Michal Valko

Publication Activity (10 Years)

Years Active: 2007-2024
Publications (10 Years): 165

Top Venues

CoRR

ICML

NeurIPS

AISTATS

Publications

Yunhao Tang, Zhaohan Daniel Guo, Zeyu Zheng, Daniele Calandriello, Rémi Munos, Mark Rowland, Pierre Harvey Richemond, Michal Valko, Bernardo Ávila Pires, Bilal Piot
Generalized Preference Optimization: A Unified Approach to Offline Alignment. CoRR (2024)
Tianlin Liu, Shangmin Guo, Leonardo Bianco, Daniele Calandriello, Quentin Berthet, Felipe Llinares, Jessica Hoffmann, Lucas Dixon, Michal Valko, Mathieu Blondel
Decoding-time Realignment of Language Models. CoRR (2024)
Rémi Munos, Michal Valko, Daniele Calandriello, Mohammad Gheshlaghi Azar, Mark Rowland, Zhaohan Daniel Guo, Yunhao Tang, Matthieu Geist, Thomas Mesnard, Andrea Michi, Marco Selvi, Sertan Girgin, Nikola Momchev, Olivier Bachem, Daniel J. Mankowitz, Doina Precup, Bilal Piot
Nash Learning from Human Feedback. CoRR (2023)
Yunhao Tang, Rémi Munos, Mark Rowland, Michal Valko
VA-learning as a more efficient alternative to Q-learning. CoRR (2023)
Daniil Tiapkin, Denis Belomestny, Daniele Calandriello, Eric Moulines, Alexey Naumov, Pierre Perrault, Michal Valko, Pierre Ménard
Demonstration-Regularized RL. CoRR (2023)
Yunhao Tang, Rémi Munos, Mark Rowland, Michal Valko
VA-learning as a more efficient alternative to Q-learning. ICML (2023)
Côme Fiegel, Pierre Ménard, Tadashi Kozuno, Rémi Munos, Vianney Perchet, Michal Valko
Local and adaptive mirror descents in extensive-form games. CoRR (2023)
Alaa Saade, Steven Kapturowski, Daniele Calandriello, Charles Blundell, Pablo Sprechmann, Leopoldo Sarra, Oliver Groth, Michal Valko, Bilal Piot
Unlocking the Power of Representations in Long-term Novelty-based Exploration. CoRR (2023)
Daniel Jarrett, Corentin Tallec, Florent Altché, Thomas Mesnard, Rémi Munos, Michal Valko
Curiosity in Hindsight: Intrinsic Exploration in Stochastic Environments. ICML (2023)
Yunhao Tang, Tadashi Kozuno, Mark Rowland, Anna Harutyunyan, Rémi Munos, Bernardo Ávila Pires, Michal Valko
DoMo-AC: Doubly Multi-step Off-policy Actor-Critic Algorithm. ICML (2023)
Mehdi Azabou, Venkataramana Ganesh, Shantanu Thakoor, Chi-Heng Lin, Lakshmi Sathidevi, Ran Liu, Michal Valko, Petar Velickovic, Eva L. Dyer
Half-Hop: A graph upsampling approach for slowing down message passing. CoRR (2023)
Côme Fiegel, Pierre Ménard, Tadashi Kozuno, Rémi Munos, Vianney Perchet, Michal Valko
Adapting to game trees in zero-sum imperfect information games. ICML (2023)
Yunhao Tang, Zhaohan Daniel Guo, Pierre Harvey Richemond, Bernardo Ávila Pires, Yash Chandak, Rémi Munos, Mark Rowland, Mohammad Gheshlaghi Azar, Charline Le Lan, Clare Lyle, András György, Shantanu Thakoor, Will Dabney, Bilal Piot, Daniele Calandriello, Michal Valko
Understanding Self-Predictive Learning for Reinforcement Learning. ICML (2023)
