Publications

2023

  1. Should We Learn Most Likely Functions or Parameters?
    Tim G. J. Rudner, Sanyam Kapoor, Shikai Qiu, and Andrew Gordon Wilson
    Advances in Neural Information Processing Systems (forthcoming) (NeurIPS), 2023
  2. wang2023m2ib.png
    Visual Explanations of Image-Text Representations via Multi-Modal Information Bottleneck Attribution
    Ying Wang, Tim G. J. Rudner, and Andrew Gordon Wilson
    Advances in Neural Information Processing Systems (forthcoming) (NeurIPS), 2023
  3. shwartz2023vicreg.png
    An Information-Theoretic Perspective on Variance-Invariance-Covariance Regularization
    Ravid Shwartz-Ziv, Randall Balestriero, Kenji Kawaguchi, Tim G. J. Rudner, and Yann LeCun
    Advances in Neural Information Processing Systems (forthcoming) (NeurIPS), 2023
  4. gruver2023nos.png
    Protein Design with Guided Discrete Diffusion
    Nate Gruver, Samuel Stanton, Nathan C. Frey, Tim G. J. Rudner, Isidro Hotzel, Julien Lafrance-Vanasse, Arvind Rajpal, Kyunghyun Cho, and Andrew Gordon Wilson
    Advances in Neural Information Processing Systems (forthcoming) (NeurIPS), 2023
  5. li2023bostudy.png
    A Study of Bayesian Neural Network Surrogates for Bayesian Optimization
    Yucen Lily Li, Tim G. J. Rudner, and Andrew Gordon Wilson
    Symposium on Advances in Approximate Bayesian Inference (AABI), 2023
  6. feng2023attackingbayes.png
    Attacking Bayes: Are Bayesian Neural Networks Inherently Robust?
    Yunzhen Feng, Tim G. J. Rudner, Nikolaos Tsilivis, and Julia Kempe
    Symposium on Advances in Approximate Bayesian Inference (AABI), 2023
  7. rudner2023fseb.png
    Function-Space Regularization in Neural Networks: A Probabilistic Perspective
    Tim G. J. Rudner, Sanyam Kapoor, Shikai Qiu, and Andrew Gordon Wilson
    International Conference on Machine Learning (ICML), 2023
  8. klarner2023qsavi.png
    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 Deane, and Yee Whye Teh
    International Conference on Machine Learning (ICML), 2023
  9. lu2023challenges.png
    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, and Yee Whye Teh
    Transactions on Machine Learning Research (TMLR), 2023
  10. gupta2023activesampling.png
    Can Active Sampling Reduce Causal Confusion in Offline Reinforcement Learning?
    Gunshi Gupta, Tim G. J. Rudner, Rowan Thomas McAllister, Adrien Gaidon, and Yarin Gal
    Conference on Causal Learning and Reasoning (CLeaR), 2023

2022

  1. rudner2022fsvi.png
    Tractable Function-Space Variational Inference in Bayesian Neural Networks
    Tim G. J. Rudner, Zonghao Chen, Yee Whye Teh, and Yarin Gal
    Advances in Neural Information Processing Systems (NeurIPS), 2022
  2. rudner2022sfsvi.png
    Continual Learning via Sequential Function-Space Variational Inference
    Tim G. J. Rudner, Freddie Bickford Smith, Qixuan Feng, Yee Whye Teh, and Yarin Gal
    International Conference on Machine Learning (ICML), 2022
  3. tran2022plex.png
    Plex: Towards Reliability Using Pretrained Large Model Extensions
    Dustin Tran, Jeremiah 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 Kirsch, Rodolphe Jenatton, Nithum Thain, Honglin Yuan, Kelly Buchanan, Kevin Murphy, D. Sculley, Yarin Gal, Zoubin Ghahramani, Jasper Snoek, and Balaji Lakshminarayanan
    ICML 2022 Workshop on Pre-training: Perspectives, Pitfalls, and Paths Forward, 2022

2021

  1. rudner2021odrl.png
    Outcome-Driven Reinforcement Learning via Variational Inference
    Tim G. J. Rudner, Vitchyr H. Pong, Rowan McAllister, Yarin Gal, and Sergey Levine
    Advances in Neural Information Processing Systems (NeurIPS), 2021
  2. rudner2021pathologies.png
    On Pathologies in KL-Regularized Reinforcement Learning from Expert Demonstrations
    Tim G. J. Rudner, Cong Lu, Michael A. Osborne, Yarin Gal, and Yee Whye Teh
    Advances in Neural Information Processing Systems (NeurIPS), 2021
  3. band2021benchmarking.png
    Benchmarking Bayesian Deep Learning on Diabetic Retinopathy Detection Tasks
    Neil Band, Tim G. J. Rudner, Qixuan Feng, Angelos Filos, Zachary Nado, Michael W. Dusenberry, Ghassen Jerfel, Dustin Tran, and Yarin Gal
    Advances in Neural Information Processing Systems (NeurIPS), 2021
  4. nado2021uncertaintybaselines.png
    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 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, and Dustin Tran
    NeurIPS 2021 Workshop on Bayesian Deep Learning, 2021
  5. rudner2021snrissues.png
    On Signal-to-Noise Ratio Issues in Variational Inference for Deep Gaussian Processes
    Tim G. J. Rudner, Oscar Key, Yarin Gal, and Tom Rainforth
    International Conference on Machine Learning (ICML), 2021

2020

  1. rudner2020interdomaindgps.png
    Inter-domain Deep Gaussian Processes
    Tim G. J. Rudner, Dino Sejdinovic, and Yarin Gal
    International Conference on Machine Learning (ICML), 2020

2019

  1. fellows2019virel.png
    VIREL: A Variational Inference Framework for Reinforcement Learning
    Matthew Fellows, Anuj Mahajan, Tim G. J. Rudner, and Shimon Whiteson
    Advances in Neural Information Processing Systems (NeurIPS), 2019
  2. rudner2019naturalntk.png
    The Natural Neural Tangent Kernel: Neural Network Training Dynamics under Natural Gradient Descent
    Tim G. J. Rudner, Florian Wenzel, Yee Whye Teh, and Yarin Gal
    NeurIPS 2019 Workshop on Bayesian Deep Learning, 2019
  3. filos2019bdlb.png
    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 Kroon, and Yarin Gal
    NeurIPS 2019 Workshop on Bayesian Deep Learning, 2019
  4. rudner2019multi3net.png
    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 Kopackova, and Piotr Bilinski
    AAAI Conference on Artificial Intelligence (AAAI), 2019
  5. samvelyan19smac.png
    The StarCraft Multi-Agent Challenge
    Mikayel Samvelyan, Tabish Rashid, Christian Witt, Gregory Farquhar, Nantas Nardelli, Tim G. J. Rudner, Chia-Man Hung, Philip H. S. Torr, Jakob Foerster, and Shimon Whiteson
    International Conference on Autonomous Agents and MultiAgent Systems (AAMAS), 2019

2018

  1. rudner2018npsasgps.png
    On the Connection between Neural Processes and Gaussian Processes with Deep Kernels
    Tim G. J. Rudner, Vincent Fortuin, Yee Whye Teh, and Yarin Gal
    NeurIPS 2018 Workshop on Bayesian Deep Learning, 2018