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Author Information
Aadirupa Saha (Indian Institute of Science (IISc), Bangalore)
Bio: Aadirupa Saha is currently a visiting faculty at Toyota Technological Institute at Chicago (TTIC). She obtained her PhD from the Department of Computer Science, Indian Institute of Science, Bangalore, advised by Aditya Gopalan and Chiranjib Bhattacharyya. She spent two years at Microsoft Research New York City as a postdoctoral researcher. During her PhD, Aadirupa interned at Microsoft Research, Bangalore, Inria, Paris, and Google AI, Mountain View. Her research interests include Bandits, Reinforcement Learning, Optimization, Learning theory, Algorithms. She has organized various workshops, tutorials and also served as a reviewer in top ML conferences. Research Interests: Machine Learning Theory (specifically Online Learning, Bandits, Reinforcement Learning), Optimization, Game Theory, Algorithms. She is recently interested in exploring ML problems at the intersection of Fairness, Privacy, Game theory and Mechanism design.
Pierre Gaillard (INRIA)
Michal Valko (DeepMind)
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.
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2021 : Marginalized Operators for Off-Policy Reinforcement Learning »
Yunhao Tang · Mark Rowland · Remi Munos · Michal Valko -
2021 : Stochastic Shortest Path: Minimax, Parameter-Free and Towards Horizon-Free Regret »
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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 : Bandits Meet Mechanism Design to Combat Clickbait in Online Recommendation »
Thomas Kleine Büning · Aadirupa Saha · Christos Dimitrakakis · Haifeng Xu -
2023 Workshop: The Many Facets of Preference-Based Learning »
Aadirupa Saha · Mohammad Ghavamzadeh · Robert Busa-Fekete · Branislav Kveton · Viktor Bengs -
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: Federated Online and Bandit Convex Optimization »
Kumar Kshitij Patel · Lingxiao Wang · Aadirupa Saha · Nati Srebro -
2023 Poster: Half-Hop: A graph upsampling approach for slowing down message passing »
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2023 Poster: Curiosity in Hindsight: Intrinsic Exploration in Stochastic Environments »
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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: Adapting to game trees in zero-sum imperfect information games »
Côme Fiegel · Pierre Menard · Tadashi Kozuno · Remi Munos · Vianney Perchet · Michal Valko -
2023 Poster: Sequential Counterfactual Risk Minimization »
Houssam Zenati · Eustache Diemert · Matthieu Martin · Julien Mairal · Pierre Gaillard -
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 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: 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: 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 Workshop: Complex feedback in online learning »
Rémy Degenne · Pierre Gaillard · Wouter Koolen · Aadirupa Saha -
2022 Poster: From Dirichlet to Rubin: Optimistic Exploration in RL without Bonuses »
Daniil Tiapkin · Denis Belomestny · Eric Moulines · Alexey Naumov · Sergey Samsonov · Yunhao Tang · Michal Valko · Pierre Menard -
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 Poster: Versatile Dueling Bandits: Best-of-both World Analyses for Learning from Relative Preferences »
Aadirupa Saha · Pierre Gaillard -
2022 Spotlight: Versatile Dueling Bandits: Best-of-both World Analyses for Learning from Relative Preferences »
Aadirupa Saha · Pierre Gaillard -
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 Oral: From Dirichlet to Rubin: Optimistic Exploration in RL without Bonuses »
Daniil Tiapkin · Denis Belomestny · Eric Moulines · Alexey Naumov · Sergey Samsonov · Yunhao Tang · Michal Valko · Pierre Menard -
2022 Poster: Scaling Gaussian Process Optimization by Evaluating a Few Unique Candidates Multiple Times »
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2022 Poster: Stochastic Contextual Dueling Bandits under Linear Stochastic Transitivity Models »
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2022 Poster: Optimal and Efficient Dynamic Regret Algorithms for Non-Stationary Dueling Bandits »
Aadirupa Saha · Shubham Gupta -
2022 Spotlight: Scaling Gaussian Process Optimization by Evaluating a Few Unique Candidates Multiple Times »
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2022 Spotlight: Optimal and Efficient Dynamic Regret Algorithms for Non-Stationary Dueling Bandits »
Aadirupa Saha · Shubham Gupta -
2022 Spotlight: Stochastic Contextual Dueling Bandits under Linear Stochastic Transitivity Models »
Viktor Bengs · Aadirupa Saha · Eyke Hüllermeier -
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 Poster: Confidence-Budget Matching for Sequential Budgeted Learning »
Yonathan Efroni · Nadav Merlis · Aadirupa Saha · Shie Mannor -
2021 Poster: Adversarial Dueling Bandits »
Aadirupa Saha · Tomer Koren · Yishay Mansour -
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 Spotlight: Confidence-Budget Matching for Sequential Budgeted Learning »
Yonathan Efroni · Nadav Merlis · Aadirupa Saha · Shie Mannor -
2021 Spotlight: Adversarial Dueling Bandits »
Aadirupa Saha · Tomer Koren · Yishay Mansour -
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 »
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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 Poster: Optimal regret algorithm for Pseudo-1d Bandit Convex Optimization »
Aadirupa Saha · Nagarajan Natarajan · Praneeth Netrapalli · Prateek Jain -
2021 Spotlight: Taylor Expansion of Discount Factors »
Yunhao Tang · Mark Rowland · Remi Munos · Michal Valko -
2021 Spotlight: Optimal regret algorithm for Pseudo-1d Bandit Convex Optimization »
Aadirupa Saha · Nagarajan Natarajan · Praneeth Netrapalli · Prateek Jain -
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: Dueling Convex Optimization »
Aadirupa Saha · Tomer Koren · Yishay Mansour -
2021 Spotlight: Dueling Convex Optimization »
Aadirupa Saha · Tomer Koren · Yishay Mansour -
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: From PAC to Instance-Optimal Sample Complexity in the Plackett-Luce Model »
Aadirupa Saha · Aditya Gopalan -
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: Budgeted Online Influence Maximization »
Pierre Perrault · Jennifer Healey · Zheng Wen · Michal Valko -
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 : 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