Tim G. J. Rudner
Publication record assembled from the DBLP archive of ranked conferences.
Papers indexed
20
Venues
7
Active years
2019–2025
Best venue rank
A*
Where they publish
Papers
20 indexed papers, newest first.
| Year | Venue | Title | Authors |
|---|---|---|---|
| 2025 | AISTATS | Improving Pre-trained Self-Supervised Embeddings Through Effective Entropy Maximization. | Deep Chakraborty, Yann LeCun, Tim G. J. Rudner, Erik G. Learned-Miller |
| 2025 | AISTATS | 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 |
| 2025 | AISTATS | 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 |
| 2025 | EMNLP | Simple Factuality Probes Detect Hallucinations in Long-Form Natural Language Generation. | Jiatong Han, Neil Band, Muhammed Razzak, Jannik Kossen, Tim G. J. Rudner, Yarin Gal |
| 2025 | EMNLP | MetaFaith: Faithful Natural Language Uncertainty Expression in LLMs. | Gabrielle Kaili-May Liu, Gal Yona, Avi Caciularu, Idan Szpektor, Tim G. J. Rudner, Arman Cohan |
| 2025 | ICML | Position: Supervised Classifiers Answer the Wrong Questions for OOD Detection. | Yucen Lily Li, Daohan Lu, Polina Kirichenko, Shikai Qiu, Tim G. J. Rudner, C. Bayan Bruss, Andrew Gordon Wilson |
| 2025 | ICML | Can Transformers Learn Full Bayesian Inference in Context? | Arik Reuter, Tim G. J. Rudner, Vincent Fortuin, David Rgamer |
| 2025 | NAACL | SCIURus: Shared Circuits for Interpretable Uncertainty Representations in Language Models. | Carter Teplica, Yixin Liu, Arman Cohan, Tim G. J. Rudner |
| 2024 | AIES | Not Oracles of the Battlefield: Safety Considerations for AI-Based Military Decision Support Systems. | Emelia Probasco, Matthew Burtell, Helen Toner, Tim G. J. Rudner |
| 2024 | AISTATS | Mind the GAP: Improving Robustness to Subpopulation Shifts with Group-Aware Priors. | Tim G. J. Rudner, Ya Shi Zhang, Andrew Gordon Wilson, Julia Kempe |
| 2024 | ICLR | A Study of Bayesian Neural Network Surrogates for Bayesian Optimization. | Yucen Lily Li, Tim G. J. Rudner, Andrew Gordon Wilson |
| 2024 | ICML | 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 |
| 2024 | ICML | Non-Vacuous Generalization Bounds for Large Language Models. | Sanae Lotfi, Marc Anton Finzi, Yilun Kuang, Tim G. J. Rudner, Micah Goldblum, Andrew Gordon Wilson |
| 2024 | ICML | 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, Jos Miguel Hernndez-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 Rgamer, Yee Whye Teh, Max Welling, Andrew Gordon Wilson, Ruqi Zhang |
| 2023 | ICML | 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 |
| 2023 | ICML | Function-Space Regularization in Neural Networks: A Probabilistic Perspective. | Tim G. J. Rudner, Sanyam Kapoor, Shikai Qiu, Andrew Gordon Wilson |
| 2022 | ICML | Continual Learning via Sequential Function-Space Variational Inference. | Tim G. J. Rudner, Freddie Bickford Smith, Qixuan Feng, Yee Whye Teh, Yarin Gal |
| 2021 | ICML | On Signal-to-Noise Ratio Issues in Variational Inference for Deep Gaussian Processes. | Tim G. J. Rudner, Oscar Key, Yarin Gal, Tom Rainforth |
| 2020 | ICML | Inter-domain Deep Gaussian Processes. | Tim G. J. Rudner, Dino Sejdinovic, Yarin Gal |
| 2019 | AAAI | Multi3Net: Segmenting Flooded Buildings via Fusion of Multiresolution, Multisensor, and Multitemporal Satellite Imagery. | Tim G. J. Rudner, Marc Ruwurm, Jakub Fil, Ramona Pelich, Benjamin Bischke, Veronika Kopackov, Piotr Bilinski |