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2020 – today
- 2024
[c22]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: 127-135- [c21]Yucen Lily Li, Tim G. J. Rudner, Andrew Gordon Wilson:
A Study of Bayesian Neural Network Surrogates for Bayesian Optimization. ICLR 2024 - [c20]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. ICML 2024 - [c19]Sanae Lotfi, Marc Anton Finzi, Yilun Kuang, Tim G. J. Rudner, Micah Goldblum, Andrew Gordon Wilson:
Non-Vacuous Generalization Bounds for Large Language Models. ICML 2024 - [c18]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: Bayesian Deep Learning is Needed in the Age of Large-Scale AI. ICML 2024 - [i32]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 abs/2402.00809 (2024) - [i31]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 abs/2403.09869 (2024) - [i30]Yunzhen Feng, Tim G. J. Rudner, Nikolaos Tsilivis, Julia Kempe:
Attacking Bayes: On the Adversarial Robustness of Bayesian Neural Networks. CoRR abs/2404.19640 (2024) - [i29]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 abs/2405.05852 (2024) - [i28]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 abs/2407.11942 (2024) - [i27]Xingzhi Sun, Danqi Liao, Kincaid MacDonald, Yanlei Zhang, Chen Liu, Guillaume Huguet, Guy Wolf, Ian Adelstein, Tim G. J. Rudner, Smita Krishnaswamy:
Geometry-Aware Generative Autoencoders for Warped Riemannian Metric Learning and Generative Modeling on Data Manifolds. CoRR abs/2410.12779 (2024) - [i26]Qidong Yang, Weicheng Zhu, Joseph Keslin, Laure Zanna, Tim G. J. Rudner, Carlos Fernandez-Granda:
A Monte Carlo Framework for Calibrated Uncertainty Estimation in Sequence Prediction. CoRR abs/2410.23272 (2024) - 2023
- [j2]Samuel Kessler
, Adam D. Cobb, Tim G. J. Rudner, Stefan Zohren, Stephen J. Roberts:
On Sequential Bayesian Inference for Continual Learning. Entropy 25(6): 884 (2023) - [j1]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) - [c17]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: 386-407 - [c16]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: 17176-17197 - [c15]Tim G. J. Rudner, Sanyam Kapoor, Shikai Qiu, Andrew Gordon Wilson:
Function-Space Regularization in Neural Networks: A Probabilistic Perspective. ICML 2023: 29275-29290 - [c14]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 - [c13]Shikai Qiu, Tim G. J. Rudner, Sanyam Kapoor, Andrew Gordon Wilson:
Should We Learn Most Likely Functions or Parameters? NeurIPS 2023 - [c12]Ravid Shwartz-Ziv, Randall Balestriero, Kenji Kawaguchi, Tim G. J. Rudner, Yann LeCun:
An Information Theory Perspective on Variance-Invariance-Covariance Regularization. NeurIPS 2023 - [c11]Ying Wang, Tim G. J. Rudner, Andrew Gordon Wilson:
Visual Explanations of Image-Text Representations via Multi-Modal Information Bottleneck Attribution. NeurIPS 2023 - [i25]Samuel Kessler, Adam D. Cobb, Tim G. J. Rudner, Stefan Zohren, Stephen J. Roberts:
On Sequential Bayesian Inference for Continual Learning. CoRR abs/2301.01828 (2023) - [i24]Ravid Shwartz-Ziv, Randall Balestriero, Kenji Kawaguchi, Tim G. J. Rudner, Yann LeCun:
An Information-Theoretic Perspective on Variance-Invariance-Covariance Regularization. CoRR abs/2303.00633 (2023) - [i23]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 abs/2305.20009 (2023) - [i22]Yucen Lily Li, Tim G. J. Rudner, Andrew Gordon Wilson:
A Study of Bayesian Neural Network Surrogates for Bayesian Optimization. CoRR abs/2305.20028 (2023) - [i21]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 abs/2307.15073 (2023) - [i20]Shikai Qiu, Tim G. J. Rudner, Sanyam Kapoor, Andrew Gordon Wilson:
Should We Learn Most Likely Functions or Parameters? CoRR abs/2311.15990 (2023) - [i19]L. Julian Lechuga Lopez, Tim G. J. Rudner, Farah E. Shamout:
Informative Priors Improve the Reliability of Multimodal Clinical Data Classification. CoRR abs/2312.00794 (2023) - [i18]Tim G. J. Rudner, Sanyam Kapoor, Shikai Qiu, Andrew Gordon Wilson:
