. J. Rudner, Oscar Key, Yarin Gal, Tom Rainforth
On Signal-to-Noise Ratio Issues in Variational Inference for Deep Gaussian Processes. ICML (2021)
Neil Band, Tim G. J. Rudner, Qixuan Feng, Angelos Filos, Zachary Nado, Mike Dusenberry, Ghassen Jerfel, Dustin Tran, Yarin Gal
Benchmarking Bayesian Deep Learning on Diabetic Retinopathy Detection Tasks. NeurIPS Datasets and Benchmarks (2021)
Tim G. J. Rudner, Vitchyr H. Pong, Rowan McAllister, Yarin Gal, Sergey Levine
Outcome-Driven Reinforcement Learning via Variational Inference. CoRR (2021)
Tim G. J. Rudner, Vitchyr Pong, Rowan McAllister, Yarin Gal, Sergey Levine
Outcome-Driven Reinforcement Learning via Variational Inference. NeurIPS (2021)
Zachary Nado, Neil Band, Mark Collier, Josip Djolonga, Michael W. Dusenberry, Sebastian Farquhar, Angelos Filos, Marton Havasi, Rodolphe Jenatton, Ghassen Jerfel, Jeremiah Liu, Zelda Mariet, Jeremy Nixon, Shreyas Padhy, Jie Ren, Tim G. J. Rudner, Yeming Wen, Florian Wenzel, Kevin Murphy, D. Sculley, Balaji Lakshminarayanan, Jasper Snoek, Yarin Gal, Dustin Tran
Uncertainty Baselines: Benchmarks for Uncertainty & Robustness in Deep Learning. CoRR (2021)
Tim G. J. Rudner, Oscar Key, Yarin Gal, Tom Rainforth
On Signal-to-Noise Ratio Issues in Variational Inference for Deep Gaussian Processes. CoRR (2020)
Tim G. J. Rudner, Dino Sejdinovic, Yarin Gal
Inter-domain Deep Gaussian Processes. CoRR (2020)
Tim G. J. Rudner, Dino Sejdinovic, Yarin Gal
Inter-domain Deep Gaussian Processes. ICML (2020)
Tim G. J. Rudner, Marc Rußwurm, Jakub Fil, Ramona Pelich, Benjamin Bischke, Veronika Kopacková, Piotr Bilinski
Multi3Net: Segmenting Flooded Buildings via Fusion of Multiresolution, Multisensor, and Multitemporal Satellite Imagery. AAAI (2019)
Mikayel Samvelyan, Tabish Rashid, Christian Schröder de Witt, Gregory Farquhar, Nantas Nardelli, Tim G. J. Rudner, Chia-Man Hung, Philip H. S. Torr, Jakob N. Foerster, Shimon Whiteson
The StarCraft Multi-Agent Challenge. AAMAS (2019)
Mikayel Samvelyan, Tabish Rashid, Christian Schröder de Witt, Gregory Farquhar, Nantas Nardelli, Tim G. J. Rudner, Chia-Man Hung, Philip H. S. Torr, Jakob N. Foerster, Shimon Whiteson
The StarCraft Multi-Agent Challenge. CoRR (2019)
Angelos Filos, Sebastian Farquhar, Aidan N. Gomez, Tim G. J. Rudner, Zachary Kenton, Lewis Smith, Milad Alizadeh, Arnoud de Kroon, Yarin Gal
A Systematic Comparison of Bayesian Deep Learning Robustness in Diabetic Retinopathy Tasks. CoRR (2019)
Matthew Fellows, Anuj Mahajan, Tim G. J. Rudner, Shimon Whiteson
VIREL: A Variational Inference Framework for Reinforcement Learning. NeurIPS (2019)
Matthew Fellows, Anuj Mahajan, Tim G. J. Rudner, Shimon Whiteson
VIREL: A Variational Inference Framework for Reinforcement Learning. CoRR (2018)
Tim G. J. Rudner, Marc Rußwurm, Jakub Fil, Ramona Pelich, Benjamin Bischke, Veronika Kopacková, Piotr Bilinski
Net: Segmenting Flooded Buildings via Fusion of Multiresolution, Multisensor, and Multitemporal Satellite Imagery. CoRR (2018)