Timezone: »
Author Information
Daniil Tiapkin (HSE University)
Denis Belomestny (Universitaet Duisburg-Essen)
Eric Moulines (Ecole Polytechnique)
Alexey Naumov (National Research University Higher School of Economics)
Sergey Samsonov (National Research University Higher School of Economics)
Yunhao Tang (DeepMind)
Michal Valko (DeepMind / Inria / ENS Paris-Saclay)
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.
Pierre Menard (OvGU)
Related Events (a corresponding poster, oral, or spotlight)
-
2022 Oral: From Dirichlet to Rubin: Optimistic Exploration in RL without Bonuses »
Thu. Jul 21st 07:30 -- 07:50 PM Room Room 309
More from the Same Authors
-
2021 : Marginalized Operators for Off-Policy Reinforcement Learning »
Yunhao Tang · Mark Rowland · Remi Munos · Michal Valko -
2021 : Model-Free Approach to Evaluate Reinforcement Learning Algorithms »
Denis Belomestny · Ilya Levin · Eric Moulines · Alexey Naumov · Sergey Samsonov · Veronika Zorina -
2021 : Stochastic Shortest Path: Minimax, Parameter-Free and Towards Horizon-Free Regret »
Jean Tarbouriech · Jean Tarbouriech · Simon Du · Matteo Pirotta · Michal Valko · Alessandro Lazaric -
2021 : Density-Based Bonuses on Learned Representations for Reward-Free Exploration in Deep Reinforcement Learning »
Omar Darwiche Domingues · Corentin Tallec · Remi Munos · Michal Valko -
2023 : Balanced Training of Energy-Based Models with Adaptive Flow Sampling »
Louis Grenioux · Eric Moulines · Marylou Gabrié -
2023 Poster: Understanding Self-Predictive Learning for Reinforcement Learning »
Yunhao Tang · Zhaohan Guo · Pierre Richemond · Bernardo Avila Pires · Yash Chandak · Remi Munos · Mark Rowland · Mohammad Gheshlaghi Azar · Charline Le Lan · Clare Lyle · Andras Gyorgy · Shantanu Thakoor · Will Dabney · Bilal Piot · Daniele Calandriello · Michal Valko -
2023 Poster: Half-Hop: A graph upsampling approach for slowing down message passing »
Mehdi Azabou · Venkataramana Ganesh · Shantanu Thakoor · Chi-Heng Lin · Lakshmi Sathidevi · Ran Liu · Michal Valko · Petar Veličković · Eva Dyer -
2023 Poster: Curiosity in Hindsight: Intrinsic Exploration in Stochastic Environments »
Daniel Jarrett · Corentin Tallec · Florent Altché · Thomas Mesnard · Remi Munos · Michal Valko -
2023 Poster: Representations and Exploration for Deep Reinforcement Learning using Singular Value Decomposition »
Yash Chandak · Shantanu Thakoor · Zhaohan Guo · Yunhao Tang · Remi Munos · Will Dabney · Diana Borsa -
2023 Poster: Towards a better understanding of representation dynamics under TD-learning »
Yunhao Tang · Remi Munos -
2023 Oral: Adapting to game trees in zero-sum imperfect information games »
Côme Fiegel · Pierre Menard · Tadashi Kozuno · Remi Munos · Vianney Perchet · Michal Valko -
2023 Poster: Conformal Prediction for Federated Uncertainty Quantification Under Label Shift »
Vincent Plassier · Mehdi Makni · Aleksandr Rubashevskii · Eric Moulines · Maxim Panov -
2023 Poster: Adapting to game trees in zero-sum imperfect information games »
Côme Fiegel · Pierre Menard · Tadashi Kozuno · Remi Munos · Vianney Perchet · Michal Valko -