Daniil Tiapkin, Denis Belomestny, Daniele Calandriello, Eric Moulines, Rémi Munos, Alexey Naumov, Pierre Perrault, Yunhao Tang, Michal Valko, Pierre Ménard
Fast Rates for Maximum Entropy Exploration. CoRR (2023)
Toshinori Kitamura, Tadashi Kozuno, Yunhao Tang, Nino Vieillard, Michal Valko, Wenhao Yang, Jincheng Mei, Pierre Ménard, Mohammad Gheshlaghi Azar, Rémi Munos, Olivier Pietquin, Matthieu Geist, Csaba Szepesvári, Wataru Kumagai, Yutaka Matsuo
Regularization and Variance-Weighted Regression Achieves Minimax Optimality in Linear MDPs: Theory and Practice. CoRR (2023)
Thomas Mesnard, Wenqi Chen, Alaa Saade, Yunhao Tang, Mark Rowland, Theophane Weber, Clare Lyle, Audrunas Gruslys, Michal Valko, Will Dabney, Georg Ostrovski, Eric Moulines, Rémi Munos
Quantile Credit Assignment. ICML (2023)
Mehdi Azabou, Venkataramana Ganesh, Shantanu Thakoor, Chi-Heng Lin, Lakshmi Sathidevi, Ran Liu, Michal Valko, Petar Velickovic, Eva L. Dyer
Half-Hop: A graph upsampling approach for slowing down message passing. ICML (2023)
Daniil Tiapkin, Denis Belomestny, Daniele Calandriello, Eric Moulines, Rémi Munos, Alexey Naumov, Pierre Perrault, Michal Valko, Pierre Ménard
Model-free Posterior Sampling via Learning Rate Randomization. CoRR (2023)
Mohammad Gheshlaghi Azar, Mark Rowland, Bilal Piot, Daniel Guo, Daniele Calandriello, Michal Valko, Rémi Munos
A General Theoretical Paradigm to Understand Learning from Human Preferences. CoRR (2023)
Toshinori Kitamura, Tadashi Kozuno, Yunhao Tang, Nino Vieillard, Michal Valko, Wenhao Yang, Jincheng Mei, Pierre Ménard, Mohammad Gheshlaghi Azar, Rémi Munos, Olivier Pietquin, Matthieu Geist, Csaba Szepesvári, Wataru Kumagai, Yutaka Matsuo
Regularization and Variance-Weighted Regression Achieves Minimax Optimality in Linear MDPs: Theory and Practice. ICML (2023)
Daniil Tiapkin, Denis Belomestny, Daniele Calandriello, Eric Moulines, Rémi Munos, Alexey Naumov, Pierre Perrault, Yunhao Tang, Michal Valko, Pierre Ménard
Fast Rates for Maximum Entropy Exploration. ICML (2023)
Yunhao Tang, Tadashi Kozuno, Mark Rowland, Anna Harutyunyan, Rémi Munos, Bernardo Ávila Pires, Michal Valko
DoMo-AC: Doubly Multi-step Off-policy Actor-Critic Algorithm. CoRR (2023)
Daniil Tiapkin, Denis Belomestny, Daniele Calandriello, Eric Moulines, Rémi Munos, Alexey Naumov, Pierre Perrault, Michal Valko, Pierre Ménard
Model-free Posterior Sampling via Learning Rate Randomization. NeurIPS (2023)
Yunhao Tang, Mark Rowland, Rémi Munos, Michal Valko
Marginalized Operators for Off-policy Reinforcement Learning. CoRR (2022)
Zhaohan Guo, Shantanu Thakoor, Miruna Pislar, Bernardo Ávila Pires, Florent Altché, Corentin Tallec, Alaa Saade, Daniele Calandriello, Jean-Bastien Grill, Yunhao Tang, Michal Valko, Rémi Munos, Mohammad Gheshlaghi Azar, Bilal Piot
BYOL-Explore: Exploration by Bootstrapped Prediction. NeurIPS (2022)
