Tim G. J. Rudner

80 papers A* 21A 5Journal 48Unranked 6

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	An Empirical Analysis of Calibration and Selective Prediction in Multimodal Clinical Condition Classification. CoRR Leopoldo Julian Lechuga López, Farah E. Shamout, Tim G. J. Rudner
2026	J	`jnl`	Data-Driven Priors for Uncertainty-Aware Deterioration Risk Prediction with Multimodal Data. CoRR L. Julián Lechuga López, Tim G. J. Rudner, Farah E. Shamout
2026	J	`jnl`	Radial-VCReg: More Informative Representation Learning Through Radial Gaussianization. CoRR Yilun Kuang, Yash Dagade, Deep Chakraborty, Erik G. Learned-Miller, Randall Balestriero, Tim G. J. Rudner, Yann LeCun
2026	J	`jnl`	Rectified LpJEPA: Joint-Embedding Predictive Architectures with Sparse and Maximum-Entropy Representations. CoRR Yilun Kuang, Yash Dagade, Tim G. J. Rudner, Randall Balestriero, Yann LeCun
2026	J	`jnl`	Temporal Straightening for Latent Planning. CoRR Ying Wang, Oumayma Bounou, Gaoyue Zhou, Randall Balestriero, Tim G. J. Rudner, Yann LeCun, Mengye Ren
2025	A*	`conf`	Can Transformers Learn Full Bayesian Inference in Context? ICML Arik Reuter, Tim G. J. Rudner, Vincent Fortuin, David Rügamer
2025	J	`jnl`	Can Transformers Learn Full Bayesian Inference in Context? CoRR Arik Reuter, Tim G. J. Rudner, Vincent Fortuin, David Rügamer
2025	J	`jnl`	Embedding Trust: Semantic Isotropy Predicts Nonfactuality in Long-Form Text Generation. CoRR Dhrupad Bhardwaj, Julia Kempe, Tim G. J. Rudner
2025	A	`conf`	Fine-Tuning with Uncertainty-Aware Priors Makes Vision and Language Foundation Models More Reliable. AISTATS Tim G. J. Rudner, Xiang Pan, Yucen Lily Li, Ravid Shwartz-Ziv, Andrew Gordon Wilson
2025	A	`conf`	Geometry-Aware Generative Autoencoders for Warped Riemannian Metric Learning and Generative Modeling on Data Manifolds. AISTATS Xingzhi Sun, Danqi Liao, Kincaid MacDonald, Yanlei Zhang, Guillaume Huguet, Guy Wolf, Ian Adelstein, Tim G. J. Rudner, Smita Krishnaswamy
2025	A	`conf`	Improving Pre-trained Self-Supervised Embeddings Through Effective Entropy Maximization. AISTATS Deep Chakraborty, Yann LeCun, Tim G. J. Rudner, Erik G. Learned-Miller
2025	J	`jnl`	Insertion Language Models: Sequence Generation with Arbitrary-Position Insertions. CoRR Dhruvesh Patel, Aishwarya Sahoo, Avinash Amballa, Tahira Naseem, Tim G. J. Rudner, Andrew McCallum
2025	J	`jnl`	Learning from Reward-Free Offline Data: A Case for Planning with Latent Dynamics Models. CoRR Vlad Sobal, Wancong Zhang, Kynghyun Cho, Randall Balestriero, Tim G. J. Rudner, Yann LeCun
2025	A*	`conf`	MetaFaith: Faithful Natural Language Uncertainty Expression in LLMs. EMNLP Gabrielle Kaili-May Liu, Gal Yona, Avi Caciularu, Idan Szpektor, Tim G. J. Rudner, Arman Cohan
2025	J	`jnl`	MetaFaith: Faithful Natural Language Uncertainty Expression in LLMs. CoRR Gabrielle Kaili-May Liu, Gal Yona, Avi Caciularu, Idan Szpektor, Tim G. J. Rudner, Arman Cohan
2025	J	`jnl`	Out-of-Distribution Detection Methods Answer the Wrong Questions. CoRR Yucen Lily Li, Daohan Lu, Polina Kirichenko, Shikai Qiu, Tim G. J. Rudner, C. Bayan Bruss, Andrew Gordon Wilson
2025	—	`conf`	Position: Supervised Classifiers Answer the Wrong Questions for OOD Detection. ICML (Position Papers) Yucen Lily Li, Daohan Lu, Polina Kirichenko, Shikai Qiu, Tim G. J. Rudner, C. Bayan Bruss, Andrew Gordon Wilson
2025	—	`conf`	SCIURus: Shared Circuits for Interpretable Uncertainty Representations in Language Models. NAACL (Long Papers) Carter Teplica, Yixin Liu, Arman Cohan, Tim G. J. Rudner
