Tim G. J. Rudner

According to our database, Tim G. J. Rudner authored at least 46 papers between 2018 and 2025.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

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, July, 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, May, 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, May, 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, February, 2025

Can Transformers Learn Full Bayesian Inference in Context?

Arik Reuter, Tim G. J. Rudner, Vincent Fortuin, David Rügamer

CoRR, January, 2025

SCIURus: Shared Circuits for Interpretable Uncertainty Representations in Language Models.

Carter Teplica, Yixin Liu, Arman Cohan, Tim G. J. Rudner

Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies, 2025

Geometry-Aware Generative Autoencoders for Warped Riemannian Metric Learning and Generative Modeling on Data Manifolds.

Xingzhi Sun, Danqi Liao, Kincaid MacDonald, Yanlei Zhang, Guillaume Huguet, Guy Wolf, Ian Adelstein, Tim G. J. Rudner, Smita Krishnaswamy

Proceedings of the International Conference on Artificial Intelligence and Statistics, 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

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2025

Improving Pre-trained Self-Supervised Embeddings Through Effective Entropy Maximization.

Deep Chakraborty, Yann LeCun, Tim G. J. Rudner, Erik G. Learned-Miller

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2025

2024

Attacking Bayes: On the Adversarial Robustness of Bayesian Neural Networks.

Yunzhen Feng, Tim G. J. Rudner, Nikolaos Tsilivis, Julia Kempe

Trans. Mach. Learn. Res., 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, Tim G. J. Rudner, Ian Adelstein, 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

Geometry-Aware Generative Autoencoders for Warped Riemannian Metric Learning and Generative Modeling on Data Manifolds.

Xingzhi Sun, Danqi Liao, Kincaid MacDonald, Yanlei Zhang, Chen Liu, Guillaume Huguet, Guy Wolf, Ian Adelstein, Tim G. J. Rudner, Smita Krishnaswamy

CoRR, 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, 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

CoRR, 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

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 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, 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

Proceedings of the Forty-first International Conference on Machine Learning, 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

Proceedings of the Forty-first International Conference on Machine Learning, 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

Proceedings of the Forty-first International Conference on Machine Learning, 2024

A Study of Bayesian Neural Network Surrogates for Bayesian Optimization.

Yucen Lily Li, Tim G. J. Rudner, Andrew Gordon Wilson

Proceedings of the Twelfth International Conference on Learning Representations, 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

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

Not Oracles of the Battlefield: Safety Considerations for AI-Based Military Decision Support Systems.

Emelia Probasco, Matthew Burtell, Helen Toner, Tim G. J. Rudner

Proceedings of the Seventh AAAI/ACM Conference on AI, Ethics, and Society (AIES-24) - Full Archival Papers, October 21-23, 2024, San Jose, California, USA, 2024

2023

On Sequential Bayesian Inference for Continual Learning.

Samuel Kessler, Adam D. Cobb, Tim G. J. Rudner, Stefan Zohren, Stephen J. Roberts

Entropy, June, 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

Informative Priors Improve the Reliability of Multimodal Clinical Data Classification.

L. Julian Lechuga Lopez, Tim G. J. Rudner, Farah E. Shamout

CoRR, 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

Visual Explanations of Image-Text Representations via Multi-Modal Information Bottleneck Attribution.

Ying Wang, Tim G. J. Rudner, Andrew Gordon Wilson

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

An Information Theory Perspective on Variance-Invariance-Covariance Regularization.

Ravid Shwartz-Ziv, Randall Balestriero, Kenji Kawaguchi, Tim G. J. Rudner, Yann LeCun

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Should We Learn Most Likely Functions or Parameters?

Shikai Qiu, Tim G. J. Rudner, Sanyam Kapoor, Andrew Gordon Wilson

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 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

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Function-Space Regularization in Neural Networks: A Probabilistic Perspective.

Tim G. J. Rudner, Sanyam Kapoor, Shikai Qiu, Andrew Gordon Wilson

Proceedings of the International Conference on Machine Learning, 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

Proceedings of the International Conference on Machine Learning, 2023

Can Active Sampling Reduce Causal Confusion in Offline Reinforcement Learning?

Gunshi Gupta, Tim G. J. Rudner, Rowan Thomas McAllister, Adrien Gaidon, Yarin Gal

Proceedings of the Conference on Causal Learning and Reasoning, 2023

2022

Plex: Towards Reliability using Pretrained Large Model Extensions.

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

CoRR, 2022

Tractable Function-Space Variational Inference in Bayesian Neural Networks.

Tim G. J. Rudner, Zonghao Chen, Yee Whye Teh, Yarin Gal

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Continual Learning via Sequential Function-Space Variational Inference.

Tim G. J. Rudner, Freddie Bickford Smith, Qixuan Feng, Yee Whye Teh, Yarin Gal

Proceedings of the International Conference on Machine Learning, 2022

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, 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

CoRR, 2021

Outcome-Driven Reinforcement Learning via Variational Inference.

Tim G. J. Rudner, Vitchyr Pong, Rowan McAllister, Yarin Gal, Sergey Levine

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 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

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 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

Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1, 2021

On Signal-to-Noise Ratio Issues in Variational Inference for Deep Gaussian Processes.

Tim G. J. Rudner, Oscar Key, Yarin Gal, Tom Rainforth

Proceedings of the 38th International Conference on Machine Learning, 2021

2020

Inter-domain Deep Gaussian Processes.

Tim G. J. Rudner, Dino Sejdinovic, Yarin Gal

Proceedings of the 37th International Conference on Machine Learning, 2020

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

VIREL: A Variational Inference Framework for Reinforcement Learning.

Matthew Fellows, Anuj Mahajan, Tim G. J. Rudner, Shimon Whiteson

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 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

Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems, 2019

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

Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence, 2019

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

CoRR, 2018