Michal Valko - RankMe

207 papers A* 70A 20B 7Misc 2Journal 93Unranked 15

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Language Generation with Replay: A Learning-Theoretic View of Model Collapse. CoRR Giorgio Racca, Michal Valko, Amartya Sanyal
2025	J	`jnl`	Accelerating Nash Learning from Human Feedback via Mirror Prox. CoRR Daniil Tiapkin, Daniele Calandriello, Denis Belomestny, Eric Moulines, Alexey Naumov, Kashif Rasul, Michal Valko, Pierre Ménard
2025	J	`jnl`	RL-finetuning LLMs from on- and off-policy data with a single algorithm. CoRR Yunhao Tang, Taco Cohen, David W. Zhang, Michal Valko, Rémi Munos
2025	A*	`conf`	The Harder Path: Last Iterate Convergence for Uncoupled Learning in Zero-Sum Games with Bandit Feedback. ICML Côme Fiegel, Pierre Ménard, Tadashi Kozuno, Michal Valko, Vianney Perchet
2024	A	`conf`	A General Theoretical Paradigm to Understand Learning from Human Preferences. AISTATS Mohammad Gheshlaghi Azar, Zhaohan Daniel Guo, Bilal Piot, Rémi Munos, Mark Rowland, Michal Valko, Daniele Calandriello
2024	J	`jnl`	A New Bound on the Cumulant Generating Function of Dirichlet Processes. CoRR Pierre Perrault, Denis Belomestny, Pierre Ménard, Éric Moulines, Alexey Naumov, Daniil Tiapkin, Michal Valko
2024	—	`conf`	CardioCNN: Gamification of counterfactual image transplants. BMVC Workshops Hakan Lane, Michal Valko, Stefan Kramer
2024	A*	`conf`	Decoding-time Realignment of Language Models. ICML Tianlin Liu, Shangmin Guo, Leonardo Bianco, Daniele Calandriello, Quentin Berthet, Felipe Llinares-López, Jessica Hoffmann, Lucas Dixon, Michal Valko, Mathieu Blondel
2024	J	`jnl`	Decoding-time Realignment of Language Models. CoRR Tianlin Liu, Shangmin Guo, Leonardo Bianco, Daniele Calandriello, Quentin Berthet, Felipe Llinares, Jessica Hoffmann, Lucas Dixon, Michal Valko, Mathieu Blondel
2024	A*	`conf`	Demonstration-Regularized RL. ICLR Daniil Tiapkin, Denis Belomestny, Daniele Calandriello, Eric Moulines, Alexey Naumov, Pierre Perrault, Michal Valko, Pierre Ménard
2024	A*	`conf`	Generalized Preference Optimization: A Unified Approach to Offline Alignment. ICML Yunhao Tang, Zhaohan Daniel Guo, Zeyu Zheng, Daniele Calandriello, Rémi Munos, Mark Rowland, Pierre Harvey Richemond, Michal Valko, Bernardo Ávila Pires, Bilal Piot
2024	J	`jnl`	Generalized Preference Optimization: A Unified Approach to Offline Alignment. CoRR Yunhao Tang, Zhaohan Daniel Guo, Zeyu Zheng, Daniele Calandriello, Rémi Munos, Mark Rowland, Pierre Harvey Richemond, Michal Valko, Bernardo Ávila Pires, Bilal Piot
2024	A*	`conf`	Human Alignment of Large Language Models through Online Preference Optimisation. ICML Daniele Calandriello, Zhaohan Daniel Guo, Rémi Munos, Mark Rowland, Yunhao Tang, Bernardo Ávila Pires, Pierre Harvey Richemond, Charline Le Lan, Michal Valko, Tianqi Liu, Rishabh Joshi, Zeyu Zheng, Bilal Piot
2024	J	`jnl`	Human Alignment of Large Language Models through Online Preference Optimisation. CoRR Daniele Calandriello, Daniel Guo, Rémi Munos, Mark Rowland, Yunhao Tang, Bernardo Ávila Pires, Pierre Harvey Richemond, Charline Le Lan, Michal Valko, Tianqi Liu, Rishabh Joshi, Zeyu Zheng, Bilal Piot
2024	A*	`conf`	Local and Adaptive Mirror Descents in Extensive-Form Games. NeurIPS Côme Fiegel, Pierre Ménard, Tadashi Kozuno, Rémi Munos, Vianney Perchet, Michal Valko
2024	A*	`conf`	Metacognitive Capabilities of LLMs: An Exploration in Mathematical Problem Solving. NeurIPS Aniket Didolkar, Anirudh Goyal, Nan Rosemary Ke, Siyuan Guo, Michal Valko, Timothy P. Lillicrap, Danilo Jimenez Rezende, Yoshua Bengio, Michael C. Mozer, Sanjeev Arora
2024	J	`jnl`	Metacognitive Capabilities of LLMs: An Exploration in Mathematical Problem Solving. CoRR Aniket Didolkar, Anirudh Goyal, Nan Rosemary Ke, Siyuan Guo, Michal Valko, Timothy P. Lillicrap, Danilo J. Rezende, Yoshua Bengio, Michael Mozer, Sanjeev Arora
2024	A*	`conf`	Nash Learning from Human Feedback. ICML Rémi Munos, Michal Valko, Daniele Calandriello, Mohammad Gheshlaghi Azar, Mark Rowland, Daniel Guo, Yunhao Tang, Matthieu Geist, Thomas Mesnard, Côme Fiegel, Andrea Michi, Marco Selvi, Sertan Girgin, Nikola Momchev, Olivier Bachem, Daniel J. Mankowitz, Doina Precup, Bilal Piot
2024	J	`jnl`	Optimal Design for Reward Modeling in RLHF. CoRR Antoine Scheid, Etienne Boursier, Alain Durmus, Michael I. Jordan, Pierre Ménard, Eric Moulines, Michal Valko
2024	—	`conf`	Patchhealer: Counterfactual Image Segment Transplants with chest X-ray domain check. BMVC Workshops Hakan Lane, Michal Valko, Veda Sahaja Bandi, Nandini Lokesh Reddy, Stefan Kramer
2024	J	`jnl`	Preference Optimization with Multi-Sample Comparisons. CoRR Chaoqi Wang, Zhuokai Zhao, Chen Zhu, Karthik Abinav Sankararaman, Michal Valko, Xuefei Cao, Zhaorun Chen, Madian Khabsa, Yuxin Chen, Hao Ma, Sinong Wang
2024	J	`jnl`	Understanding the performance gap between online and offline alignment algorithms. CoRR Yunhao Tang, Zhaohan Daniel Guo, Zeyu Zheng, Daniele Calandriello, Yuan Cao, Eugene Tarassov, Rémi Munos, Bernardo Ávila Pires, Michal Valko, Yong Cheng, Will Dabney
2024	A*	`conf`	Unlocking the Power of Representations in Long-term Novelty-based Exploration. ICLR Alaa Saade, Steven Kapturowski, Daniele Calandriello, Charles Blundell, Pablo Sprechmann, Leopoldo Sarra, Oliver Groth, Michal Valko, Bilal Piot
2023	J	`jnl`	A General Theoretical Paradigm to Understand Learning from Human Preferences. CoRR Mohammad Gheshlaghi Azar, Mark Rowland, Bilal Piot, Daniel Guo, Daniele Calandriello, Michal Valko, Rémi Munos
2023	A*	`conf`	Adapting to game trees in zero-sum imperfect information games. ICML Côme Fiegel, Pierre Ménard, Tadashi Kozuno, Rémi Munos, Vianney Perchet, Michal Valko
2023	A*	`conf`	Curiosity in Hindsight: Intrinsic Exploration in Stochastic Environments. ICML Daniel Jarrett, Corentin Tallec, Florent Altché, Thomas Mesnard, Rémi Munos, Michal Valko
2023	J	`jnl`	Demonstration-Regularized RL. CoRR Daniil Tiapkin, Denis Belomestny, Daniele Calandriello, Eric Moulines, Alexey Naumov, Pierre Perrault, Michal Valko, Pierre Ménard
2023	A*	`conf`	DoMo-AC: Doubly Multi-step Off-policy Actor-Critic Algorithm. ICML Yunhao Tang, Tadashi Kozuno, Mark Rowland, Anna Harutyunyan, Rémi Munos, Bernardo Ávila Pires, Michal Valko
2023	J	`jnl`	DoMo-AC: Doubly Multi-step Off-policy Actor-Critic Algorithm. CoRR Yunhao Tang, Tadashi Kozuno, Mark Rowland, Anna Harutyunyan, Rémi Munos, Bernardo Ávila Pires, Michal Valko
2023	A*	`conf`	Fast Rates for Maximum Entropy Exploration. ICML Daniil Tiapkin, Denis Belomestny, Daniele Calandriello, Eric Moulines, Rémi Munos, Alexey Naumov, Pierre Perrault, Yunhao Tang, Michal Valko, Pierre Ménard
2023	J	`jnl`	Fast Rates for Maximum Entropy Exploration. CoRR Daniil Tiapkin, Denis Belomestny, Daniele Calandriello, Eric Moulines, Rémi Munos, Alexey Naumov, Pierre Perrault, Yunhao Tang, Michal Valko, Pierre Ménard
2023	A*	`conf`	Half-Hop: A graph upsampling approach for slowing down message passing. ICML Mehdi Azabou, Venkataramana Ganesh, Shantanu Thakoor, Chi-Heng Lin, Lakshmi Sathidevi, Ran Liu, Michal Valko, Petar Velickovic, Eva L. Dyer
2023	J	`jnl`	Half-Hop: A graph upsampling approach for slowing down message passing. CoRR Mehdi Azabou, Venkataramana Ganesh, Shantanu Thakoor, Chi-Heng Lin, Lakshmi Sathidevi, Ran Liu, Michal Valko, Petar Velickovic, Eva L. Dyer
2023	J	`jnl`	Local and adaptive mirror descents in extensive-form games. CoRR Côme Fiegel, Pierre Ménard, Tadashi Kozuno, Rémi Munos, Vianney Perchet, Michal Valko
2023	A*	`conf`	Model-free Posterior Sampling via Learning Rate Randomization. NeurIPS Daniil Tiapkin, Denis Belomestny, Daniele Calandriello, Eric Moulines, Rémi Munos, Alexey Naumov, Pierre Perrault, Michal Valko, Pierre Ménard
2023	J	`jnl`	Model-free Posterior Sampling via Learning Rate Randomization. CoRR Daniil Tiapkin, Denis Belomestny, Daniele Calandriello, Eric Moulines, Rémi Munos, Alexey Naumov, Pierre Perrault, Michal Valko, Pierre Ménard
2023	J	`jnl`	Nash Learning from Human Feedback. CoRR 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
2023	A*	`conf`	Quantile Credit Assignment. ICML 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
2023	A*	`conf`	Regularization and Variance-Weighted Regression Achieves Minimax Optimality in Linear MDPs: Theory and Practice. ICML 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
2023	J	`jnl`	Regularization and Variance-Weighted Regression Achieves Minimax Optimality in Linear MDPs: Theory and Practice. CoRR 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
2023	A*	`conf`	Understanding Self-Predictive Learning for Reinforcement Learning. ICML 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
2023	J	`jnl`	Unlocking the Power of Representations in Long-term Novelty-based Exploration. CoRR Alaa Saade, Steven Kapturowski, Daniele Calandriello, Charles Blundell, Pablo Sprechmann, Leopoldo Sarra, Oliver Groth, Michal Valko, Bilal Piot
2023	A*	`conf`	VA-learning as a more efficient alternative to Q-learning. ICML Yunhao Tang, Rémi Munos, Mark Rowland, Michal Valko
2023	J	`jnl`	VA-learning as a more efficient alternative to Q-learning. CoRR Yunhao Tang, Rémi Munos, Mark Rowland, Michal Valko
2022	J	`jnl`	Adapting to game trees in zero-sum imperfect information games. CoRR Côme Fiegel, Pierre Ménard, Tadashi Kozuno, Rémi Munos, Vianney Perchet, Michal Valko
2022	A	`conf`	Adaptive Multi-Goal Exploration. AISTATS Jean Tarbouriech, Omar Darwiche Domingues, Pierre Ménard, Matteo Pirotta, Michal Valko, Alessandro Lazaric
2022	A*	`conf`	BYOL-Explore: Exploration by Bootstrapped Prediction. NeurIPS 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
2022	J	`jnl`	BYOL-Explore: Exploration by Bootstrapped Prediction. CoRR 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
2022	J	`jnl`	Curiosity in hindsight. CoRR Daniel Jarrett, Corentin Tallec, Florent Altché, Thomas Mesnard, Rémi Munos, Michal Valko
2022	A*	`conf`	From Dirichlet to Rubin: Optimistic Exploration in RL without Bonuses. ICML Daniil Tiapkin, Denis Belomestny, Eric Moulines, Alexey Naumov, Sergey Samsonov, Yunhao Tang, Michal Valko, Pierre Ménard
2022	J	`jnl`	From Dirichlet to Rubin: Optimistic Exploration in RL without Bonuses. CoRR Daniil Tiapkin, Denis Belomestny, Eric Moulines, Alexey Naumov, Sergey Samsonov, Yunhao Tang, Michal Valko, Pierre Ménard
2022	J	`jnl`	KL-Entropy-Regularized RL with a Generative Model is Minimax Optimal. CoRR 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
2022	A*	`conf`	Large-Scale Representation Learning on Graphs via Bootstrapping. ICLR Shantanu Thakoor, Corentin Tallec, Mohammad Gheshlaghi Azar, Mehdi Azabou, Eva L. Dyer, Rémi Munos, Petar Velickovic, Michal Valko
2022	A	`conf`	Marginalized Operators for Off-policy Reinforcement Learning. AISTATS Yunhao Tang, Mark Rowland, Rémi Munos, Michal Valko
2022	J	`jnl`	Marginalized Operators for Off-policy Reinforcement Learning. CoRR Yunhao Tang, Mark Rowland, Rémi Munos, Michal Valko
2022	A*	`conf`	Optimistic Posterior Sampling for Reinforcement Learning with Few Samples and Tight Guarantees. NeurIPS Daniil Tiapkin, Denis Belomestny, Daniele Calandriello, Eric Moulines, Rémi Munos, Alexey Naumov, Mark Rowland, Michal Valko, Pierre Ménard
2022	J	`jnl`	Optimistic Posterior Sampling for Reinforcement Learning with Few Samples and Tight Guarantees. CoRR Daniil Tiapkin, Denis Belomestny, Daniele Calandriello, Eric Moulines, Rémi Munos, Alexey Naumov, Mark Rowland, Michal Valko, Pierre Ménard
2022	A*	`conf`	Retrieval-Augmented Reinforcement Learning. ICML Anirudh Goyal, Abram L. Friesen, Andrea Banino, Theophane Weber, Nan Rosemary Ke, Adrià Puigdomènech Badia, Arthur Guez, Mehdi Mirza, Peter Conway Humphreys, Ksenia Konyushkova, Michal Valko, Simon Osindero, Timothy P. Lillicrap, Nicolas Heess, Charles Blundell
2022	J	`jnl`	Retrieval-Augmented Reinforcement Learning. CoRR 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
2022	A*	`conf`	Scaling Gaussian Process Optimization by Evaluating a Few Unique Candidates Multiple Times. ICML Daniele Calandriello, Luigi Carratino, Alessandro Lazaric, Michal Valko, Lorenzo Rosasco
2022	J	`jnl`	Scaling Gaussian Process Optimization by Evaluating a Few Unique Candidates Multiple Times. CoRR Daniele Calandriello, Luigi Carratino, Alessandro Lazaric, Michal Valko, Lorenzo Rosasco
2022	J	`jnl`	Understanding Self-Predictive Learning for Reinforcement Learning. CoRR 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
2021	A	`conf`	A Kernel-Based Approach to Non-Stationary Reinforcement Learning in Metric Spaces. AISTATS Omar Darwiche Domingues, Pierre Ménard, Matteo Pirotta, Emilie Kaufmann, Michal Valko
2021	A*	`conf`	A Provably Efficient Sample Collection Strategy for Reinforcement Learning. NeurIPS Jean Tarbouriech, Matteo Pirotta, Michal Valko, Alessandro Lazaric
2021	J	`jnl`	Adaptive Multi-Goal Exploration. CoRR Jean Tarbouriech, Omar Darwiche Domingues, Pierre Ménard, Matteo Pirotta, Michal Valko, Alessandro Lazaric
2021	B	`conf`	Adaptive Reward-Free Exploration. ALT Emilie Kaufmann, Pierre Ménard, Omar Darwiche Domingues, Anders Jonsson, Edouard Leurent, Michal Valko
2021	J	`jnl`	Bootstrapped Representation Learning on Graphs. CoRR Shantanu Thakoor, Corentin Tallec, Mohammad Gheshlaghi Azar, Rémi Munos, Petar Velickovic, Michal Valko
2021	A*	`conf`	Broaden Your Views for Self-Supervised Video Learning. ICCV 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
2021	J	`jnl`	Broaden Your Views for Self-Supervised Video Learning. CoRR 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
2021	A*	`conf`	Drop, Swap, and Generate: A Self-Supervised Approach for Generating Neural Activity. NeurIPS Ran Liu, Mehdi Azabou, Max Dabagia, Chi-Heng Lin, Mohammad Gheshlaghi Azar, Keith B. Hengen, Michal Valko, Eva L. Dyer
2021	J	`jnl`	Drop, Swap, and Generate: A Self-Supervised Approach for Generating Neural Activity. CoRR Ran Liu, Mehdi Azabou, Max Dabagia, Chi-Heng Lin, Mohammad Gheshlaghi Azar, Keith B. Hengen, Michal Valko, Eva L. Dyer
2021	B	`conf`	Episodic Reinforcement Learning in Finite MDPs: Minimax Lower Bounds Revisited. ALT Omar Darwiche Domingues, Pierre Ménard, Emilie Kaufmann, Michal Valko
2021	A*	`conf`	Fast active learning for pure exploration in reinforcement learning. ICML Pierre Ménard, Omar Darwiche Domingues, Anders Jonsson, Emilie Kaufmann, Edouard Leurent, Michal Valko
2021	J	`jnl`	Fast sampling from β-ensembles. Stat. Comput. Guillaume Gautier, Rémi Bardenet, Michal Valko
2021	J	`jnl`	Game Plan: What AI can do for Football, and What Football can do for AI. J. Artif. Intell. Res. 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
2021	J	`jnl`	Geometric Entropic Exploration. CoRR Zhaohan Daniel Guo, Mohammad Gheshlaghi Azar, Alaa Saade, Shantanu Thakoor, Bilal Piot, Bernardo Ávila Pires, Michal Valko, Thomas Mesnard, Tor Lattimore, Rémi Munos
2021	A*	`conf`	Kernel-Based Reinforcement Learning: A Finite-Time Analysis. ICML Omar Darwiche Domingues, Pierre Ménard, Matteo Pirotta, Emilie Kaufmann, Michal Valko
2021	A*	`conf`	Learning in two-player zero-sum partially observable Markov games with perfect recall. NeurIPS Tadashi Kozuno, Pierre Ménard, Rémi Munos, Michal Valko
2021	J	`jnl`	Mine Your Own vieW: Self-Supervised Learning Through Across-Sample Prediction. CoRR 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
2021	J	`jnl`	Model-Free Learning for Two-Player Zero-Sum Partially Observable Markov Games with Perfect Recall. CoRR Tadashi Kozuno, Pierre Ménard, Rémi Munos, Michal Valko
2021	J	`jnl`	On the Approximation Relationship between Optimizing Ratio of Submodular (RS) and Difference of Submodular (DS) Functions. CoRR Pierre Perrault, Jennifer Healey, Zheng Wen, Michal Valko
2021	A*	`conf`	Online A-Optimal Design and Active Linear Regression. ICML Xavier Fontaine, Pierre Perrault, Michal Valko, Vianney Perchet
2021	A*	`conf`	Revisiting Peng's Q(λ) for Modern Reinforcement Learning. ICML Tadashi Kozuno, Yunhao Tang, Mark Rowland, Rémi Munos, Steven Kapturowski, Will Dabney, Michal Valko, David Abel
2021	J	`jnl`	Revisiting Peng's Q(λ) for Modern Reinforcement Learning. CoRR Tadashi Kozuno, Yunhao Tang, Mark Rowland, Rémi Munos, Steven Kapturowski, Will Dabney, Michal Valko, David Abel
2021	B	`conf`	Sample Complexity Bounds for Stochastic Shortest Path with a Generative Model. ALT Jean Tarbouriech, Matteo Pirotta, Michal Valko, Alessandro Lazaric
2021	A*	`conf`	Stochastic Shortest Path: Minimax, Parameter-Free and Towards Horizon-Free Regret. NeurIPS Jean Tarbouriech, Runlong Zhou, Simon S. Du, Matteo Pirotta, Michal Valko, Alessandro Lazaric
2021	J	`jnl`	Stochastic Shortest Path: Minimax, Parameter-Free and Towards Horizon-Free Regret. CoRR Jean Tarbouriech, Runlong Zhou, Simon S. Du, Matteo Pirotta, Michal Valko, Alessandro Lazaric
2021	A*	`conf`	Taylor Expansion of Discount Factors. ICML Yunhao Tang, Mark Rowland, Rémi Munos, Michal Valko
2021	J	`jnl`	Taylor Expansion of Discount Factors. CoRR Yunhao Tang, Mark Rowland, Rémi Munos, Michal Valko
2021	A*	`conf`	UCB Momentum Q-learning: Correcting the bias without forgetting. ICML Pierre Ménard, Omar Darwiche Domingues, Xuedong Shang, Michal Valko
2021	J	`jnl`	UCB Momentum Q-learning: Correcting the bias without forgetting. CoRR Pierre Ménard, Omar Darwiche Domingues, Xuedong Shang, Michal Valko
2021	A*	`conf`	Unifying Gradient Estimators for Meta-Reinforcement Learning via Off-Policy Evaluation. NeurIPS Yunhao Tang, Tadashi Kozuno, Mark Rowland, Rémi Munos, Michal Valko
2021	J	`jnl`	Unifying Gradient Estimators for Meta-Reinforcement Learning via Off-Policy Evaluation. CoRR Yunhao Tang, Tadashi Kozuno, Mark Rowland, Rémi Munos, Michal Valko
2020	J	`jnl`	A Kernel-Based Approach to Non-Stationary Reinforcement Learning in Metric Spaces. CoRR Omar Darwiche Domingues, Pierre Ménard, Matteo Pirotta, Emilie Kaufmann, Michal Valko
2020	J	`jnl`	A Provably Efficient Sample Collection Strategy for Reinforcement Learning. CoRR Jean Tarbouriech, Matteo Pirotta, Michal Valko, Alessandro Lazaric
2020	A	`conf`	A single algorithm for both restless and rested rotting bandits. AISTATS Julien Seznec, Pierre Ménard, Alessandro Lazaric, Michal Valko
2020	J	`jnl`	Adaptive Reward-Free Exploration. CoRR Emilie Kaufmann, Pierre Ménard, Omar Darwiche Domingues, Anders Jonsson, Edouard Leurent, Michal Valko
2020	A	`conf`	Adaptive multi-fidelity optimization with fast learning rates. AISTATS Côme Fiegel, Victor Gabillon, Michal Valko
2020	J	`jnl`	BYOL works even without batch statistics. CoRR 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
2020	A*	`conf`	Bootstrap Your Own Latent - A New Approach to Self-Supervised Learning. NeurIPS 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
2020	J	`jnl`	Bootstrap Your Own Latent: A New Approach to Self-Supervised Learning. CoRR 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
2020	A*	`conf`	Budgeted Online Influence Maximization. ICML Pierre Perrault, Jennifer Healey, Zheng Wen, Michal Valko
2020	A*	`conf`	Covariance-adapting algorithm for semi-bandits with application to sparse outcomes. COLT Pierre Perrault, Michal Valko, Vianney Perchet
2020	A	`conf`	Derivative-Free & Order-Robust Optimisation. AISTATS Haitham Ammar, Victor Gabillon, Rasul Tutunov, Michal Valko
2020	J	`jnl`	Episodic Reinforcement Learning in Finite MDPs: Minimax Lower Bounds Revisited. CoRR Omar Darwiche Domingues, Pierre Ménard, Emilie Kaufmann, Michal Valko
2020	J	`jnl`	Fast active learning for pure exploration in reinforcement learning. CoRR Pierre Ménard, Omar Darwiche Domingues, Anders Jonsson, Emilie Kaufmann, Edouard Leurent, Michal Valko
2020	A	`conf`	Fixed-confidence guarantees for Bayesian best-arm identification. AISTATS Xuedong Shang, Rianne de Heide, Pierre Ménard, Emilie Kaufmann, Michal Valko
2020	J	`jnl`	Game Plan: What AI can do for Football, and What Football can do for AI. CoRR 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
2020	A*	`conf`	Gamification of Pure Exploration for Linear Bandits. ICML Rémy Degenne, Pierre Ménard, Xuedong Shang, Michal Valko
2020	J	`jnl`	Gamification of Pure Exploration for Linear Bandits. CoRR Rémy Degenne, Pierre Ménard, Xuedong Shang, Michal Valko
2020	A*	`conf`	Improved Sample Complexity for Incremental Autonomous Exploration in MDPs. NeurIPS Jean Tarbouriech, Matteo Pirotta, Michal Valko, Alessandro Lazaric
2020	J	`jnl`	Improved Sample Complexity for Incremental Autonomous Exploration in MDPs. CoRR Jean Tarbouriech, Matteo Pirotta, Michal Valko, Alessandro Lazaric
2020	A*	`conf`	Improved Sleeping Bandits with Stochastic Action Sets and Adversarial Rewards. ICML Aadirupa Saha, Pierre Gaillard, Michal Valko
2020	J	`jnl`	Improved Sleeping Bandits with Stochastic Actions Sets and Adversarial Rewards. CoRR Aadirupa Saha, Pierre Gaillard, Michal Valko
2020	A*	`conf`	Monte-Carlo Tree Search as Regularized Policy Optimization. ICML Jean-Bastien Grill, Florent Altché, Yunhao Tang, Thomas Hubert, Michal Valko, Ioannis Antonoglou, Rémi Munos
2020	J	`jnl`	Monte-Carlo Tree Search as Regularized Policy Optimization. CoRR Jean-Bastien Grill, Florent Altché, Yunhao Tang, Thomas Hubert, Michal Valko, Ioannis Antonoglou, Rémi Munos
2020	J	`jnl`	Near-linear Time Gaussian Process Optimization with Adaptive Batching and Resparsification. CoRR Daniele Calandriello, Luigi Carratino, Alessandro Lazaric, Michal Valko, Lorenzo Rosasco
2020	A*	`conf`	Near-linear time Gaussian process optimization with adaptive batching and resparsification. ICML Daniele Calandriello, Luigi Carratino, Alessandro Lazaric, Michal Valko, Lorenzo Rosasco
2020	A*	`conf`	No-Regret Exploration in Goal-Oriented Reinforcement Learning. ICML Jean Tarbouriech, Evrard Garcelon, Michal Valko, Matteo Pirotta, Alessandro Lazaric
2020	A*	`conf`	Planning in Markov Decision Processes with Gap-Dependent Sample Complexity. NeurIPS Anders Jonsson, Emilie Kaufmann, Pierre Ménard, Omar Darwiche Domingues, Edouard Leurent, Michal Valko
2020	J	`jnl`	Planning in Markov Decision Processes with Gap-Dependent Sample Complexity. CoRR Anders Jonsson, Emilie Kaufmann, Pierre Ménard, Omar Darwiche Domingues, Edouard Leurent, Michal Valko
2020	J	`jnl`	Regret Bounds for Kernel-Based Reinforcement Learning. CoRR Omar Darwiche Domingues, Pierre Ménard, Matteo Pirotta, Emilie Kaufmann, Michal Valko
2020	A*	`conf`	Sampling from a k-DPP without looking at all items. NeurIPS Daniele Calandriello, Michal Derezinski, Michal Valko
2020	J	`jnl`	Sampling from a k-DPP without looking at all items. CoRR Daniele Calandriello, Michal Derezinski, Michal Valko
2020	A*	`conf`	Statistical Efficiency of Thompson Sampling for Combinatorial Semi-Bandits. NeurIPS Pierre Perrault, Etienne Boursier, Michal Valko, Vianney Perchet
2020	J	`jnl`	Statistical Efficiency of Thompson Sampling for Combinatorial Semi-Bandits. CoRR Pierre Perrault, Etienne Boursier, Vianney Perchet, Michal Valko
2020	A*	`conf`	Stochastic bandits with arm-dependent delays. ICML Anne Gael Manegueu, Claire Vernade, Alexandra Carpentier, Michal Valko
2020	J	`jnl`	Stochastic bandits with arm-dependent delays. CoRR Anne Gael Manegueu, Claire Vernade, Alexandra Carpentier, Michal Valko
2020	A*	`conf`	Taylor Expansion Policy Optimization. ICML Yunhao Tang, Michal Valko, Rémi Munos
2020	J	`jnl`	Taylor Expansion Policy Optimization. CoRR Yunhao Tang, Michal Valko, Rémi Munos
2019	B	`conf`	A simple parameter-free and adaptive approach to optimization under a minimal local smoothness assumption. ALT Peter L. Bartlett, Victor Gabillon, Michal Valko
2019	A	`conf`	Active multiple matrix completion with adaptive confidence sets. AISTATS Andrea Locatelli, Alexandra Carpentier, Michal Valko
2019	J	`jnl`	DPPy: DPP Sampling with Python. J. Mach. Learn. Res. Guillaume Gautier, Guillermo Polito, Rémi Bardenet, Michal Valko
2019	J	`jnl`	Derivative-Free & Order-Robust Optimisation. CoRR Victor Gabillon, Rasul Tutunov, Michal Valko, Haitham Bou-Ammar
2019	A*	`conf`	Exact sampling of determinantal point processes with sublinear time preprocessing. NeurIPS Michal Derezinski, Daniele Calandriello, Michal Valko
2019	J	`jnl`	Exact sampling of determinantal point processes with sublinear time preprocessing. CoRR Michal Derezinski, Daniele Calandriello, Michal Valko
2019	J	`jnl`	Exploiting Structure of Uncertainty for Efficient Combinatorial Semi-Bandits. CoRR Pierre Perrault, Vianney Perchet, Michal Valko
2019	A*	`conf`	Exploiting structure of uncertainty for efficient matroid semi-bandits. ICML Pierre Perrault, Vianney Perchet, Michal Valko
2019	A	`conf`	Finding the bandit in a graph: Sequential search-and-stop. AISTATS Pierre Perrault, Vianney Perchet, Michal Valko
2019	J	`jnl`	Fixed-Confidence Guarantees for Bayesian Best-Arm Identification. CoRR Xuedong Shang, Rianne de Heide, Emilie Kaufmann, Pierre Ménard, Michal Valko
2019	A*	`conf`	Gaussian Process Optimization with Adaptive Sketching: Scalable and No Regret. COLT Daniele Calandriello, Luigi Carratino, Alessandro Lazaric, Michal Valko, Lorenzo Rosasco
2019	J	`jnl`	Gaussian Process Optimization with Adaptive Sketching: Scalable and No Regret. CoRR Daniele Calandriello, Luigi Carratino, Alessandro Lazaric, Michal Valko, Lorenzo Rosasco
2019	B	`conf`	General parallel optimization a without metric. ALT Xuedong Shang, Emilie Kaufmann, Michal Valko
2019	A*	`conf`	Multiagent Evaluation under Incomplete Information. NeurIPS Mark Rowland, Shayegan Omidshafiei, Karl Tuyls, Julien Pérolat, Michal Valko, Georgios Piliouras, Rémi Munos
2019	J	`jnl`	Multiagent Evaluation under Incomplete Information. CoRR Mark Rowland, Shayegan Omidshafiei, Karl Tuyls, Julien Pérolat, Michal Valko, Georgios Piliouras, Rémi Munos