Anirudh Goyal, Abram L. Friesen, Andrea Banino, Theophane Weber, Nan Rosemary Ke, Adrià Puigdomènech Badia, Arthur Guez, Mehdi Mirza, Peter C. Humphreys, Ksenia Konyushkova, Michal Valko, Simon Osindero, Timothy P. Lillicrap, Nicolas Heess, Charles Blundell
Retrieval-Augmented Reinforcement Learning. ICML (2022)
Yunhao Tang, Mark Rowland, Rémi Munos, Michal Valko
Marginalized Operators for Off-policy Reinforcement Learning. AISTATS (2022)
Jean Tarbouriech, Omar Darwiche Domingues, Pierre Ménard, Matteo Pirotta, Michal Valko, Alessandro Lazaric
Adaptive Multi-Goal Exploration. AISTATS (2022)
Daniil Tiapkin, Denis Belomestny, Daniele Calandriello, Eric Moulines, Rémi Munos, Alexey Naumov, Mark Rowland, Michal Valko, Pierre Ménard
Optimistic Posterior Sampling for Reinforcement Learning with Few Samples and Tight Guarantees. NeurIPS (2022)
Zhaohan Daniel Guo, Shantanu Thakoor, Miruna Pislar, Bernardo Ávila Pires, Florent Altché, Corentin Tallec, Alaa Saade, Daniele Calandriello, Jean-Bastien Grill, Yunhao Tang, Michal Valko, Rémi Munos, Mohammad Gheshlaghi Azar, Bilal Piot
BYOL-Explore: Exploration by Bootstrapped Prediction. CoRR (2022)
Daniele Calandriello, Luigi Carratino, Alessandro Lazaric, Michal Valko, Lorenzo Rosasco
Scaling Gaussian Process Optimization by Evaluating a Few Unique Candidates Multiple Times. CoRR (2022)
Daniel Jarrett, Corentin Tallec, Florent Altché, Thomas Mesnard, Rémi Munos, Michal Valko
Curiosity in hindsight. CoRR (2022)
Côme Fiegel, Pierre Ménard, Tadashi Kozuno, Rémi Munos, Vianney Perchet, Michal Valko
Adapting to game trees in zero-sum imperfect information games. CoRR (2022)
Anirudh Goyal, Abram L. Friesen, Andrea Banino, Theophane Weber, Nan Rosemary Ke, Adrià Puigdomènech Badia, Arthur Guez, Mehdi Mirza, Ksenia Konyushkova, Michal Valko, Simon Osindero, Timothy P. Lillicrap, Nicolas Heess, Charles Blundell
Retrieval-Augmented Reinforcement Learning. CoRR (2022)
Tadashi Kozuno, Wenhao Yang, Nino Vieillard, Toshinori Kitamura, Yunhao Tang, Jincheng Mei, Pierre Ménard, Mohammad Gheshlaghi Azar, Michal Valko, Rémi Munos, Olivier Pietquin, Matthieu Geist, Csaba Szepesvári
KL-Entropy-Regularized RL with a Generative Model is Minimax Optimal. CoRR (2022)
Yunhao Tang, Zhaohan Daniel Guo, Pierre Harvey Richemond, Bernardo Ávila Pires, Yash Chandak, Rémi Munos, Mark Rowland, Mohammad Gheshlaghi Azar, Charline Le Lan, Clare Lyle, András György, Shantanu Thakoor, Will Dabney, Bilal Piot, Daniele Calandriello, Michal Valko
Understanding Self-Predictive Learning for Reinforcement Learning. CoRR (2022)
Daniil Tiapkin, Denis Belomestny, Daniele Calandriello, Eric Moulines, Rémi Munos, Alexey Naumov, Mark Rowland, Michal Valko, Pierre Ménard
Optimistic Posterior Sampling for Reinforcement Learning with Few Samples and Tight Guarantees. CoRR (2022)
Daniele Calandriello, Luigi Carratino, Alessandro Lazaric, Michal Valko, Lorenzo Rosasco
Scaling Gaussian Process Optimization by Evaluating a Few Unique Candidates Multiple Times. ICML (2022)
