Tim G. J. Rudner

Tim G. J. Rudner

Probabilistic inference, reinforcement learning, Gaussian Processes

I am a DPhil student supervised by Yee Whye Teh and Yarin Gal. My research interests span Bayesian deep learning, variational inference, and reinforcement learning. I am particularly interested in uncertainty quantification in deep learning, reinforcement learning as probabilistic inference, and probabilistic transfer learning. I obtained a master’s degree in statistics from the University of Oxford and an undergraduate degree in mathematics and economics from Yale University. I am an AI Fellow at Georgetown University’s Center for Security and Emerging Technology, a Fellow of the German Academic Scholarship Foundation, and a Rhodes Scholar.

Publications

2019

  • M. Fellows , A. Mahajan , T. G. J. Rudner , S. Whiteson , VIREL: A Variational Inference Framework for Reinforcement Learning, in Advances in Neural Information Processing Systems 32, 2019.
  • T. G. J. Rudner , M. Rußwurm , J. Fil , R. Pelich , B. Bischke , V. Kopackova , P. Bilinski , Multi³Net: Segmenting Flooded Buildings via Fusion of Multiresolution, Multisensor, and Multitemporal Satellite Imagery, in Proceedings of the Thirty-Three AAAI Conference on Artificial Intelligence, 2019.
  • M. Samvelyan , T. Rashid , C. Witt , G. Farquhar , N. Nardelli , T. G. J. Rudner , C. Hung , P. H. S. Torr , J. Foerster , S. Whiteson , The StarCraft Multi-Agent Challenge, in Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems, 2019.

2018

  • T. G. J. Rudner , V. Fortuin , Y. W. Teh , Y. Gal , On the Connection between Neural Processes and Approximate Gaussian Processes, NeurIPS 2018 Workshop on Bayesian Deep Learning, 2018.

2017

  • T. G. J. Rudner , D. Sejdinovic , Inter-domain Deep Gaussian Processes, NeurIPS 2017 Workshop on Bayesian Deep Learning, 2017.