2019	J	`jnl`	No-Regret Exploration in Goal-Oriented Reinforcement Learning. CoRR Jean Tarbouriech, Evrard Garcelon, Michal Valko, Matteo Pirotta, Alessandro Lazaric
2019	A*	`conf`	On two ways to use determinantal point processes for Monte Carlo integration. NeurIPS Guillaume Gautier, Rémi Bardenet, Michal Valko
2019	J	`jnl`	Optimistic optimization of a Brownian. CoRR Jean-Bastien Grill, Michal Valko, Rémi Munos
2019	A*	`conf`	Planning in entropy-regularized Markov decision processes and games. NeurIPS Jean-Bastien Grill, Omar Darwiche Domingues, Pierre Ménard, Rémi Munos, Michal Valko
2019	A	`conf`	Rotting bandits are no harder than stochastic ones. AISTATS Julien Seznec, Andrea Locatelli, Alexandra Carpentier, Alessandro Lazaric, Michal Valko
2019	A*	`conf`	Scale-free adaptive planning for deterministic dynamics & discounted rewards. ICML Peter L. Bartlett, Victor Gabillon, Jennifer Healey, Michal Valko
2018	J	`jnl`	A simple parameter-free and adaptive approach to optimization under a minimal local smoothness assumption. CoRR Peter L. Bartlett, Victor Gabillon, Michal Valko
2018	A*	`conf`	Best of both worlds: Stochastic & adversarial best-arm identification. COLT Yasin Abbasi-Yadkori, Peter L. Bartlett, Victor Gabillon, Alan Malek, Michal Valko
2018	—	`conf`	Compressing the Input for CNNs with the First-Order Scattering Transform. ECCV (9) Edouard Oyallon, Eugene Belilovsky, Sergey Zagoruyko, Michal Valko
2018	J	`jnl`	Compressing the Input for CNNs with the First-Order Scattering Transform. CoRR Edouard Oyallon, Eugene Belilovsky, Sergey Zagoruyko, Michal Valko
2018	J	`jnl`	DPPy: Sampling Determinantal Point Processes with Python. CoRR Guillaume Gautier, Rémi Bardenet, Michal Valko
2018	J	`jnl`	Distributed Adaptive Sampling for Kernel Matrix Approximation. CoRR Daniele Calandriello, Alessandro Lazaric, Michal Valko
2018	J	`jnl`	Finding the Bandit in a Graph: Sequential Search-and-Stop. CoRR Pierre Perrault, Vianney Perchet, Michal Valko
2018	A*	`conf`	Improved Large-Scale Graph Learning through Ridge Spectral Sparsification. ICML Daniele Calandriello, Ioannis Koutis, Alessandro Lazaric, Michal Valko
2018	A*	`conf`	Optimistic optimization of a Brownian. NeurIPS Jean-Bastien Grill, Michal Valko, Rémi Munos
2018	—	`conf`	Preface. ITS Workshops Fabrice Popineau, Michal Valko, Jill-Jênn Vie
2018	J	`jnl`	Rotting bandits are no harder than stochastic ones. CoRR Julien Seznec, Andrea Locatelli, Alexandra Carpentier, Alessandro Lazaric, Michal Valko
2017	A	`conf`	Distributed Adaptive Sampling for Kernel Matrix Approximation. AISTATS Daniele Calandriello, Alessandro Lazaric, Michal Valko
2017	—	`conf`	Efficient Second-Order Online Kernel Learning with Adaptive Embedding. NIPS Daniele Calandriello, Alessandro Lazaric, Michal Valko
2017	—	`conf`	Online Influence Maximization under Independent Cascade Model with Semi-Bandit Feedback. NIPS Zheng Wen, Branislav Kveton, Michal Valko, Sharan Vaswani
2017	A*	`conf`	Second-Order Kernel Online Convex Optimization with Adaptive Sketching. ICML Daniele Calandriello, Alessandro Lazaric, Michal Valko
2017	J	`jnl`	Second-Order Kernel Online Convex Optimization with Adaptive Sketching. CoRR Daniele Calandriello, Alessandro Lazaric, Michal Valko
2017	A	`conf`	Trading off Rewards and Errors in Multi-Armed Bandits. AISTATS Akram Erraqabi, Alessandro Lazaric, Michal Valko, Emma Brunskill, Yun-En Liu
2017	A*	`conf`	Zonotope Hit-and-run for Efficient Sampling from Projection DPPs. ICML Guillaume Gautier, Rémi Bardenet, Michal Valko
2017	J	`jnl`	Zonotope hit-and-run for efficient sampling from projection DPPs. CoRR Guillaume Gautier, Rémi Bardenet, Michal Valko
2016	J	`jnl`	Analysis of Kelner and Levin graph sparsification algorithm for a streaming setting. CoRR Daniele Calandriello, Alessandro Lazaric, Michal Valko
2016	A	`conf`	Analysis of Nyström method with sequential ridge leverage scores. UAI Daniele Calandriello, Alessandro Lazaric, Michal Valko
2016	J	`jnl`	Bayesian Policy Gradient and Actor-Critic Algorithms. J. Mach. Learn. Res. Mohammad Ghavamzadeh, Yaakov Engel, Michal Valko
2016	—	`conf`	Blazing the trails before beating the path: Sample-efficient Monte-Carlo planning. NIPS Jean-Bastien Grill, Michal Valko, Rémi Munos