Function-Space Regularization in Neural Networks: A Probabilistic Perspective. CoRR abs/2312.17162 (2023) - [i17]Gunshi Gupta, Tim G. J. Rudner, Rowan Thomas McAllister, Adrien Gaidon, Yarin Gal:
Can Active Sampling Reduce Causal Confusion in Offline Reinforcement Learning? CoRR abs/2312.17168 (2023) - [i16]Sanae Lotfi, Marc Finzi, Yilun Kuang, Tim G. J. Rudner, Micah Goldblum, Andrew Gordon Wilson:
Non-Vacuous Generalization Bounds for Large Language Models. CoRR abs/2312.17173 (2023) - [i15]Ying Wang, Tim G. J. Rudner, Andrew Gordon Wilson:
Visual Explanations of Image-Text Representations via Multi-Modal Information Bottleneck Attribution. CoRR abs/2312.17174 (2023) - [i14]Tim G. J. Rudner, Zonghao Chen, Yee Whye Teh, Yarin Gal:
Tractable Function-Space Variational Inference in Bayesian Neural Networks. CoRR abs/2312.17199 (2023) - [i13]Tim G. J. Rudner, Freddie Bickford Smith, Qixuan Feng, Yee Whye Teh, Yarin Gal:
Continual Learning via Sequential Function-Space Variational Inference. CoRR abs/2312.17210 (2023) - 2022
- [c10]Tim G. J. Rudner, Freddie Bickford Smith, Qixuan Feng, Yee Whye Teh, Yarin Gal:
Continual Learning via Sequential Function-Space Variational Inference. ICML 2022: 18871-18887 - [c9]Tim G. J. Rudner, Zonghao Chen, Yee Whye Teh, Yarin Gal:
Tractable Function-Space Variational Inference in Bayesian Neural Networks. NeurIPS 2022 - [i12]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 abs/2206.04779 (2022) - [i11]Dustin Tran, Jeremiah Z. 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 abs/2207.07411 (2022) - [i10]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 abs/2211.12717 (2022) - [i9]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 abs/2212.13936 (2022) - 2021
- [c8]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: 9148-9156 - [c7]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 - [c6]Tim G. J. Rudner, Vitchyr Pong, Rowan McAllister, Yarin Gal, Sergey Levine:
Outcome-Driven Reinforcement Learning via Variational Inference. NeurIPS 2021: 13045-13058 - [c5]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: 28376-28389 - [i8]Tim G. J. Rudner, Vitchyr H. Pong, Rowan McAllister, Yarin Gal, Sergey Levine:
Outcome-Driven Reinforcement Learning via Variational Inference. CoRR abs/2104.10190 (2021) - [i7]Zachary Nado, Neil Band, Mark Collier, Josip Djolonga, Michael W. Dusenberry, Sebastian Farquhar, Angelos Filos, Marton Havasi, Rodolphe Jenatton, Ghassen Jerfel, Jeremiah Z. 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 abs/2106.04015 (2021) - 2020
- [c4]Tim G. J. Rudner, Dino Sejdinovic, Yarin Gal:
Inter-domain Deep Gaussian Processes. ICML 2020: 8286-8294 - [i6]Tim G. J. Rudner, Dino Sejdinovic, Yarin Gal:
Inter-domain Deep Gaussian Processes. CoRR abs/2011.00415 (2020) - [i5]Tim G. J. Rudner, Oscar Key, Yarin Gal, Tom Rainforth:
On Signal-to-Noise Ratio Issues in Variational Inference for Deep Gaussian Processes. CoRR abs/2011.00515 (2020)
2010 – 2019
- 2019
- [c3]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: 702-709 - [c2]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: 2186-2188 - [c1]Matthew Fellows, Anuj Mahajan, Tim G. J. Rudner, Shimon Whiteson:
VIREL: A Variational Inference Framework for Reinforcement Learning. NeurIPS 2019: 7120-7134 - [i4]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 abs/1902.04043 (2019) - [i3]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 abs/1912.10481 (2019) - 2018
- [i2]Matthew Fellows, Anuj Mahajan, Tim G. J. Rudner, Shimon Whiteson:
VIREL: A Variational Inference Framework for Reinforcement Learning. CoRR abs/1811.01132 (2018) - [i1]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. CoRR abs/1812.01756 (2018)
Coauthor Index
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