2025	—	`conf`	Simple Factuality Probes Detect Hallucinations in Long-Form Natural Language Generation. EMNLP (Findings) Jiatong Han, Neil Band, Muhammed Razzak, Jannik Kossen, Tim G. J. Rudner, Yarin Gal
2024	J	`jnl`	A Monte Carlo Framework for Calibrated Uncertainty Estimation in Sequence Prediction. CoRR Qidong Yang, Weicheng Zhu, Joseph Keslin, Laure Zanna, Tim G. J. Rudner, Carlos Fernandez-Granda
2024	A*	`conf`	A Study of Bayesian Neural Network Surrogates for Bayesian Optimization. ICLR Yucen Lily Li, Tim G. J. Rudner, Andrew Gordon Wilson
2024	J	`jnl`	Attacking Bayes: On the Adversarial Robustness of Bayesian Neural Networks. CoRR Yunzhen Feng, Tim G. J. Rudner, Nikolaos Tsilivis, Julia Kempe
2024	J	`jnl`	Attacking Bayes: On the Adversarial Robustness of Bayesian Neural Networks. Trans. Mach. Learn. Res. Yunzhen Feng, Tim G. J. Rudner, Nikolaos Tsilivis, Julia Kempe
2024	A*	`conf`	Context-Guided Diffusion for Out-of-Distribution Molecular and Protein Design. ICML Leo Klarner, Tim G. J. Rudner, Garrett M. Morris, Charlotte M. Deane, Yee Whye Teh
2024	J	`jnl`	Context-Guided Diffusion for Out-of-Distribution Molecular and Protein Design. CoRR Leo Klarner, Tim G. J. Rudner, Garrett M. Morris, Charlotte M. Deane, Yee Whye Teh
2024	J	`jnl`	Exploring the Manifold of Neural Networks Using Diffusion Geometry. CoRR 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
2024	J	`jnl`	Geometry-Aware Generative Autoencoders for Warped Riemannian Metric Learning and Generative Modeling on Data Manifolds. CoRR Xingzhi Sun, Danqi Liao, Kincaid MacDonald, Yanlei Zhang, Chen Liu, Guillaume Huguet, Guy Wolf, Ian Adelstein, Tim G. J. Rudner, Smita Krishnaswamy
2024	J	`jnl`	Improving Pre-Trained Self-Supervised Embeddings Through Effective Entropy Maximization. CoRR Deep Chakraborty, Yann LeCun, Tim G. J. Rudner, Erik G. Learned-Miller
2024	A	`conf`	Mind the GAP: Improving Robustness to Subpopulation Shifts with Group-Aware Priors. AISTATS Tim G. J. Rudner, Ya Shi Zhang, Andrew Gordon Wilson, Julia Kempe
2024	J	`jnl`	Mind the GAP: Improving Robustness to Subpopulation Shifts with Group-Aware Priors. CoRR Tim G. J. Rudner, Ya Shi Zhang, Andrew Gordon Wilson, Julia Kempe
2024	A*	`conf`	Non-Vacuous Generalization Bounds for Large Language Models. ICML Sanae Lotfi, Marc Anton Finzi, Yilun Kuang, Tim G. J. Rudner, Micah Goldblum, Andrew Gordon Wilson
2024	—	`conf`	Not Oracles of the Battlefield: Safety Considerations for AI-Based Military Decision Support Systems. AIES (1) Emelia Probasco, Matthew Burtell, Helen Toner, Tim G. J. Rudner
2024	J	`jnl`	Position Paper: Bayesian Deep Learning in the Age of Large-Scale AI. CoRR 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
2024	A*	`conf`	Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI. ICML 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
2024	A*	`conf`	Pre-trained Text-to-Image Diffusion Models Are Versatile Representation Learners for Control. NeurIPS Gunshi Gupta, Karmesh Yadav, Yarin Gal, Dhruv Batra, Zsolt Kira, Cong Lu, Tim G. J. Rudner
2024	J	`jnl`	Pre-trained Text-to-Image Diffusion Models Are Versatile Representation Learners for Control. CoRR Gunshi Gupta, Karmesh Yadav, Yarin Gal, Dhruv Batra, Zsolt Kira, Cong Lu, Tim G. J. Rudner
2023	J	`jnl`	A Study of Bayesian Neural Network Surrogates for Bayesian Optimization. CoRR Yucen Lily Li, Tim G. J. Rudner, Andrew Gordon Wilson
2023	A*	`conf`	An Information Theory Perspective on Variance-Invariance-Covariance Regularization. NeurIPS Ravid Shwartz-Ziv, Randall Balestriero, Kenji Kawaguchi, Tim G. J. Rudner, Yann LeCun