Daniil Tiapkin, Denis Belomestny, Eric Moulines, Alexey Naumov, Sergey Samsonov, Yunhao Tang, Michal Valko, Pierre Ménard
From Dirichlet to Rubin: Optimistic Exploration in RL without Bonuses. CoRR (2022)
Daniil Tiapkin, Denis Belomestny, Eric Moulines, Alexey Naumov, Sergey Samsonov, Yunhao Tang, Michal Valko, Pierre Ménard
From Dirichlet to Rubin: Optimistic Exploration in RL without Bonuses. ICML (2022)
Shantanu Thakoor, Corentin Tallec, Mohammad Gheshlaghi Azar, Mehdi Azabou, Eva L. Dyer, Rémi Munos, Petar Velickovic, Michal Valko
Large-Scale Representation Learning on Graphs via Bootstrapping. ICLR (2022)
Jean Tarbouriech, Matteo Pirotta, Michal Valko, Alessandro Lazaric
Sample Complexity Bounds for Stochastic Shortest Path with a Generative Model. ALT (2021)
Yunhao Tang, Tadashi Kozuno, Mark Rowland, Rémi Munos, Michal Valko
Unifying Gradient Estimators for Meta-Reinforcement Learning via Off-Policy Evaluation. NeurIPS (2021)
Jean Tarbouriech, Matteo Pirotta, Michal Valko, Alessandro Lazaric
A Provably Efficient Sample Collection Strategy for Reinforcement Learning. NeurIPS (2021)
Omar Darwiche Domingues, Pierre Ménard, Matteo Pirotta, Emilie Kaufmann, Michal Valko
A Kernel-Based Approach to Non-Stationary Reinforcement Learning in Metric Spaces. AISTATS (2021)
Tadashi Kozuno, Yunhao Tang, Mark Rowland, Rémi Munos, Steven Kapturowski, Will Dabney, Michal Valko, David Abel
Revisiting Peng's Q(λ) for Modern Reinforcement Learning. CoRR (2021)
Ran Liu, Mehdi Azabou, Max Dabagia, Chi-Heng Lin, Mohammad Gheshlaghi Azar, Keith B. Hengen, Michal Valko, Eva L. Dyer
Drop, Swap, and Generate: A Self-Supervised Approach for Generating Neural Activity. CoRR (2021)
Shantanu Thakoor, Corentin Tallec, Mohammad Gheshlaghi Azar, Rémi Munos, Petar Velickovic, Michal Valko
Bootstrapped Representation Learning on Graphs. CoRR (2021)
Pierre Ménard, Omar Darwiche Domingues, Xuedong Shang, Michal Valko
UCB Momentum Q-learning: Correcting the bias without forgetting. CoRR (2021)
Adrià Recasens, Pauline Luc, Jean-Baptiste Alayrac, Luyu Wang, Florian Strub, Corentin Tallec, Mateusz Malinowski, Viorica Patraaucean, Florent Altché, Michal Valko, Jean-Bastien Grill, Aäron van den Oord, Andrew Zisserman
Broaden Your Views for Self-Supervised Video Learning. ICCV (2021)
Tadashi Kozuno, Pierre Ménard, Rémi Munos, Michal Valko
Model-Free Learning for Two-Player Zero-Sum Partially Observable Markov Games with Perfect Recall. CoRR (2021)
Yunhao Tang, Mark Rowland, Rémi Munos, Michal Valko
Taylor Expansion of Discount Factors. ICML (2021)
Jean Tarbouriech, Omar Darwiche Domingues, Pierre Ménard, Matteo Pirotta, Michal Valko, Alessandro Lazaric
Adaptive Multi-Goal Exploration. CoRR (2021)
Jean Tarbouriech, Runlong Zhou, Simon S. Du, Matteo Pirotta, Michal Valko, Alessandro Lazaric
