Tim G. J. Rudner

PhD Candidate, Department of Computer Science, University of Oxford

  Oxford Applied & Theoretical Machine Learning Group (OATML)

  Oxford Computational Statistics & Machine Learning Group (OxCSML)

Wolfson Building, Parks Road, Oxford OX1 3QD, United Kingdom

tim.rudner [AT] cs.ox.ac.uk

     

About

I am a PhD Candidate in the Department of Computer Science at the University of Oxford, where I conduct research on probabilistic machine learning with Yarin Gal and Yee Whye Teh. My research interests span Bayesian deep learning, variational inference, and reinforcement learning. I am particularly interested in Bayesian uncertainty quantification in deep learning, probabilistic inference in reinforcement learning and control, and AI safety. For my work on safe decision-making under uncertainty, I received the 2021 Qualcomm Innovation Fellowship. I deeply care about equitable access to education and serve as an Equality, Diversity & Inclusion Fellow at the University of Oxford.

I am also a Rhodes Scholar and an AI Fellow at Georgetown University's Center for Security and Emerging Technology.

Bio

I obtained an undergraduate degree in mathematics and economics from Yale University, where I received the Charles E. Clark Memorial Award for Academic Excellence and was fortunate to be advised by Sekhar Tatikonda. Subsequently, I earned a master's degree in statistics from the University of Oxford, where I was supervised by Dino Sejdinovic. Prior to my PhD, I conducted research on game theoretic equilibria in digital goods markets, systemic risk in financial markets, and drivers of financial crises. I am a member of the Oxford Center for Doctoral Training in Autonomous Intelligent Machines & Systems (AIMS) and the OECD Network of Experts, as well as a Fellow of the German Academic Scholarship Foundation.

News

Publications

Preprints





Continual Learning via Function-Space Variational Inference

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

Preprint. 2021.

ICML '21 Workshop on Theory and Foundations of Continual Learning. 2021.

ICML '21 Workshop on Subset Selection in Machine Learning: From Theory to Applications. 2021.

  Paper   Bibtex





Tractable Function-Space Variational Inference in Bayesian Neural Networks

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

Preprint. 2021.

ICML '21 Workshop on Uncertainty & Robustness in Deep Learning. 2021.

AABI '21 Symposium on Advances in Approximate Bayesian Inference. 2021.

  Paper   Bibtex







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, Dustin Tran

Preprint. 2021.

NeurIPS '21 Workshop on Bayesian Deep Learning. 2021.

   arXiv      Code   Bibtex
2021



Outcome-Driven Reinforcement Learning via Variational Inference

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

NeurIPS '21 Conference on Neural Information Processing Systems. 2021.

  Paper   Website     Talk     Bibtex



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 '21 Conference on Neural Information Processing Systems. 2021.

  Paper   Website    Poster    Bibtex




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, Yarin Gal

NeurIPS '21 Conference on Neural Information Processing Systems. 2021.

  Paper     Code   Bibtex



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

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

ICML '21 International Conference on Machine Learning. 2021.

   arXiv      Code       Talk       Slides     Poster    Bibtex
2020



Inter-domain Deep Gaussian Processes

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

ICML '20 International Conference on Machine Learning. 2020.

   arXiv    Website     Talk       Slides  Bibtex
2019



VIREL: A Variational Inference Framework for Reinforcement Learning

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

NeurIPS '19 Conference on Neural Information Processing Systems. 2019. (Spotlight Talk)

   arXiv      Code   Bibtex



The Natural Neural Tangent Kernel: Neural Network Training Dynamics under Natural Gradient Descent

Tim G. J. Rudner, Florian Wenzel, Yee Whye Teh, Yarin Gal

NeurIPS '19 Workshop on Bayesian Deep Learning. 2019. (Contributed Talk)

  Paper       Talk     Bibtex




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

NeurIPS '19 Workshop on Bayesian Deep Learning. 2019.

   arXiv      Code   Bibtex



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, Piotr Bilinski

AAAI '19 Conference on Artificial Intelligence. 2019.

   arXiv      Code    Media  Bibtex




The StarCraft Multi-Agent Challenge

Mikayel Samvelyan, Tabish Rashid, Christian Schroeder de Witt, Gregory Farquhar, Nantas Nardelli,
Tim G. J. Rudner, Chia-Man Hung, Philip H. S. Torr, Jakob Foerster, Shimon Whiteson

AAMAS '19 International Conference on Autonomous Agents and Multiagent Systems. 2019.

   arXiv      Code   Bibtex
2018



On the Connection between Neural Processes and Gaussian Processes with Deep Kernels

Tim G. J. Rudner, Vincent Fortuin, Yee Whye Teh, Yarin Gal

NeurIPS '18 Workshop on Bayesian Deep Learning. 2018.

  Paper   Bibtex

Teaching (Past & Present)