2023	J	`jnl`	An Information-Theoretic Perspective on Variance-Invariance-Covariance Regularization. CoRR Ravid Shwartz-Ziv, Randall Balestriero, Kenji Kawaguchi, Tim G. J. Rudner, Yann LeCun
2023	—	`conf`	Can Active Sampling Reduce Causal Confusion in Offline Reinforcement Learning? CLeaR Gunshi Gupta, Tim G. J. Rudner, Rowan Thomas McAllister, Adrien Gaidon, Yarin Gal
2023	J	`jnl`	Can Active Sampling Reduce Causal Confusion in Offline Reinforcement Learning? CoRR Gunshi Gupta, Tim G. J. Rudner, Rowan Thomas McAllister, Adrien Gaidon, Yarin Gal
2023	J	`jnl`	Challenges and Opportunities in Offline Reinforcement Learning from Visual Observations. Trans. Mach. Learn. Res. Cong Lu, Philip J. Ball, Tim G. J. Rudner, Jack Parker-Holder, Michael A. Osborne, Yee Whye Teh
2023	J	`jnl`	Continual Learning via Sequential Function-Space Variational Inference. CoRR Tim G. J. Rudner, Freddie Bickford Smith, Qixuan Feng, Yee Whye Teh, Yarin Gal
2023	A*	`conf`	Drug Discovery under Covariate Shift with Domain-Informed Prior Distributions over Functions. ICML Leo Klarner, Tim G. J. Rudner, Michael Reutlinger, Torsten Schindler, Garrett M. Morris, Charlotte M. Deane, Yee Whye Teh
2023	J	`jnl`	Drug Discovery under Covariate Shift with Domain-Informed Prior Distributions over Functions. CoRR Leo Klarner, Tim G. J. Rudner, Michael Reutlinger, Torsten Schindler, Garrett M. Morris, Charlotte M. Deane, Yee Whye Teh
2023	A*	`conf`	Function-Space Regularization in Neural Networks: A Probabilistic Perspective. ICML Tim G. J. Rudner, Sanyam Kapoor, Shikai Qiu, Andrew Gordon Wilson
2023	J	`jnl`	Function-Space Regularization in Neural Networks: A Probabilistic Perspective. CoRR Tim G. J. Rudner, Sanyam Kapoor, Shikai Qiu, Andrew Gordon Wilson
2023	J	`jnl`	Informative Priors Improve the Reliability of Multimodal Clinical Data Classification. CoRR L. Julián Lechuga López, Tim G. J. Rudner, Farah E. Shamout
2023	J	`jnl`	Non-Vacuous Generalization Bounds for Large Language Models. CoRR Sanae Lotfi, Marc Finzi, Yilun Kuang, Tim G. J. Rudner, Micah Goldblum, Andrew Gordon Wilson
2023	J	`jnl`	On Sequential Bayesian Inference for Continual Learning. CoRR Samuel Kessler, Adam D. Cobb, Tim G. J. Rudner, Stefan Zohren, Stephen J. Roberts
2023	J	`jnl`	On Sequential Bayesian Inference for Continual Learning. Entropy Samuel Kessler, Adam D. Cobb, Tim G. J. Rudner, Stefan Zohren, Stephen J. Roberts
2023	A*	`conf`	Protein Design with Guided Discrete Diffusion. NeurIPS Nate Gruver, Samuel Stanton, Nathan C. Frey, Tim G. J. Rudner, Isidro Hötzel, Julien Lafrance-Vanasse, Arvind Rajpal, Kyunghyun Cho, Andrew Gordon Wilson
2023	J	`jnl`	Protein Design with Guided Discrete Diffusion. CoRR Nate Gruver, Samuel Stanton, Nathan C. Frey, Tim G. J. Rudner, Isidro Hötzel, Julien Lafrance-Vanasse, Arvind Rajpal, Kyunghyun Cho, Andrew Gordon Wilson
2023	A*	`conf`	Should We Learn Most Likely Functions or Parameters? NeurIPS Shikai Qiu, Tim G. J. Rudner, Sanyam Kapoor, Andrew Gordon Wilson
2023	J	`jnl`	Should We Learn Most Likely Functions or Parameters? CoRR Shikai Qiu, Tim G. J. Rudner, Sanyam Kapoor, Andrew Gordon Wilson
2023	J	`jnl`	Tractable Function-Space Variational Inference in Bayesian Neural Networks. CoRR Tim G. J. Rudner, Zonghao Chen, Yee Whye Teh, Yarin Gal
2023	A*	`conf`	Visual Explanations of Image-Text Representations via Multi-Modal Information Bottleneck Attribution. NeurIPS Ying Wang, Tim G. J. Rudner, Andrew Gordon Wilson
2023	J	`jnl`	Visual Explanations of Image-Text Representations via Multi-Modal Information Bottleneck Attribution. CoRR Ying Wang, Tim G. J. Rudner, Andrew Gordon Wilson