Stochastic Shortest Path: Minimax, Parameter-Free and Towards Horizon-Free Regret. CoRR (2021)
Pierre Ménard, Omar Darwiche Domingues, Xuedong Shang, Michal Valko
UCB Momentum Q-learning: Correcting the bias without forgetting. ICML (2021)
Omar Darwiche Domingues, Pierre Ménard, Emilie Kaufmann, Michal Valko
Episodic Reinforcement Learning in Finite MDPs: Minimax Lower Bounds Revisited. ALT (2021)
Emilie Kaufmann, Pierre Ménard, Omar Darwiche Domingues, Anders Jonsson, Edouard Leurent, Michal Valko
Adaptive Reward-Free Exploration. ALT (2021)
Xavier Fontaine, Pierre Perrault, Michal Valko, Vianney Perchet
Online A-Optimal Design and Active Linear Regression. ICML (2021)
Jean Tarbouriech, Runlong Zhou, Simon S. Du, Matteo Pirotta, Michal Valko, Alessandro Lazaric
Stochastic Shortest Path: Minimax, Parameter-Free and Towards Horizon-Free Regret. NeurIPS (2021)
Pierre Ménard, Omar Darwiche Domingues, Anders Jonsson, Emilie Kaufmann, Edouard Leurent, Michal Valko
Fast active learning for pure exploration in reinforcement learning. ICML (2021)
Guillaume Gautier, Rémi Bardenet, Michal Valko
Fast sampling from β-ensembles. Stat. Comput. 31 (1) (2021)
Tadashi Kozuno, Yunhao Tang, Mark Rowland, Rémi Munos, Steven Kapturowski, Will Dabney, Michal Valko, David Abel
Revisiting Peng's Q(λ) for Modern Reinforcement Learning. ICML (2021)
Yunhao Tang, Tadashi Kozuno, Mark Rowland, Rémi Munos, Michal Valko
Unifying Gradient Estimators for Meta-Reinforcement Learning via Off-Policy Evaluation. CoRR (2021)
Pierre Perrault, Jennifer Healey, Zheng Wen, Michal Valko
On the Approximation Relationship between Optimizing Ratio of Submodular (RS) and Difference of Submodular (DS) Functions. CoRR (2021)
Zhaohan Daniel Guo, Mohammad Gheshlaghi Azar, Alaa Saade, Shantanu Thakoor, Bilal Piot, Bernardo Ávila Pires, Michal Valko, Thomas Mesnard, Tor Lattimore, Rémi Munos
Geometric Entropic Exploration. CoRR (2021)
Tadashi Kozuno, Pierre Ménard, Rémi Munos, Michal Valko
Learning in two-player zero-sum partially observable Markov games with perfect recall. NeurIPS (2021)
Karl Tuyls, Shayegan Omidshafiei, Paul Muller, Zhe Wang, Jerome T. Connor, Daniel Hennes, Ian Graham, William Spearman, Tim Waskett, Dafydd Steele, Pauline Luc, Adrià Recasens, Alexandre Galashov, Gregory Thornton, Romuald Elie, Pablo Sprechmann, Pol Moreno, Kris Cao, Marta Garnelo, Praneet Dutta, Michal Valko, Nicolas Heess, Alex Bridgland, Julien Pérolat, Bart De Vylder, S. M. Ali Eslami, Mark Rowland, Andrew Jaegle, Rémi Munos, Trevor Back, Razia Ahamed, Simon Bouton, Nathalie Beauguerlange, Jackson Broshear, Thore Graepel, Demis Hassabis
Game Plan: What AI can do for Football, and What Football can do for AI. J. Artif. Intell. Res. 71 (2021)
Mehdi Azabou, Mohammad Gheshlaghi Azar, Ran Liu, Chi-Heng Lin, Erik C. Johnson, Kiran Bhaskaran-Nair, Max Dabagia, Keith B. Hengen, William R. Gray Roncal, Michal Valko, Eva L. Dyer