2023 Poster: Fast Rates for Maximum Entropy Exploration »
Daniil Tiapkin · Denis Belomestny · Daniele Calandriello · Eric Moulines · Remi Munos · Alexey Naumov · Pierre Perrault · Yunhao Tang · Michal Valko · Pierre Menard -
2023 Poster: On Sampling with Approximate Transport Maps »
Louis Grenioux · Alain Oliviero Durmus · Eric Moulines · Marylou Gabrié -
2023 Oral: Quantile Credit Assignment »
Thomas Mesnard · Wenqi Chen · Alaa Saade · Yunhao Tang · Mark Rowland · Theophane Weber · Clare Lyle · Audrunas Gruslys · Michal Valko · Will Dabney · Georg Ostrovski · Eric Moulines · Remi Munos -
2023 Poster: The Statistical Benefits of Quantile Temporal-Difference Learning for Value Estimation »
Mark Rowland · Yunhao Tang · Clare Lyle · Remi Munos · Marc Bellemare · Will Dabney -
2023 Poster: Quantile Credit Assignment »
Thomas Mesnard · Wenqi Chen · Alaa Saade · Yunhao Tang · Mark Rowland · Theophane Weber · Clare Lyle · Audrunas Gruslys · Michal Valko · Will Dabney · Georg Ostrovski · Eric Moulines · Remi Munos -
2023 Poster: DoMo-AC: Doubly Multi-step Off-policy Actor-Critic Algorithm »
Yunhao Tang · Tadashi Kozuno · Mark Rowland · Anna Harutyunyan · Remi Munos · Bernardo Avila Pires · Michal Valko -
2023 Poster: The Edge of Orthogonality: A Simple View of What Makes BYOL Tick »
Pierre Richemond · Allison Tam · Yunhao Tang · Florian Strub · Bilal Piot · Feilx Hill -
2023 Poster: State and parameter learning with PARIS particle Gibbs »
Gabriel Cardoso · Yazid Janati el idrissi · Sylvain Le Corff · Eric Moulines · Jimmy Olsson -
2023 Poster: VA-learning as a more efficient alternative to Q-learning »
Yunhao Tang · Remi Munos · Mark Rowland · Michal Valko -
2023 Poster: Regularization and Variance-Weighted Regression Achieves Minimax Optimality in Linear MDPs: Theory and Practice »
Toshinori Kitamura · Tadashi Kozuno · Yunhao Tang · Nino Vieillard · Michal Valko · Wenhao Yang · Jincheng Mei · Pierre Menard · Mohammad Gheshlaghi Azar · Remi Munos · Olivier Pietquin · Matthieu Geist · Csaba Szepesvari · Wataru Kumagai · Yutaka Matsuo -
2022 Poster: Retrieval-Augmented Reinforcement Learning »
Anirudh Goyal · Abe Friesen Friesen · Andrea Banino · Theophane Weber · Nan Rosemary Ke · Adrià Puigdomenech Badia · Arthur Guez · Mehdi Mirza · Peter Humphreys · Ksenia Konyushkova · Michal Valko · Simon Osindero · Timothy Lillicrap · Nicolas Heess · Charles Blundell -
2022 Spotlight: Retrieval-Augmented Reinforcement Learning »
Anirudh Goyal · Abe Friesen Friesen · Andrea Banino · Theophane Weber · Nan Rosemary Ke · Adrià Puigdomenech Badia · Arthur Guez · Mehdi Mirza · Peter Humphreys · Ksenia Konyushkova · Michal Valko · Simon Osindero · Timothy Lillicrap · Nicolas Heess · Charles Blundell -
2022 Poster: Biased Gradient Estimate with Drastic Variance Reduction for Meta Reinforcement Learning »
Yunhao Tang -
2022 Spotlight: Biased Gradient Estimate with Drastic Variance Reduction for Meta Reinforcement Learning »
Yunhao Tang -
2022 Poster: Scaling Gaussian Process Optimization by Evaluating a Few Unique Candidates Multiple Times »
Daniele Calandriello · Luigi Carratino · Alessandro Lazaric · Michal Valko · Lorenzo Rosasco -
