Arthur Guez

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Arthur Guez [1]

Arthur Guez,
a Canadian computer and neuro scientist, currently researcher at Google DeepMind with expertise in machine learning, in particular deep learning, and involved in the AlphaGo and AlphaZero projects. He holds a M.Sc. in machine learning from McGill University in 2010 and a Ph.D. from Gatsby Computational Neuroscience Unit at University College London in 2015 titled Sample-based Search Methods for Bayes-Adaptive Planning, where he was supervised by Peter Dayan and David Silver.

Ph.D. Thesis

In his Ph.D. thesis, Arthur Guez elaborates on search and planning methods in the face of uncertainty about the environment inducing the exploration versus exploitation trade-off of an agent-based model to optimize the return by maintaining a posterior distribution over possible environments considering all possible future paths. This optimization is equivalent to solving a Markov decision process (MDP) whose hyperstate comprises the agent’s beliefs about the environment, as well as its current state in that environment - the corresponding process is called a Bayes-Adaptive MDP (BAMDP), also using a tailored Monte-Carlo tree search. In historical notes on Bayesian Adaptive control, Arthur Guez mentions Abraham Wald's Sequential Probability Ratio Test (SPRT) [2], and that Alan Turing assisted by Jack Good used a similar sequential testing technique to help decipher enigma codes at Bletchley Park [3] [4].

Selected Publications


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2015 ...

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External Links


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