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Jean-Bastien Grill (INRIA Lille - Nord Europe)
Michal Valko (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 (Google DeepMind)
Remi Munos (Google DeepMind)
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2021 Oral: Drop, Swap, and Generate: A Self-Supervised Approach for Generating Neural Activity »
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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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2019 Poster: Multiagent Evaluation under Incomplete Information »
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2019 Poster: Hindsight Credit Assignment »
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2019 Spotlight: Hindsight Credit Assignment »
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2018 Poster: Optimistic optimization of a Brownian »
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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 »
Jean-Bastien Grill · Michal Valko · Remi Munos -
2016 Poster: Safe and Efficient Off-Policy Reinforcement Learning »
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2014 Poster: Active Regression by Stratification »
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2014 Poster: Best-Arm Identification in Linear Bandits »
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2014 Poster: Bounded Regret for Finite-Armed Structured Bandits »
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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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2014 Poster: Optimistic Planning in Markov Decision Processes Using a Generative Model »
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2013 Workshop: Bayesian Optimization in Theory and Practice »
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2013 Poster: Thompson Sampling for 1-Dimensional Exponential Family Bandits »
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2013 Poster: Aggregating Optimistic Planning Trees for Solving Markov Decision Processes »
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2012 Poster: Bandit Algorithms boost Brain Computer Interfaces for motor-task selection of a brain-controlled button »
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2012 Poster: Adaptive Stratified Sampling for Monte-Carlo integration of Differentiable functions »
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2012 Poster: Risk-Aversion in Multi-armed Bandits »
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2011 Poster: Finite Time Analysis of Stratified Sampling for Monte Carlo »
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2011 Poster: Selecting the State-Representation in Reinforcement Learning »
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2011 Poster: Sparse Recovery with Brownian Sensing »
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2011 Session: Spotlight Session 2 »
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2011 Session: Oral Session 1 »
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2011 Poster: Optimistic Optimization of Deterministic Functions »
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2011 Poster: Speedy Q-Learning »
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2010 Spotlight: LSTD with Random Projections »
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2010 Poster: LSTD with Random Projections »
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2010 Poster: Scrambled Objects for Least-Squares Regression »
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2010 Poster: Error Propagation for Approximate Policy and Value Iteration »
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2009 Poster: Sensitivity analysis in HMMs with application to likelihood maximization »
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2009 Poster: Compressed Least-Squares Regression »
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2008 Poster: Online Optimization in X-Armed Bandits »
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2008 Poster: Algorithms for Infinitely Many-Armed Bandits »
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2008 Spotlight: Algorithms for Infinitely Many-Armed Bandits »
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2008 Poster: Particle Filter-based Policy Gradient in POMDPs »
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2007 Poster: Fitted Q-iteration in continuous action-space MDPs »
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