Multi3Net: Segmenting Flooded Buildings via Fusion of Multiresolution, Multisensor, and Multitemporal Satellite Imagery. - Tim G. J. Rudner; Marc Rußwurm; Jakub Fil; Ramona Pelich; Benjamin Bischke; Veronika Kopacková; Piotr Bilinski (CoRR, 2018)

Multi3Net: Segmenting Flooded Buildings via Fusion of Multiresolution, Multisensor, and Multitemporal Satellite Imagery. - Tim G. J. Rudner; Marc Rußwurm; Jakub Fil; Ramona Pelich; Benjamin Bischke; Veronika Kopacková; Piotr Bilinski (AAAI, 2019)

The StarCraft Multi-Agent Challenge. - 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 (AAMAS, 2019)

VIREL: A Variational Inference Framework for Reinforcement Learning. - Matthew Fellows; Anuj Mahajan; Tim G. J. Rudner; Shimon Whiteson (NeurIPS, 2019)

A Systematic Comparison of Bayesian Deep Learning Robustness in Diabetic Retinopathy Tasks. - Angelos Filos; Sebastian Farquhar; Aidan N. Gomez; Tim G. J. Rudner; Zachary Kenton; Lewis Smith; Milad Alizadeh; Arnoud de Kroon; Yarin Gal (CoRR, 2019)

Inter-domain Deep Gaussian Processes. - Tim G. J. Rudner; Dino Sejdinovic; Yarin Gal (ICML, 2020)

On Signal-to-Noise Ratio Issues in Variational Inference for Deep Gaussian Processes. - Tim G. J. Rudner; Oscar Key; Yarin Gal; Tom Rainforth (ICML, 2021)

Benchmarking Bayesian Deep Learning on Diabetic Retinopathy Detection Tasks. - Neil Band; Tim G. J. Rudner; Qixuan Feng; Angelos Filos; Zachary Nado; Mike Dusenberry; Ghassen Jerfel; Dustin Tran; Yarin Gal (NeurIPS Datasets and Benchmarks, 2021)

On Pathologies in KL-Regularized Reinforcement Learning from Expert Demonstrations. - Tim G. J. Rudner; Cong Lu; Michael A. Osborne; Yarin Gal; Yee Whye Teh (NeurIPS, 2021)

Outcome-Driven Reinforcement Learning via Variational Inference. - Tim G. J. Rudner; Vitchyr Pong; Rowan McAllister; Yarin Gal; Sergey Levine (NeurIPS, 2021)

Uncertainty Baselines: Benchmarks for Uncertainty & Robustness in Deep Learning. - 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; et al. (CoRR, 2021)

Continual Learning via Sequential Function-Space Variational Inference. - Tim G. J. Rudner; Freddie Bickford Smith; Qixuan Feng; Yee Whye Teh; Yarin Gal (ICML, 2022)

Tractable Function-Space Variational Inference in Bayesian Neural Networks. - Tim G. J. Rudner; Zonghao Chen; Yee Whye Teh; Yarin Gal (NeurIPS, 2022)

Plex: Towards Reliability using Pretrained Large Model Extensions. - Dustin Tran; Jeremiah Z. Liu; Michael W. Dusenberry; Du Phan; Mark Collier; Jie Ren 006; Kehang Han; Zi Wang; Zelda Mariet; Huiyi Hu; Neil Band; Tim G. J. Rudner; et al. (CoRR, 2022)

Can Active Sampling Reduce Causal Confusion in Offline Reinforcement Learning? - Gunshi Gupta; Tim G. J. Rudner; Rowan Thomas McAllister; Adrien Gaidon; Yarin Gal (CLeaR, 2023)

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 (ICML, 2023)

Function-Space Regularization in Neural Networks: A Probabilistic Perspective. - Tim G. J. Rudner; Sanyam Kapoor; Shikai Qiu; Andrew Gordon Wilson (ICML, 2023)

Protein Design with Guided Discrete Diffusion. - Nate Gruver; Samuel Stanton; Nathan C. Frey; Tim G. J. Rudner; Isidro Hötzel; Julien Lafrance-Vanasse; Arvind Rajpal; Kyunghyun Cho; Andrew Gordon Wilson (NeurIPS, 2023)

Should We Learn Most Likely Functions or Parameters? - Shikai Qiu; Tim G. J. Rudner; Sanyam Kapoor; Andrew Gordon Wilson (NeurIPS, 2023)

An Information Theory Perspective on Variance-Invariance-Covariance Regularization. - Ravid Shwartz-Ziv; Randall Balestriero; Kenji Kawaguchi; Tim G. J. Rudner; Yann LeCun (NeurIPS, 2023)

Visual Explanations of Image-Text Representations via Multi-Modal Information Bottleneck Attribution. - Ying Wang 063; Tim G. J. Rudner; Andrew Gordon Wilson (NeurIPS, 2023)

An Information-Theoretic Perspective on Variance-Invariance-Covariance Regularization. - Ravid Shwartz-Ziv; Randall Balestriero; Kenji Kawaguchi; Tim G. J. Rudner; Yann LeCun (CoRR, 2023)

Informative Priors Improve the Reliability of Multimodal Clinical Data Classification. - L. Julian Lechuga Lopez; Tim G. J. Rudner; Farah E. Shamout (CoRR, 2023)

Challenges and Opportunities in Offline Reinforcement Learning from Visual Observations. - Cong Lu; Philip J. Ball; Tim G. J. Rudner; Jack Parker-Holder; Michael A. Osborne; Yee Whye Teh (Trans. Mach. Learn. Res., 2023)