Mine Your Own vieW: Self-Supervised Learning Through Across-Sample Prediction. CoRR (2021)
Omar Darwiche Domingues, Pierre Ménard, Matteo Pirotta, Emilie Kaufmann, Michal Valko
Kernel-Based Reinforcement Learning: A Finite-Time Analysis. ICML (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
Broaden Your Views for Self-Supervised Video Learning. CoRR (2021)
Ran Liu, Mehdi Azabou, Max Dabagia, Chi-Heng Lin, Mohammad Gheshlaghi Azar, Keith B. Hengen, Michal Valko, Eva L. Dyer
Drop, Swap, and Generate: A Self-Supervised Approach for Generating Neural Activity. NeurIPS (2021)
Yunhao Tang, Mark Rowland, Rémi Munos, Michal Valko
Taylor Expansion of Discount Factors. CoRR (2021)
Aadirupa Saha, Pierre Gaillard, Michal Valko
Improved Sleeping Bandits with Stochastic Actions Sets and Adversarial Rewards. CoRR (2020)
Daniele Calandriello, Michal Derezinski, Michal Valko
Sampling from a k-DPP without looking at all items. CoRR (2020)
Pierre Perrault, Jennifer Healey, Zheng Wen, Michal Valko
Budgeted Online Influence Maximization. ICML (2020)
Rémy Degenne, Pierre Ménard, Xuedong Shang, Michal Valko
Gamification of Pure Exploration for Linear Bandits. ICML (2020)
Daniele Calandriello, Michal Derezinski, Michal Valko
Sampling from a k-DPP without looking at all items. NeurIPS (2020)
Yunhao Tang, Michal Valko, Rémi Munos
Taylor Expansion Policy Optimization. CoRR (2020)
Omar Darwiche Domingues, Pierre Ménard, Matteo Pirotta, Emilie Kaufmann, Michal Valko
A Kernel-Based Approach to Non-Stationary Reinforcement Learning in Metric Spaces. CoRR (2020)
Daniele Calandriello, Luigi Carratino, Alessandro Lazaric, Michal Valko, Lorenzo Rosasco
Near-linear Time Gaussian Process Optimization with Adaptive Batching and Resparsification. CoRR (2020)
Haitham Ammar, Victor Gabillon, Rasul Tutunov, Michal Valko
Derivative-Free & Order-Robust Optimisation. AISTATS (2020)
Pierre Perrault, Etienne Boursier, Michal Valko, Vianney Perchet
Statistical Efficiency of Thompson Sampling for Combinatorial Semi-Bandits. NeurIPS (2020)
Jean Tarbouriech, Matteo Pirotta, Michal Valko, Alessandro Lazaric
Improved Sample Complexity for Incremental Autonomous Exploration in MDPs. NeurIPS (2020)
Jean-Bastien Grill, Florian Strub, Florent Altché, Corentin Tallec, Pierre H. Richemond, Elena Buchatskaya, Carl Doersch, Bernardo Ávila Pires, Zhaohan Daniel Guo, Mohammad Gheshlaghi Azar, Bilal Piot, Koray Kavukcuoglu, Rémi Munos, Michal Valko
Bootstrap Your Own Latent: A New Approach to Self-Supervised Learning. CoRR (2020)
Pierre Perrault, Michal Valko, Vianney Perchet
Covariance-adapting algorithm for semi-bandits with application to sparse outcomes. COLT (2020)
Pierre H. Richemond, Jean-Bastien Grill, Florent Altché, Corentin Tallec, Florian Strub, Andrew Brock, Samuel L. Smith, Soham De, Razvan Pascanu, Bilal Piot, Michal Valko
BYOL works even without batch statistics. CoRR (2020)
