Tim G. J. Rudner
University of Oxford
H-index: 13
Europe-United Kingdom
Top articles of Tim G. J. Rudner
Title | Journal | Author(s) | Publication Date |
|---|---|---|---|
Position Paper: Bayesian Deep Learning in the Age of Large-Scale AI | Theodore Papamarkou Maria Skoularidou Konstantina Palla Laurence Aitchison Julyan Arbel | 2024/2 | |
Pre-trained Text-to-Image Diffusion Models Are Versatile Representation Learners for Control | Gunshi Gupta Karmesh Yadav Yarin Gal Dhruv Batra Zsolt Kira | 2024/5 | |
Mind the GAP: Improving Robustness to Subpopulation Shifts with Group-Aware Priors | Tim GJ Rudner Ya Shi Zhang Andrew Gordon Wilson Julia Kempe | 2024/5 | |
A Study of Bayesian Neural Network Surrogates for Bayesian Optimization | Yucen Lily Li Tim GJ Rudner Andrew Gordon Wilson | 2024/5 | |
Attacking Bayes: On the Adversarial Robustness of Bayesian Neural Networks | arXiv preprint arXiv:2404.19640 | Yunzhen Feng Tim GJ Rudner Nikolaos Tsilivis Julia Kempe | 2024/4/27 |
Attacking Bayes: Are Bayesian Neural Networks Inherently Robust? | Yunzhen Feng Tim GJ Rudner Nikolaos Tsilivis Julia Kempe | 2023/6 | |
An Information-Theoretic Perspective on Variance-Invariance-Covariance Regularization | Ravid Shwartz-Ziv Randall Balestriero Kenji Kawaguchi Tim GJ Rudner Yann LeCun | 2023/12 | |
On Sequential Bayesian Inference for Continual Learning | Entropy | Samuel Kessler Adam Cobb Tim GJ Rudner Stefan Zohren Stephen J Roberts | 2023/6 |
Informative Priors Improve the Reliability of Multimodal Clinical Data Classification | arXiv preprint arXiv:2312.00794 | L Lopez Tim GJ Rudner Farah E Shamout | 2023/11/17 |
Non-vacuous Generalization Bounds for Large Language Models | arXiv preprint arXiv:2312.17173 | Sanae Lotfi Marc Finzi Yilun Kuang Tim GJ Rudner Micah Goldblum | 2023/12/28 |
Can Active Sampling Reduce Causal Confusion in Offline Reinforcement Learning? | Gunshi Gupta Tim GJ Rudner Rowan Thomas McAllister Adrien Gaidon Yarin Gal | 2023/4 | |
Function-Space Regularization in Neural Networks: A Probabilistic Perspective | Tim GJ Rudner Sanyam Kapoor Shikai Qiu Andrew Gordon Wilson | 2023/7 | |
Should We Learn Most Likely Functions or Parameters? | Shikai Qiu* Tim GJ Rudner* Sanyam Kapoor* Andrew Gordon Wilson | 2023/12 | |
Drug Discovery under Covariate Shift with Domain-Informed Prior Distributions over Functions | Leo Klarner Tim GJ Rudner Michael Reutlinger Torsten Schindler Garrett M Morris | 2023/7 | |
Visual Explanations of Image-Text Representations via Multi-Modal Information Bottleneck Attribution | Ying Wang* Tim GJ Rudner* Andrew Gordon Wilson | 2023/12 | |
Challenges and Opportunities in Offline Reinforcement Learning from Visual Observations | Transactions on Machine Learning Research (TMLR) | Cong Lu Philip J Ball Tim GJ Rudner Jack Parker-Holder Michael A Osborne | 2023/7 |
Protein Design with Guided Discrete Diffusion | Nate Gruver Samuel Stanton Nathan C Frey Tim GJ Rudner Isidro Hotzel | 2023/12 | |
Continual Learning via Sequential Function-Space Variational Inference | Proceedings of the International Conference on Machine Learning (ICML) | Tim GJ Rudner Freddie Bickford Smith Qixuan Feng Yee Whye Teh Yarin Gal | 2022 |
A Neural Tangent Kernel Perspective on Function-Space Regularization in Neural Networks | Zonghao Chen Xupeng Shi Tim GJ Rudner Qixuan Feng Weizhong Zhang | 2022/12 | |
Tractable Function-Space Variational Inference in Bayesian Neural Networks | Advances in Neural Information Processing Systems (NeurIPS) | Tim GJ Rudner Zonghao Chen Yee Whye Teh Yarin Gal | 2022/12 |
Co-Authors