2022 Poster: Diffusion bridges vector quantized variational autoencoders »
Max Cohen · Guillaume QUISPE · Sylvain Le Corff · Charles Ollion · Eric Moulines -
2022 Spotlight: Diffusion bridges vector quantized variational autoencoders »
Max Cohen · Guillaume QUISPE · Sylvain Le Corff · Charles Ollion · Eric Moulines -
2022 Spotlight: Scaling Gaussian Process Optimization by Evaluating a Few Unique Candidates Multiple Times »
Daniele Calandriello · Luigi Carratino · Alessandro Lazaric · Michal Valko · Lorenzo Rosasco -
2021 Poster: Monte Carlo Variational Auto-Encoders »
Achille Thin · Nikita Kotelevskii · Arnaud Doucet · Alain Durmus · Eric Moulines · Maxim Panov -
2021 Poster: Problem Dependent View on Structured Thresholding Bandit Problems »
James Cheshire · Pierre Menard · Alexandra Carpentier -
2021 Spotlight: Problem Dependent View on Structured Thresholding Bandit Problems »
James Cheshire · Pierre Menard · Alexandra Carpentier -
2021 Spotlight: Monte Carlo Variational Auto-Encoders »
Achille Thin · Nikita Kotelevskii · Arnaud Doucet · Alain Durmus · Eric Moulines · Maxim Panov -
2021 Poster: DG-LMC: A Turn-key and Scalable Synchronous Distributed MCMC Algorithm via Langevin Monte Carlo within Gibbs »
Vincent Plassier · Maxime Vono · Alain Durmus · Eric Moulines -
2021 Oral: DG-LMC: A Turn-key and Scalable Synchronous Distributed MCMC Algorithm via Langevin Monte Carlo within Gibbs »
Vincent Plassier · Maxime Vono · Alain Durmus · Eric Moulines -
2021 Poster: Fast active learning for pure exploration in reinforcement learning »
Pierre Menard · Omar Darwiche Domingues · Anders Jonsson · Emilie Kaufmann · Edouard Leurent · Michal Valko -
2021 Poster: UCB Momentum Q-learning: Correcting the bias without forgetting »
Pierre Menard · Omar Darwiche Domingues · Xuedong Shang · Michal Valko -
2021 Spotlight: Fast active learning for pure exploration in reinforcement learning »
Pierre Menard · Omar Darwiche Domingues · Anders Jonsson · Emilie Kaufmann · Edouard Leurent · Michal Valko -
2021 Oral: UCB Momentum Q-learning: Correcting the bias without forgetting »
Pierre Menard · Omar Darwiche Domingues · Xuedong Shang · Michal Valko -
2021 Poster: Kernel-Based Reinforcement Learning: A Finite-Time Analysis »
Omar Darwiche Domingues · Pierre Menard · Matteo Pirotta · Emilie Kaufmann · Michal Valko -
2021 Poster: Online A-Optimal Design and Active Linear Regression »
Xavier Fontaine · Pierre Perrault · Michal Valko · Vianney Perchet -
2021 Spotlight: Kernel-Based Reinforcement Learning: A Finite-Time Analysis »
Omar Darwiche Domingues · Pierre Menard · Matteo Pirotta · Emilie Kaufmann · Michal Valko -
2021 Spotlight: Online A-Optimal Design and Active Linear Regression »
Xavier Fontaine · Pierre Perrault · Michal Valko · Vianney Perchet -
2021 Poster: Revisiting Peng's Q($\lambda$) for Modern Reinforcement Learning »
Tadashi Kozuno · Yunhao Tang · Mark Rowland · Remi Munos · Steven Kapturowski · Will Dabney · Michal Valko · David Abel -
2021 Poster: Taylor Expansion of Discount Factors »
Yunhao Tang · Mark Rowland · Remi Munos · Michal Valko -
2021 Spotlight: Taylor Expansion of Discount Factors »
Yunhao Tang · Mark Rowland · Remi Munos · Michal Valko -
2021 Spotlight: Revisiting Peng's Q($\lambda$) for Modern Reinforcement Learning »
Tadashi Kozuno · Yunhao Tang · Mark Rowland · Remi Munos · Steven Kapturowski · Will Dabney · Michal Valko · David Abel -
2021 Poster: Counterfactual Credit Assignment in Model-Free Reinforcement Learning »
Thomas Mesnard · Theophane Weber · Fabio Viola · Shantanu Thakoor · Alaa Saade · Anna Harutyunyan · Will Dabney · Thomas Stepleton · Nicolas Heess · Arthur Guez · Eric Moulines · Marcus Hutter · Lars Buesing · Remi Munos -
2021 Spotlight: Counterfactual Credit Assignment in Model-Free Reinforcement Learning »
Thomas Mesnard · Theophane Weber · Fabio Viola · Shantanu Thakoor · Alaa Saade · Anna Harutyunyan · Will Dabney · Thomas Stepleton · Nicolas Heess · Arthur Guez · Eric Moulines · Marcus Hutter · Lars Buesing · Remi Munos -
2020 Poster: Monte-Carlo Tree Search as Regularized Policy Optimization »
Jean-Bastien Grill · Florent Altché · Yunhao Tang · Thomas Hubert · Michal Valko · Ioannis Antonoglou · Remi Munos -
2020 Poster: Improved Sleeping Bandits with Stochastic Action Sets and Adversarial Rewards »
Aadirupa Saha · Pierre Gaillard · Michal Valko -
2020 Poster: No-Regret Exploration in Goal-Oriented Reinforcement Learning »
Jean Tarbouriech · Evrard Garcelon · Michal Valko · Matteo Pirotta · Alessandro Lazaric -
2020 Poster: Gamification of Pure Exploration for Linear Bandits »
Rémy Degenne · Pierre Menard · Xuedong Shang · Michal Valko -
2020 Poster: Stochastic bandits with arm-dependent delays »
Anne Gael Manegueu · Claire Vernade · Alexandra Carpentier · Michal Valko -
2020 Poster: Fast and Consistent Learning of Hidden Markov Models by Incorporating Non-Consecutive Correlations »
Robert Mattila · Cristian R. Rojas · Eric Moulines · Vikram Krishnamurthy · Bo Wahlberg -
2020 Poster: Learning to Score Behaviors for Guided Policy Optimization »
Aldo Pacchiano · Jack Parker-Holder · Yunhao Tang · Krzysztof Choromanski · Anna Choromanska · Michael Jordan -
2020 Poster: Budgeted Online Influence Maximization »
Pierre Perrault · Jennifer Healey · Zheng Wen · Michal Valko -
2020 Poster: Reinforcement Learning for Integer Programming: Learning to Cut »
Yunhao Tang · Shipra Agrawal · Yuri Faenza -
2020 Poster: Near-linear time Gaussian process optimization with adaptive batching and resparsification »
Daniele Calandriello · Luigi Carratino · Alessandro Lazaric · Michal Valko · Lorenzo Rosasco -
2020 Poster: Taylor Expansion Policy Optimization »
Yunhao Tang · Michal Valko · Remi Munos -
2019 : poster session I »
Nicholas Rhinehart · Yunhao Tang · Vinay Prabhu · Dian Ang Yap · Alexander Wang · Marc Finzi · Manoj Kumar · You Lu · Abhishek Kumar · Qi Lei · Michael Przystupa · Nicola De Cao · Polina Kirichenko · Pavel Izmailov · Andrew Wilson · Jakob Kruse · Diego Mesquita · Mario Lezcano Casado · Thomas Müller · Keir Simmons · Andrei Atanov -
2019 : Gaussian Process Optimization with Adaptive Sketching: Scalable and No Regret »
Michal Valko -
2019 : Michal Valko: How Negative Dependence Broke the Quadratic Barrier for Learning with Graphs and Kernels »
Michal Valko -
2019 : Poster Session 1 (all papers) »
Matilde Gargiani · Yochai Zur · Chaim Baskin · Evgenii Zheltonozhskii · Liam Li · Ameet Talwalkar · Xuedong Shang · Harkirat Singh Behl · Atilim Gunes Baydin · Ivo Couckuyt · Tom Dhaene · Chieh Lin · Wei Wei · Min Sun · Orchid Majumder · Michele Donini · Yoshihiko Ozaki · Ryan P. Adams · Christian Geißler · Ping Luo · zhanglin peng · · Ruimao Zhang · John Langford · Rich Caruana · Debadeepta Dey · Charles Weill · Xavi Gonzalvo · Scott Yang · Scott Yak · Eugen Hotaj · Vladimir Macko · Mehryar Mohri · Corinna Cortes · Stefan Webb · Jonathan Chen · Martin Jankowiak · Noah Goodman · Aaron Klein · Frank Hutter · Mojan Javaheripi · Mohammad Samragh · Sungbin Lim · Taesup Kim · SUNGWOONG KIM · Michael Volpp · Iddo Drori · Yamuna Krishnamurthy · Kyunghyun Cho · Stanislaw Jastrzebski · Quentin de Laroussilhe · Mingxing Tan · Xiao Ma · Neil Houlsby · Andrea Gesmundo · Zalán Borsos · Krzysztof Maziarz · Felipe Petroski Such · Joel Lehman · Kenneth Stanley · Jeff Clune · Pieter Gijsbers · Joaquin Vanschoren · Felix Mohr · Eyke Hüllermeier · Zheng Xiong · Wenpeng Zhang · Wenwu Zhu · Weijia Shao · Aleksandra Faust · Michal Valko · Michael Y Li · Hugo Jair Escalante · Marcel Wever · Andrey Khorlin · Tara Javidi · Anthony Francis · Saurajit Mukherjee · Jungtaek Kim · Michael McCourt · Saehoon Kim · Tackgeun You · Seungjin Choi · Nicolas Knudde · Alexander Tornede · Ghassen Jerfel -
2019 Poster: Exploiting structure of uncertainty for efficient matroid semi-bandits »
Pierre Perrault · Vianney Perchet · Michal Valko -
2019 Poster: Scale-free adaptive planning for deterministic dynamics & discounted rewards »
Peter Bartlett · Victor Gabillon · Jennifer Healey · Michal Valko -
2019 Oral: Exploiting structure of uncertainty for efficient matroid semi-bandits »
Pierre Perrault · Vianney Perchet · Michal Valko -
2019 Oral: Scale-free adaptive planning for deterministic dynamics & discounted rewards »
Peter Bartlett · Victor Gabillon · Jennifer Healey · Michal Valko -
2018 Poster: Improved large-scale graph learning through ridge spectral sparsification »
Daniele Calandriello · Alessandro Lazaric · Ioannis Koutis · Michal Valko -
2018 Oral: Improved large-scale graph learning through ridge spectral sparsification »
Daniele Calandriello · Alessandro Lazaric · Ioannis Koutis · Michal Valko -
2017 : Faster graph bandit learning using information about the neighbors »
Michal Valko -
2017 Poster: Zonotope hit-and-run for efficient sampling from projection DPPs »
Guillaume Gautier · Rémi Bardenet · Michal Valko -
2017 Talk: Zonotope hit-and-run for efficient sampling from projection DPPs »
Guillaume Gautier · Rémi Bardenet · Michal Valko -
2017 Poster: Second-Order Kernel Online Convex Optimization with Adaptive Sketching »
Daniele Calandriello · Alessandro Lazaric · Michal Valko -
2017 Talk: Second-Order Kernel Online Convex Optimization with Adaptive Sketching »
Daniele Calandriello · Alessandro Lazaric · Michal Valko