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Author Information
Jean-Bastien Grill (Google DeepMind)
Omar Darwiche Domingues (Inria)
Pierre Menard (Inria)
Remi Munos (DeepMind)
Michal Valko (DeepMind Paris and Inria Lille - Nord Europe)
Michal is a research scientist in DeepMind Paris and SequeL team at Inria Lille - Nord Europe, France, lead by Philippe Preux and Rémi Munos. He also teaches the course Graphs in Machine Learning at l'ENS Cachan. 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) minimising the data that humans need spend inspecting, classifying, or “tuning” the algorithms. Another important feature of machine learning algorithms should be the ability to adapt to changing environments. That is why he is working in domains that are able to deal with minimal feedback, such as semi-supervised learning, bandit algorithms, and anomaly detection. The common thread of Michal's work has been adaptive graph-based learning and its application to the real world applications such as recommender systems, medical error detection, and face recognition. His industrial collaborators include Intel, Technicolor, and Microsoft Research. He received his PhD in 2011 from University of Pittsburgh under the supervision of Miloš Hauskrecht and after was a postdoc of Rémi Munos.
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2021 Spotlight: Stochastic Shortest Path: Minimax, Parameter-Free and Towards Horizon-Free Regret »
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2021 Spotlight: A Provably Efficient Sample Collection Strategy for 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 »
Ran Liu · Mehdi Azabou · Max Dabagia · Chi-Heng Lin · Mohammad Gheshlaghi Azar · Keith Hengen · Michal Valko · Eva Dyer -
2021 Poster: Learning in two-player zero-sum partially observable Markov games with perfect recall »
Tadashi Kozuno · Pierre Ménard · Remi Munos · Michal Valko -
2021 Poster: Stochastic Shortest Path: Minimax, Parameter-Free and Towards Horizon-Free Regret »
Jean Tarbouriech · Runlong Zhou · Simon Du · Matteo Pirotta · Michal Valko · Alessandro Lazaric -
2021 Poster: A Provably Efficient Sample Collection Strategy for Reinforcement Learning »
Jean Tarbouriech · Matteo Pirotta · Michal Valko · Alessandro Lazaric -
2021 Poster: Unifying Gradient Estimators for Meta-Reinforcement Learning via Off-Policy Evaluation »
Yunhao Tang · Tadashi Kozuno · Mark Rowland · Remi Munos · Michal Valko -
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 »
Jean-Bastien Grill · Florian Strub · Florent Altché · Corentin Tallec · Pierre Richemond · Elena Buchatskaya · Carl Doersch · Bernardo Avila Pires · Zhaohan Guo · Mohammad Gheshlaghi Azar · Bilal Piot · koray kavukcuoglu · Remi Munos · Michal Valko -
2020 Oral: Leverage the Average: an Analysis of KL Regularization in Reinforcement Learning »
Nino Vieillard · Tadashi Kozuno · Bruno Scherrer · Olivier Pietquin · Remi Munos · Matthieu Geist -
2020 Oral: Bootstrap Your Own Latent - A New Approach to Self-Supervised Learning »
Jean-Bastien Grill · Florian Strub · Florent Altché · Corentin Tallec · Pierre Richemond · Elena Buchatskaya · Carl Doersch · Bernardo Avila Pires · Zhaohan Guo · Mohammad Gheshlaghi Azar · Bilal Piot · koray kavukcuoglu · Remi Munos · Michal Valko -
2020 Poster: Improved Sample Complexity for Incremental Autonomous Exploration in MDPs »
Jean Tarbouriech · Matteo Pirotta · Michal Valko · Alessandro Lazaric -
2020 Poster: Planning in Markov Decision Processes with Gap-Dependent Sample Complexity »
