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From Dirichlet to Rubin: Optimistic exploration in RL without bonuses
Daniil Tiapkin *, Denis Belomestny *, Éric Moulines *, Alexey Naumov *, Sergey Samsonov *, Yunhao Tang, Michal Valko, Pierre Menard *
ICML
2022-07-17
Deep reinforcement learning
Reinforcement learning
Theory & foundations
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Publication
An adaptive and efficient multi-goal exploration
Jean Tarbouriech *, Omar Darwiche Domingues *, Pierre Ménard *, Matteo Pirotta *, Michal Valko, Alessandro Lazaric *
AISTATS
2022-03-28
Reinforcement learning
Deep reinforcement learning
Theory & foundations
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Publication
Marginalized operators for off-policy reinforcement learning
Yunhao Tang *, Mark Rowland, Remi Munos, Michal Valko
AISTATS
2022-03-28
Reinforcement learning
Theory & foundations
Deep reinforcement learning
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Publication
Your Policy Regularizer is Secretly an Adversary
Rob Brekelmans *, Tim Genewein, Jordi Grau, Grégoire Delétang, Markus Kunesch, Shane Legg, Pedro Ortega
arXiv
2022-03-23
Safety
Reinforcement learning
Theory & foundations
Control & robotics
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Publication
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
2022-02-04
Games
Multi-agent learning
Theory & foundations
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Publication
On the Role of Neural Collapse in Transfer Learning
Andras Gyorgy, Marcus Hutter
ICLR
2021-12-30
Continual & transfer learning
Deep learning
Meta-Learning
Multi-task learning
Representation learning
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Publication
ParK: Sound and Efficient Kernel Ridge Regression by Feature Space Partitions
Luigi Carratino *, Daniele Calandriello, Stefano Vigogna *, Lorenzo Rosasco *
NeurIPS
2021-12-10
Theory & foundations
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Publication
On the Expressivity of Markov Reward
David Abel, Doina Precup, Anna Harutyunyan, Mark Ho *, Michael Littman *, Will Dabney, Satinder Baveja
NeurIPS
2021-12-01
Reinforcement learning
Theory & foundations
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Publication
Asymptotically Best Casual Effect Identification with Multi-Armed Bandits
Alan Malek, Silvia Chiappa
NeurIPS
2021-10-26
Causal inference
Online Learning
Theory & foundations
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Publication
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
2021-10-21
Deep reinforcement learning
Multi-agent learning
Theory & foundations
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Publication
Deep Kernel Shaping
James Martens, Andy Ballard, Guillaume Desjardins, Grzegorz Swirszcz, Valentin Dalibard, Jascha Sohl-Dickstein *, Sam Schoenholz *
arXiv
2021-10-05
Deep learning
Optimisation
Theory & foundations
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Publication
Deep Learning without Shortcuts: Shaping the Kernel with Tailored Rectifiers
Guodong Zhang, Alex Botev, James Martens
ICLR
2021-09-29
Deep learning
Theory & foundations
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Publication
Temporally Abstract Partial Models
Khimya Khetarpal *, Zafarali Ahmed, Gheorghe Comanici, Doina Precup
NeurIPS
2021-09-28
Reinforcement learning
HRL
Theory & foundations
Abstraction & concepts
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Publication
Nonparametric Regression with Shallow Overparameterized Neural Networks Trained by GD with Early Stopping
Ilja Kuzborskij, Csaba Szepesvari
COLT
2021-08-15
Theory & foundations
Deep learning
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Publication
Taylor expansions of discount factors
Yunhao Tang *, Remi Munos, Mark Rowland, Michal Valko
ICML
2021-07-18
Deep reinforcement learning
Reinforcement learning
Theory & foundations
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Publication
Kernel-based reinforcement Learning: A finite-time analysis
O Domingues *, Pierre Menard *, Matteo Pirotta *, Emilie Kaufmann *, Michal Valko
ICML
2021-07-18
Reinforcement learning
Theory & foundations
Representation learning
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Publication
Online A-optimal design and active linear regression
Xavier Fontaine *, Pierre Perrault *, Michal Valko, V Perchet *
ICML
2021-07-18
Online Learning
Theory & foundations
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Publication
Fast active learning for pure exploration in reinforcement learning
Pierre Ménard *, O Domingues *, Emilie Kaufmann *, Anders Jonsson *, