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Publications

Theory & foundations

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An adaptive and efficient multi-goal exploration
Jean Tarbouriech *, Omar Darwiche Domingues *, Pierre Ménard *, Matteo Pirotta *, Michal Valko, Alessandro Lazaric *
AISTATS
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Marginalized operators for off-policy reinforcement learning
Yunhao Tang *, Mark Rowland, Remi Munos, Michal Valko
AISTATS
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Your Policy Regularizer is Secretly an Adversary
Rob Brekelmans *, Tim Genewein, Jordi Grau, Grégoire Delétang, Markus Kunesch, Shane Legg, Pedro Ortega
arXiv
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Sample-based Approximation of Nash in Large Many-Player Games via Gradient Descent
Ian Gemp, Rahul Savani *, Marc Lanctot, Yoram Bachrach, Thomas Anthony, Richard Everett, Andrea Tacchetti, Tom Eccles, János Kramár
AAMAS
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On the Role of Neural Collapse in Transfer Learning
Andras Gyorgy, Marcus Hutter
ICLR
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ParK: Sound and Efficient Kernel Ridge Regression by Feature Space Partitions
Luigi Carratino *, Daniele Calandriello, Stefano Vigogna *, Lorenzo Rosasco *
NeurIPS
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On the Expressivity of Markov Reward
David Abel, Doina Precup, Anna Harutyunyan, Mark Ho *, Michael Littman *, Will Dabney, Satinder Baveja
NeurIPS
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Asymptotically Best Casual Effect Identification with Multi-Armed Bandits
Alan Malek, Silvia Chiappa
NeurIPS
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Statistical discrimination in learning agents
Joel Leibo, Yiran Mao, Kevin McKee, William Isaac, Silvia Chiappa, Ben Coppin, Karl Tuyls, Yoram Bachrach, Suzanne Sadedin *, Sasha Vezhnevets, Michiel Bakker
arXiv
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Deep Kernel Shaping
James Martens, Andy Ballard, Guillaume Desjardins, Grzegorz Swirszcz, Valentin Dalibard, Jascha Sohl-Dickstein *, Sam Schoenholz *
arXiv
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Deep Learning without Shortcuts: Shaping the Kernel with Tailored Rectifiers
Guodong Zhang, Alex Botev, James Martens
ICLR
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Temporally Abstract Partial Models
Khimya Khetarpal *, Zafarali Ahmed, Gheorghe Comanici, Doina Precup
NeurIPS
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Nonparametric Regression with Shallow Overparameterized Neural Networks Trained by GD with Early Stopping
Ilja Kuzborskij, Csaba Szepesvari
COLT
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Taylor expansions of discount factors
Yunhao Tang *, Remi Munos, Mark Rowland, Michal Valko
ICML
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Kernel-based reinforcement Learning: A finite-time analysis
O Domingues *, Pierre Menard *, Matteo Pirotta *, Emilie Kaufmann *, Michal Valko
ICML
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Online A-optimal design and active linear regression
Xavier Fontaine *, Pierre Perrault *, Michal Valko, V Perchet *
ICML
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Fast active learning for pure exploration in reinforcement learning
Pierre Ménard *, O Domingues *, Emilie Kaufmann *, Anders Jonsson *, Edouard Leurent *, Michal Valko
ICML
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Multi-Agent Training beyond Zero-Sum with Correlated Equilibrium Meta-Solvers
Luke Marris, Paul Muller, Marc Lanctot, Karl Tuyls, Thore Graepel
ICML
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A Distribution-Dependent Analysis of Meta-Learning
Mikhail Konobeev *, Ilja Kuzborskij, Csaba Szepesvari
ICML
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Learning in two-player zero-sum partially observable Markov games with perfect recall
Tadashi Kozuno *, Pierre Ménard *, Remi Munos, Michal Valko
NeurIPS
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Publication
Stochastic shortest path: minimax, parameter-free and towards horizon-free regret
Jean Tarbouriech *, Runlong Zhou *, Simon S. Du *, Matteo Pirotta *, Michal Valko, Alessandro Lazaric *
NeurIPS
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Publication
UCB Momentum Q-learning: Correcting the bias without forgetting
Pierre Menard *, O Domingues *, X Shang *, Michal Valko
ICML
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A note on a confidence bound of Kuzborskij and Szsepesvári
Omar Rivasplata
arXiv
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Equilibrium Refinements for Multi-Agent Influence Diagrams: Theory and Practice
James Fox *, Lewis Hammond *, Tom Everitt, Alessandro Abate *, Michael Wooldridge *
AAMAS
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Publication
A kernel-based approach to non-stationary reinforcement learning in metric spaces
O Domingues *, Pierre Ménard *, Matteo Pirotta *, Emilie Kaufmann *, Michal Valko
AISTATS
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