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Author Information
Jean-Bastien Grill (Google 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.
Remi Munos (DeepMind)
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2022 Poster: The Nature of Temporal Difference Errors in Multi-step Distributional Reinforcement Learning »
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2021 Oral: Drop, Swap, and Generate: A Self-Supervised Approach for Generating Neural Activity »
Ran Liu · Mehdi Azabou · Max Dabagia · Chi-Heng Lin · Mohammad Gheshlaghi Azar · Keith Hengen · Michal Valko · Eva Dyer -
2021 Poster: Drop, Swap, and Generate: A Self-Supervised Approach for Generating Neural Activity »
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2021 Poster: Learning in two-player zero-sum partially observable Markov games with perfect recall »
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2021 Poster: Stochastic Shortest Path: Minimax, Parameter-Free and Towards Horizon-Free Regret »
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2021 Poster: Unifying Gradient Estimators for Meta-Reinforcement Learning via Off-Policy Evaluation »
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2020 Poster: Leverage the Average: an Analysis of KL Regularization in Reinforcement Learning »
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2020 Poster: Bootstrap Your Own Latent - A New Approach to Self-Supervised Learning »
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2020 Oral: Leverage the Average: an Analysis of KL Regularization in Reinforcement Learning »
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2020 Oral: Bootstrap Your Own Latent - A New Approach to Self-Supervised Learning »
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2020 Poster: Improved Sample Complexity for Incremental Autonomous Exploration in MDPs »
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2020 Oral: Improved Sample Complexity for Incremental Autonomous Exploration in MDPs »
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2019 Poster: Exact sampling of determinantal point processes with sublinear time preprocessing »
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2019 Poster: Planning in entropy-regularized Markov decision processes and games »
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2019 Poster: On two ways to use determinantal point processes for Monte Carlo integration »
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2018 Poster: Actor-Critic Policy Optimization in Partially Observable Multiagent Environments »
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2017 Poster: Online Influence Maximization under Independent Cascade Model with Semi-Bandit Feedback »
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2017 Poster: Successor Features for Transfer in Reinforcement Learning »
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2017 Poster: Efficient Second-Order Online Kernel Learning with Adaptive Embedding »
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2017 Spotlight: Successor Features for Transfer in Reinforcement Learning »
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2016 Poster: Unifying Count-Based Exploration and Intrinsic Motivation »
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2016 Poster: Memory-Efficient Backpropagation Through Time »
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2016 Poster: Blazing the trails before beating the path: Sample-efficient Monte-Carlo planning »
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2016 Oral: Blazing the trails before beating the path: Sample-efficient Monte-Carlo planning »
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2016 Poster: Safe and Efficient Off-Policy Reinforcement Learning »
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2015 Poster: Black-box optimization of noisy functions with unknown smoothness »
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2014 Poster: Efficient learning by implicit exploration in bandit problems with side observations »
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2014 Poster: Extreme bandits »
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2014 Poster: Online combinatorial optimization with stochastic decision sets and adversarial losses »
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