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, reinforcement learning, and variational inference. I am particularly interested in uncertainty quantification in deep learning, reinforcement learning as probabilistic inference, and probabilistic meta 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 a member of the Oxford Center for Doctoral Training in Autonomous Intelligent Machines & Systems, a Fellow of the German National Academic Foundation, and a Rhodes Scholar.

Publications

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 (AAAI), 2019.

2018

  • T. G. J. Rudner , V. Fortuin , Y. W. Teh , Y. Gal , On the Connection between Neural Processes and Approximate Gaussian Processes, NIPS 2018 Workshop on Bayesian Deep Learning, 2018.
  • M. Fellows , A. Mahajan , T. G. J. Rudner , S. Whiteson , VIREL: A Variational Inference Framework for Reinforcement Learning, NIPS 2018 Workshop on Probabilistic Reinforcement Learning and Structured Control, 2018.
  • 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, NIPS 2018 Workshop on AI for Social Good, 2018.

2017

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