
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
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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
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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.
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M. Fellows
,
A. Mahajan
,
T. G. J. Rudner
,
S. Whiteson
,
VIREL: A Variational Inference Framework for Reinforcement Learning, NeurIPS 2018 Workshop on Probabilistic Reinforcement Learning and Structured Control, 2018.
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T. G. J. Rudner
,
M. Rußwurm
,
J. Fil
,
R. Pelich
,
B. Bischke
,
V. Kopackova
,
P. Bilinski
,
Rapid Computer Vision-aided Disaster Response via Fusion of Multiresolution, Multisensor, and Multitemporal Satellite Imagery, NeurIPS 2018 Workshop on AI for Social Good, 2018.
2017
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T. G. J. Rudner
,
D. Sejdinovic
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Inter-domain Deep Gaussian Processes, NIPS 2017 Workshop on Bayesian Deep Learning, 2017.