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Broaden Your Views for Self-Supervised Video Learning


Mar 30, 2021
Adrià Recasens, Pauline Luc, Jean-Baptiste Alayrac, Luyu Wang, Florian Strub, Corentin Tallec, Mateusz Malinowski, Viorica Patraucean, Florent Altché, Michal Valko, Jean-Bastien Grill, Aäron van den Oord, Andrew Zisserman


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UCB Momentum Q-learning: Correcting the bias without forgetting


Mar 01, 2021
Pierre Menard, Omar Darwiche Domingues, Xuedong Shang, Michal Valko


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Revisiting Peng's Q($λ$) for Modern Reinforcement Learning


Feb 27, 2021
Tadashi Kozuno, Yunhao Tang, Mark Rowland, RĂ©mi Munos, Steven Kapturowski, Will Dabney, Michal Valko, David Abel

* 26 pages, 7 figures, 2 tables 

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Mine Your Own vieW: Self-Supervised Learning Through Across-Sample Prediction


Feb 19, 2021
Mehdi Azabou, Mohammad Gheshlaghi Azar, Ran Liu, Chi-Heng Lin, Erik C. Johnson, Kiran Bhaskaran-Nair, Max Dabagia, Keith B. Hengen, William Gray-Roncal, Michal Valko, Eva L. Dyer


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Bootstrapped Representation Learning on Graphs


Feb 12, 2021
Shantanu Thakoor, Corentin Tallec, Mohammad Gheshlaghi Azar, Rémi Munos, Petar Veličković, Michal Valko


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Geometric Entropic Exploration


Jan 07, 2021
Zhaohan Daniel Guo, Mohammad Gheshlaghi Azar, Alaa Saade, Shantanu Thakoor, Bilal Piot, Bernardo Avila Pires, Michal Valko, Thomas Mesnard, Tor Lattimore, RĂ©mi Munos


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Improved Sample Complexity for Incremental Autonomous Exploration in MDPs


Dec 29, 2020
Jean Tarbouriech, Matteo Pirotta, Michal Valko, Alessandro Lazaric

* NeurIPS 2020 

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Game Plan: What AI can do for Football, and What Football can do for AI


Nov 18, 2020
Karl Tuyls, Shayegan Omidshafiei, Paul Muller, Zhe Wang, Jerome Connor, Daniel Hennes, Ian Graham, William Spearman, Tim Waskett, Dafydd Steele, Pauline Luc, Adria Recasens, Alexandre Galashov, Gregory Thornton, Romuald Elie, Pablo Sprechmann, Pol Moreno, Kris Cao, Marta Garnelo, Praneet Dutta, Michal Valko, Nicolas Heess, Alex Bridgland, Julien Perolat, Bart De Vylder, Ali Eslami, Mark Rowland, Andrew Jaegle, Remi Munos, Trevor Back, Razia Ahamed, Simon Bouton, Nathalie Beauguerlange, Jackson Broshear, Thore Graepel, Demis Hassabis


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BYOL works even without batch statistics


Oct 20, 2020
Pierre H. Richemond, Jean-Bastien Grill, Florent Altché, Corentin Tallec, Florian Strub, Andrew Brock, Samuel Smith, Soham De, Razvan Pascanu, Bilal Piot, Michal Valko


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Episodic Reinforcement Learning in Finite MDPs: Minimax Lower Bounds Revisited


Oct 07, 2020
Omar Darwiche Domingues, Pierre MĂ©nard, Emilie Kaufmann, Michal Valko


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Fast active learning for pure exploration in reinforcement learning


Jul 27, 2020
Pierre MĂ©nard, Omar Darwiche Domingues, Anders Jonsson, Emilie Kaufmann, Edouard Leurent, Michal Valko


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Monte-Carlo Tree Search as Regularized Policy Optimization


Jul 24, 2020
Jean-Bastien Grill, Florent Altché, Yunhao Tang, Thomas Hubert, Michal Valko, Ioannis Antonoglou, Rémi Munos

* Accepted to International Conference on Machine Learning (ICML), 2020 

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A Provably Efficient Sample Collection Strategy for Reinforcement Learning


Jul 13, 2020
Jean Tarbouriech, Matteo Pirotta, Michal Valko, Alessandro Lazaric


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A Kernel-Based Approach to Non-Stationary Reinforcement Learning in Metric Spaces


Jul 09, 2020
Omar Darwiche Domingues, Pierre MĂ©nard, Matteo Pirotta, Emilie Kaufmann, Michal Valko


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Gamification of Pure Exploration for Linear Bandits


Jul 02, 2020
RĂ©my Degenne, Pierre MĂ©nard, Xuedong Shang, Michal Valko

* 11+25 pages. To be published in the proceedings of ICML 2020 

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Sampling from a $k$-DPP without looking at all items


Jun 30, 2020
Daniele Calandriello, Michał Dereziński, Michal Valko


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Stochastic bandits with arm-dependent delays


Jun 18, 2020
Anne Gael Manegueu, Claire Vernade, Alexandra Carpentier, Michal Valko

* 19 Pages, 4 figures 

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Bootstrap Your Own Latent: A New Approach to Self-Supervised Learning


Jun 13, 2020
Jean-Bastien Grill, Florian Strub, Florent Altché, Corentin Tallec, Pierre H. Richemond, Elena Buchatskaya, Carl Doersch, Bernardo Avila Pires, Zhaohan Daniel Guo, Mohammad Gheshlaghi Azar, Bilal Piot, Koray Kavukcuoglu, Rémi Munos, Michal Valko


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Statistical Efficiency of Thompson Sampling for Combinatorial Semi-Bandits


Jun 11, 2020
Pierre Perrault, Etienne Boursier, Vianney Perchet, Michal Valko


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Adaptive Reward-Free Exploration


Jun 11, 2020
Emilie Kaufmann, Pierre MĂ©nard, Omar Darwiche Domingues, Anders Jonsson, Edouard Leurent, Michal Valko


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Planning in Markov Decision Processes with Gap-Dependent Sample Complexity


Jun 10, 2020
Anders Jonsson, Emilie Kaufmann, Pierre MĂ©nard, Omar Darwiche Domingues, Edouard Leurent, Michal Valko


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Regret Bounds for Kernel-Based Reinforcement Learning


Apr 12, 2020
Omar Darwiche Domingues, Pierre MĂ©nard, Matteo Pirotta, Emilie Kaufmann, Michal Valko


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Taylor Expansion Policy Optimization


Mar 13, 2020
Yunhao Tang, Michal Valko, RĂ©mi Munos


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Near-linear Time Gaussian Process Optimization with Adaptive Batching and Resparsification


Feb 26, 2020
Daniele Calandriello, Luigi Carratino, Alessandro Lazaric, Michal Valko, Lorenzo Rosasco


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No-Regret Exploration in Goal-Oriented Reinforcement Learning


Jan 30, 2020
Jean Tarbouriech, Evrard Garcelon, Michal Valko, Matteo Pirotta, Alessandro Lazaric


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Multiagent Evaluation under Incomplete Information


Oct 30, 2019
Mark Rowland, Shayegan Omidshafiei, Karl Tuyls, Julien Perolat, Michal Valko, Georgios Piliouras, Remi Munos


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Fixed-Confidence Guarantees for Bayesian Best-Arm Identification


Oct 28, 2019
Xuedong Shang, Rianne de Heide, Emilie Kaufmann, Pierre MĂ©nard, Michal Valko


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