On Sequential Bayesian Inference for Continual Learning. - Samuel Kessler; Adam D. Cobb; Tim G. J. Rudner; Stefan Zohren; Stephen J. Roberts (Entropy, 2023)

Not Oracles of the Battlefield: Safety Considerations for AI-Based Military Decision Support Systems. - Emelia Probasco; Matthew Burtell; Helen Toner; Tim G. J. Rudner (AIES (1), 2024)

Mind the GAP: Improving Robustness to Subpopulation Shifts with Group-Aware Priors. - Tim G. J. Rudner; Ya Shi Zhang; Andrew Gordon Wilson; Julia Kempe (AISTATS, 2024)

A Study of Bayesian Neural Network Surrogates for Bayesian Optimization. - Yucen Lily Li; Tim G. J. Rudner; Andrew Gordon Wilson (ICLR, 2024)

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 (ICML, 2024)

Non-Vacuous Generalization Bounds for Large Language Models. - Sanae Lotfi; Marc Anton Finzi; Yilun Kuang; Tim G. J. Rudner; Micah Goldblum; Andrew Gordon Wilson (ICML, 2024)

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; José Miguel Hernández-Lobato; Aliaksandr Hubin; Alexander Immer; et al. (ICML, 2024)

Pre-trained Text-to-Image Diffusion Models Are Versatile Representation Learners for Control. - Gunshi Gupta; Karmesh Yadav; Yarin Gal; Dhruv Batra; Zsolt Kira; Cong Lu; Tim G. J. Rudner (NeurIPS, 2024)

Position Paper: Bayesian Deep Learning 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; José Miguel Hernández-Lobato; Aliaksandr Hubin; Alexander Immer; et al. (CoRR, 2024)

Geometry-Aware Generative Autoencoders for Warped Riemannian Metric Learning and Generative Modeling on Data Manifolds. - Xingzhi Sun 003; Danqi Liao; Kincaid MacDonald; Yanlei Zhang; Chen Liu 020; Guillaume Huguet; Guy Wolf; Ian Adelstein; Tim G. J. Rudner; Smita Krishnaswamy (CoRR, 2024)

A Monte Carlo Framework for Calibrated Uncertainty Estimation in Sequence Prediction. - Qidong Yang; Weicheng Zhu; Joseph Keslin; Laure Zanna; Tim G. J. Rudner; Carlos Fernandez-Granda (CoRR, 2024)

Exploring the Manifold of Neural Networks Using Diffusion Geometry. - Elliott Abel; Peyton Crevasse; Yvan Grinspan; Selma Mazioud; Folu Ogundipe; Kristof Reimann; Ellie Schueler; Andrew J. Steindl; Ellen Zhang; Dhananjay Bhaskar; Siddharth Viswanath; Yanlei Zhang; et al. (CoRR, 2024)

Attacking Bayes: On the Adversarial Robustness of Bayesian Neural Networks. - Yunzhen Feng; Tim G. J. Rudner; Nikolaos Tsilivis 002; Julia Kempe (Trans. Mach. Learn. Res., 2024)

Improving Pre-trained Self-Supervised Embeddings Through Effective Entropy Maximization. - Deep Chakraborty; Yann LeCun; Tim G. J. Rudner; Erik G. Learned-Miller (AISTATS, 2025)

Fine-Tuning with Uncertainty-Aware Priors Makes Vision and Language Foundation Models More Reliable. - Tim G. J. Rudner; Xiang Pan; Yucen Lily Li; Ravid Shwartz-Ziv; Andrew Gordon Wilson (AISTATS, 2025)

Geometry-Aware Generative Autoencoders for Warped Riemannian Metric Learning and Generative Modeling on Data Manifolds. - Xingzhi Sun 003; Danqi Liao; Kincaid MacDonald; Yanlei Zhang; Guillaume Huguet; Guy Wolf; Ian Adelstein; Tim G. J. Rudner; Smita Krishnaswamy (AISTATS, 2025)

SCIURus: Shared Circuits for Interpretable Uncertainty Representations in Language Models. - Carter Teplica; Yixin Liu 003; Arman Cohan; Tim G. J. Rudner (NAACL (Long Papers), 2025)

Can Transformers Learn Full Bayesian Inference in Context? - Arik Reuter; Tim G. J. Rudner; Vincent Fortuin; David Rügamer (CoRR, 2025)

Learning from Reward-Free Offline Data: A Case for Planning with Latent Dynamics Models. - Vlad Sobal; Wancong Zhang; Kynghyun Cho; Randall Balestriero; Tim G. J. Rudner; Yann LeCun (CoRR, 2025)

Insertion Language Models: Sequence Generation with Arbitrary-Position Insertions. - Dhruvesh Patel; Aishwarya Sahoo; Avinash Amballa; Tahira Naseem; Tim G. J. Rudner; Andrew McCallum (CoRR, 2025)

MetaFaith: Faithful Natural Language Uncertainty Expression in LLMs. - Gabrielle Kaili-May Liu; Gal Yona; Avi Caciularu; Idan Szpektor; Tim G. J. Rudner; Arman Cohan (CoRR, 2025)

Out-of-Distribution Detection Methods Answer the Wrong Questions. - Yucen Lily Li; Daohan Lu; Polina Kirichenko; Shikai Qiu; Tim G. J. Rudner; C. Bayan Bruss; Andrew Gordon Wilson (CoRR, 2025)