2016	J	`jnl`	Incremental Spectral Sparsification for Large-Scale Graph-Based Semi-Supervised Learning. CoRR Daniele Calandriello, Alessandro Lazaric, Michal Valko, Ioannis Koutis
2016	J	`jnl`	Influence Maximization with Semi-Bandit Feedback. CoRR Zheng Wen, Branislav Kveton, Michal Valko
2016	A	`conf`	Online Learning with Noisy Side Observations. AISTATS Tomás Kocák, Gergely Neu, Michal Valko
2016	A	`conf`	Online learning with Erdos-Renyi side-observation graphs. UAI Tomás Kocák, Gergely Neu, Michal Valko
2016	A*	`conf`	Pliable Rejection Sampling. ICML Akram Erraqabi, Michal Valko, Alexandra Carpentier, Odalric-Ambrym Maillard
2016	A	`conf`	Revealing Graph Bandits for Maximizing Local Influence. AISTATS Alexandra Carpentier, Michal Valko
2015	—	`conf`	Black-box optimization of noisy functions with unknown smoothness. NIPS Jean-Bastien Grill, Michal Valko, Rémi Munos
2015	A*	`conf`	Cheap Bandits. ICML Manjesh Kumar Hanawal, Venkatesh Saligrama, Michal Valko, Rémi Munos
2015	J	`jnl`	Cheap Bandits. CoRR Manjesh Kumar Hanawal, Venkatesh Saligrama, Michal Valko, Rémi Munos
2015	A*	`conf`	Maximum Entropy Semi-Supervised Inverse Reinforcement Learning. IJCAI Julien Audiffren, Michal Valko, Alessandro Lazaric, Mohammad Ghavamzadeh
2015	A*	`conf`	Simple regret for infinitely many armed bandits. ICML Alexandra Carpentier, Michal Valko
2015	J	`jnl`	Simple regret for infinitely many armed bandits. CoRR Alexandra Carpentier, Michal Valko
2014	B	`conf`	Bandits attack function optimization. IEEE Congress on Evolutionary Computation Philippe Preux, Rémi Munos, Michal Valko
2014	—	`conf`	Efficient learning by implicit exploration in bandit problems with side observations. NIPS Tomás Kocák, Gergely Neu, Michal Valko, Rémi Munos
2014	—	`conf`	Extreme bandits. NIPS Alexandra Carpentier, Michal Valko
2014	J	`jnl`	Learning to Act Greedily: Polymatroid Semi-Bandits. CoRR Branislav Kveton, Zheng Wen, Azin Ashkan, Michal Valko
2014	—	`conf`	Online combinatorial optimization with stochastic decision sets and adversarial losses. NIPS Gergely Neu, Michal Valko
2014	A*	`conf`	Spectral Bandits for Smooth Graph Functions. ICML Michal Valko, Rémi Munos, Branislav Kveton, Tomás Kocák
2014	A*	`conf`	Spectral Thompson Sampling. AAAI Tomás Kocák, Michal Valko, Rémi Munos, Shipra Agrawal
2013	A	`conf`	Finite-Time Analysis of Kernelised Contextual Bandits. UAI Michal Valko, Nathaniel Korda, Rémi Munos, Ilias N. Flaounas, Nello Cristianini
2013	J	`jnl`	Finite-Time Analysis of Kernelised Contextual Bandits. CoRR Michal Valko, Nathaniel Korda, Rémi Munos, Ilias N. Flaounas, Nello Cristianini
2013	B	`conf`	Learning from a single labeled face and a stream of unlabeled data. FG Branislav Kveton, Michal Valko
2013	J	`jnl`	Outlier detection for patient monitoring and alerting. J. Biomed. Informatics Milos Hauskrecht, Iyad Batal, Michal Valko, Shyam Visweswaran, Gregory F. Cooper, Gilles Clermont
2013	—	`conf`	Stochastic Simultaneous Optimistic Optimization. ICML (2) Michal Valko, Alexandra Carpentier, Rémi Munos
2012	J	`jnl`	Online Semi-Supervised Learning on Quantized Graphs CoRR Michal Valko, Branislav Kveton, Ling Huang, Daniel Ting
2012	—	`conf`	Semi-Supervised Apprenticeship Learning. EWRL Michal Valko, Mohammad Ghavamzadeh, Alessandro Lazaric
2011	A*	`conf`	Conditional Anomaly Detection with Soft Harmonic Functions. ICDM Michal Valko, Branislav Kveton, Hamed Valizadegan, Gregory F. Cooper, Milos Hauskrecht
2010	—	`conf`	Feature importance analysis for patient management decisions. MedInfo Michal Valko, Milos Hauskrecht
2010	A	`conf`	Online Semi-Supervised Learning on Quantized Graphs. UAI Michal Valko, Branislav Kveton, Ling Huang, Daniel Ting
2010	—	`conf`	Online semi-supervised perception: Real-time learning without explicit feedback. CVPR Workshops Branislav Kveton, Matthai Philipose, Michal Valko, Ling Huang
2010	A	`conf`	Semi-Supervised Learning with Max-Margin Graph Cuts. AISTATS Branislav Kveton, Michal Valko, Ali Rahimi, Ling Huang
2008	Misc	`conf`	Distance Metric Learning for Conditional Anomaly Detection. FLAIRS Michal Valko, Milos Hauskrecht
2007	Misc	`conf`	Evidence-based Anomaly Detection in Clinical Domains. AMIA Milos Hauskrecht, Michal Valko, Branislav Kveton, Shyam Visweswaran, Gregory F. Cooper