Anders Jonsson · Emilie Kaufmann · Pierre Menard · Omar Darwiche Domingues · Edouard Leurent · Michal Valko -
2020 Oral: Improved Sample Complexity for Incremental Autonomous Exploration in MDPs »
Jean Tarbouriech · Matteo Pirotta · Michal Valko · Alessandro Lazaric -
2019 Poster: Exact sampling of determinantal point processes with sublinear time preprocessing »
Michal Derezinski · Daniele Calandriello · Michal Valko -
2019 Poster: On two ways to use determinantal point processes for Monte Carlo integration »
Guillaume Gautier · Rémi Bardenet · Michal Valko -
2019 Poster: Multiagent Evaluation under Incomplete Information »
Mark Rowland · Shayegan Omidshafiei · Karl Tuyls · Julien Perolat · Michal Valko · Georgios Piliouras · Remi Munos -
2019 Spotlight: Multiagent Evaluation under Incomplete Information »
Mark Rowland · Shayegan Omidshafiei · Karl Tuyls · Julien Perolat · Michal Valko · Georgios Piliouras · Remi Munos -
2019 Poster: Hindsight Credit Assignment »
Anna Harutyunyan · Will Dabney · Thomas Mesnard · Mohammad Gheshlaghi Azar · Bilal Piot · Nicolas Heess · Hado van Hasselt · Gregory Wayne · Satinder Singh · Doina Precup · Remi Munos -
2019 Spotlight: Hindsight Credit Assignment »
Anna Harutyunyan · Will Dabney · Thomas Mesnard · Mohammad Gheshlaghi Azar · Bilal Piot · Nicolas Heess · Hado van Hasselt · Gregory Wayne · Satinder Singh · Doina Precup · Remi Munos -
2018 Poster: Optimistic optimization of a Brownian »
Jean-Bastien Grill · Michal Valko · Remi Munos -
2018 Poster: Actor-Critic Policy Optimization in Partially Observable Multiagent Environments »
Sriram Srinivasan · Marc Lanctot · Vinicius Zambaldi · Julien Perolat · Karl Tuyls · Remi Munos · Michael Bowling -
2017 Poster: Online Influence Maximization under Independent Cascade Model with Semi-Bandit Feedback »
Zheng Wen · Branislav Kveton · Michal Valko · Sharan Vaswani -
2017 Poster: Successor Features for Transfer in Reinforcement Learning »
Andre Barreto · Will Dabney · Remi Munos · Jonathan Hunt · Tom Schaul · David Silver · Hado van Hasselt -
2017 Poster: Efficient Second-Order Online Kernel Learning with Adaptive Embedding »
Daniele Calandriello · Alessandro Lazaric · Michal Valko -
2017 Spotlight: Successor Features for Transfer in Reinforcement Learning »
Andre Barreto · Will Dabney · Remi Munos · Jonathan Hunt · Tom Schaul · David Silver · Hado van Hasselt -
2016 Poster: Unifying Count-Based Exploration and Intrinsic Motivation »
Marc Bellemare · Sriram Srinivasan · Georg Ostrovski · Tom Schaul · David Saxton · Remi Munos -
2016 Poster: Memory-Efficient Backpropagation Through Time »
Audrunas Gruslys · Remi Munos · Ivo Danihelka · Marc Lanctot · Alex Graves -
2016 Poster: Blazing the trails before beating the path: Sample-efficient Monte-Carlo planning »
Jean-Bastien Grill · Michal Valko · Remi Munos -
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 »
Remi Munos · Tom Stepleton · Anna Harutyunyan · Marc Bellemare -
2015 Poster: Black-box optimization of noisy functions with unknown smoothness »
Jean-Bastien Grill · Michal Valko · Remi Munos · Remi Munos -
2014 Poster: Efficient learning by implicit exploration in bandit problems with side observations »
Tomáš Kocák · Gergely Neu · Michal Valko · Remi Munos -
2014 Poster: Extreme bandits »
Alexandra Carpentier · Michal Valko -
2014 Poster: Online combinatorial optimization with stochastic decision sets and adversarial losses »
Gergely Neu · Michal Valko