Daniele Calandriello, Luigi Carratino, Alessandro Lazaric, Michal Valko, Lorenzo Rosasco
Near-linear time Gaussian process optimization with adaptive batching and resparsification. ICML (2020)
Pierre Perrault, Etienne Boursier, Vianney Perchet, Michal Valko
Statistical Efficiency of Thompson Sampling for Combinatorial Semi-Bandits. CoRR (2020)
Jean-Bastien Grill, Florian Strub, Florent Altché, Corentin Tallec, Pierre H. Richemond, Elena Buchatskaya, Carl Doersch, Bernardo Ávila Pires, Zhaohan Guo, Mohammad Gheshlaghi Azar, Bilal Piot, Koray Kavukcuoglu, Rémi Munos, Michal Valko
Bootstrap Your Own Latent - A New Approach to Self-Supervised Learning. NeurIPS (2020)
Anders Jonsson, Emilie Kaufmann, Pierre Ménard, Omar Darwiche Domingues, Edouard Leurent, Michal Valko
Planning in Markov Decision Processes with Gap-Dependent Sample Complexity. NeurIPS (2020)
Julien Seznec, Pierre Ménard, Alessandro Lazaric, Michal Valko
A single algorithm for both restless and rested rotting bandits. AISTATS (2020)
Karl Tuyls, Shayegan Omidshafiei, Paul Muller, Zhe Wang, Jerome T. Connor, Daniel Hennes, Ian Graham, William Spearman, Tim Waskett, Dafydd Steele, Pauline Luc, Adrià Recasens, Alexandre Galashov, Gregory Thornton, Romuald Elie, Pablo Sprechmann, Pol Moreno, Kris Cao, Marta Garnelo, Praneet Dutta, Michal Valko, Nicolas Heess, Alex Bridgland, Julien Pérolat, Bart De Vylder, S. M. Ali Eslami, Mark Rowland, Andrew Jaegle, Rémi Munos, Trevor Back, Razia Ahamed, Simon Bouton, Nathalie Beauguerlange, Jackson Broshear, Thore Graepel, Demis Hassabis
Game Plan: What AI can do for Football, and What Football can do for AI. CoRR (2020)
Omar Darwiche Domingues, Pierre Ménard, Emilie Kaufmann, Michal Valko
Episodic Reinforcement Learning in Finite MDPs: Minimax Lower Bounds Revisited. CoRR (2020)
Anne Gael Manegueu, Claire Vernade, Alexandra Carpentier, Michal Valko
Stochastic bandits with arm-dependent delays. ICML (2020)
Jean Tarbouriech, Evrard Garcelon, Michal Valko, Matteo Pirotta, Alessandro Lazaric
No-Regret Exploration in Goal-Oriented Reinforcement Learning. ICML (2020)
Rémy Degenne, Pierre Ménard, Xuedong Shang, Michal Valko
Gamification of Pure Exploration for Linear Bandits. CoRR (2020)
Yunhao Tang, Michal Valko, Rémi Munos
Taylor Expansion Policy Optimization. ICML (2020)
Jean-Bastien Grill, Florent Altché, Yunhao Tang, Thomas Hubert, Michal Valko, Ioannis Antonoglou, Rémi Munos
Monte-Carlo Tree Search as Regularized Policy Optimization. CoRR (2020)
Aadirupa Saha, Pierre Gaillard, Michal Valko
Improved Sleeping Bandits with Stochastic Action Sets and Adversarial Rewards. ICML (2020)
Jean Tarbouriech, Matteo Pirotta, Michal Valko, Alessandro Lazaric
Improved Sample Complexity for Incremental Autonomous Exploration in MDPs. CoRR (2020)

Michal Valko.css-eqlu8i{font-size:small;padding-left:4px;}

Publication Activity (10 Years)

Top Topics

Top Venues

Michal Valko