Oxford Applied and Theoretical Machine Learning Group
OATML

07 Sep 2019
07 Sep 2019
01 Sep 2019
20 Aug 2019
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09/2019: "BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learning" (Andreas Kirsch, Joost van Amersfoort, Yarin Gal). |
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09/2019: "VIREL: A Variational Inference Framework for Reinforcement Learning" (Matthew Fellows, Anuj Mahajan, Tim G. J. Rudner, Shimon Whiteson). |
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09/2019: "A Geometric Perspective on Optimal Representations for Reinforcement Learning" (Marc G. Bellemare, Will Dabney, Robert Dadashi, Adrien Ali Taiga, Pablo Samuel Castro, Nicolas Le Roux, Dale Schuurmans, Tor Lattimore, Clare Lyle). |
We are researchers coming from varied backgrounds, including Computer Science, Maths & Stats, Engineering, and Physics.
We come from academia (Oxford, Cambridge, MILA, McGill, U of Amsterdam, U of Toronto, Yale, and others) and industry (Google, DeepMind, Twitter, Qualcomm, and startups).
We include 3 Rhodes Scholars, 2 Clarendon Scholars, 2 DeepMind Scholars, with our students funded by many additional sources (AIMS CDT, Cyber CDT, industry, and more).
Current group members:
Yarin Gal (Group Leader) ●
Tom Rainforth (Senior postdoc, 2019) ●
Challenger Mishra (Postdoc, 2018) ●
Lisa Schut (Research Assistant, 2019) ●
Oscar Key (Research Assistant, 2019) ●
Sören Mindermann (PhD, 2019) ●
Jishnu Mukhoti (PhD, 2019) ●
Panagiotis Tigas (PhD, 2019) ●
Andrew Jesson (PhD, 2019) ●
Pascal Notin (PhD, 2019) ●
Clare Lyle (PhD, 2018) ●
Aidan Gomez (PhD, 2018) ●
Andreas Kirsch (PhD, 2018) ●
Angelos Filos (PhD, 2018) ●
Joost van Amersfoort (PhD, 2018) ●
Milad Alizadeh (PhD, 2017) ●
Sebastian Farquhar (PhD, 2017) ●
Tim G. J. Rudner (PhD, 2017) ●
Lewis Smith (PhD, 2017)