2022	J	`jnl`	Benchmarking Bayesian Deep Learning on Diabetic Retinopathy Detection Tasks. CoRR Neil Band, Tim G. J. Rudner, Qixuan Feng, Angelos Filos, Zachary Nado, Michael W. Dusenberry, Ghassen Jerfel, Dustin Tran, Yarin Gal
2022	J	`jnl`	Challenges and Opportunities in Offline Reinforcement Learning from Visual Observations. CoRR Cong Lu, Philip J. Ball, Tim G. J. Rudner, Jack Parker-Holder, Michael A. Osborne, Yee Whye Teh
2022	A*	`conf`	Continual Learning via Sequential Function-Space Variational Inference. ICML Tim G. J. Rudner, Freddie Bickford Smith, Qixuan Feng, Yee Whye Teh, Yarin Gal
2022	J	`jnl`	On Pathologies in KL-Regularized Reinforcement Learning from Expert Demonstrations. CoRR Tim G. J. Rudner, Cong Lu, Michael A. Osborne, Yarin Gal, Yee Whye Teh
2022	J	`jnl`	Plex: Towards Reliability using Pretrained Large Model Extensions. CoRR 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
2022	A*	`conf`	Tractable Function-Space Variational Inference in Bayesian Neural Networks. NeurIPS Tim G. J. Rudner, Zonghao Chen, Yee Whye Teh, Yarin Gal
2021	—	`conf`	Benchmarking Bayesian Deep Learning on Diabetic Retinopathy Detection Tasks. NeurIPS Datasets and Benchmarks Neil Band, Tim G. J. Rudner, Qixuan Feng, Angelos Filos, Zachary Nado, Mike Dusenberry, Ghassen Jerfel, Dustin Tran, Yarin Gal
2021	A*	`conf`	On Pathologies in KL-Regularized Reinforcement Learning from Expert Demonstrations. NeurIPS Tim G. J. Rudner, Cong Lu, Michael A. Osborne, Yarin Gal, Yee Whye Teh
2021	A*	`conf`	On Signal-to-Noise Ratio Issues in Variational Inference for Deep Gaussian Processes. ICML Tim G. J. Rudner, Oscar Key, Yarin Gal, Tom Rainforth
2021	A*	`conf`	Outcome-Driven Reinforcement Learning via Variational Inference. NeurIPS Tim G. J. Rudner, Vitchyr Pong, Rowan McAllister, Yarin Gal, Sergey Levine
2021	J	`jnl`	Outcome-Driven Reinforcement Learning via Variational Inference. CoRR Tim G. J. Rudner, Vitchyr H. Pong, Rowan McAllister, Yarin Gal, Sergey Levine
2021	J	`jnl`	Uncertainty Baselines: Benchmarks for Uncertainty & Robustness in Deep Learning. CoRR 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
2020	A*	`conf`	Inter-domain Deep Gaussian Processes. ICML Tim G. J. Rudner, Dino Sejdinovic, Yarin Gal
2020	J	`jnl`	Inter-domain Deep Gaussian Processes. CoRR Tim G. J. Rudner, Dino Sejdinovic, Yarin Gal
2020	J	`jnl`	On Signal-to-Noise Ratio Issues in Variational Inference for Deep Gaussian Processes. CoRR Tim G. J. Rudner, Oscar Key, Yarin Gal, Tom Rainforth
2019	J	`jnl`	A Systematic Comparison of Bayesian Deep Learning Robustness in Diabetic Retinopathy Tasks. CoRR Angelos Filos, Sebastian Farquhar, Aidan N. Gomez, Tim G. J. Rudner, Zachary Kenton, Lewis Smith, Milad Alizadeh, Arnoud de Kroon, Yarin Gal
2019	A*	`conf`	Multi3Net: Segmenting Flooded Buildings via Fusion of Multiresolution, Multisensor, and Multitemporal Satellite Imagery. AAAI Tim G. J. Rudner, Marc Rußwurm, Jakub Fil, Ramona Pelich, Benjamin Bischke, Veronika Kopacková, Piotr Bilinski
2019	A	`conf`	The StarCraft Multi-Agent Challenge. AAMAS 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
2019	J	`jnl`	The StarCraft Multi-Agent Challenge. CoRR 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
2019	A*	`conf`	VIREL: A Variational Inference Framework for Reinforcement Learning. NeurIPS Matthew Fellows, Anuj Mahajan, Tim G. J. Rudner, Shimon Whiteson
2018	J	`jnl`	Multi CoRR Tim G. J. Rudner, Marc Rußwurm, Jakub Fil, Ramona Pelich, Benjamin Bischke, Veronika Kopacková, Piotr Bilinski
2018	J	`jnl`	VIREL: A Variational Inference Framework for Reinforcement Learning. CoRR Matthew Fellows, Anuj Mahajan, Tim G. J. Rudner, Shimon Whiteson