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Author Information
Mark Rowland (DeepMind)
Shayegan Omidshafiei (DeepMind)
Karl Tuyls (DeepMind)
Julien Perolat (DeepMind)
Michal Valko (DeepMind Paris and Inria Lille - Nord Europe)
Michal is a machine learning scientist in DeepMind Paris, tenured researcher at Inria, and the lecturer of the master course Graphs in Machine Learning at l'ENS Paris-Saclay. Michal is primarily interested in designing algorithms that would require as little human supervision as possible. This means 1) reducing the “intelligence” that humans need to input into the system and 2) minimizing the data that humans need to spend inspecting, classifying, or “tuning” the algorithms. That is why he is working on methods and settings that are able to deal with minimal feedback, such as deep reinforcement learning, bandit algorithms, or self-supervised learning. Michal is actively working on represenation learning and building worlds models. He is also working on deep (reinforcement) learning algorithm that have some theoretical underpinning. He has also worked on sequential algorithms with structured decisions where exploiting the structure leads to provably faster learning. He received his Ph.D. in 2011 from the University of Pittsburgh under the supervision of Miloš Hauskrecht and after was a postdoc of Rémi Munos before taking a permanent position at Inria in 2012.
Georgios Piliouras (Singapore University of Technology and Design)
Remi Munos (DeepMind)
Related Events (a corresponding poster, oral, or spotlight)
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2019 Poster: Multiagent Evaluation under Incomplete Information »
Thu. Dec 12th 01:00 -- 03:00 AM Room East Exhibition Hall B + C #220
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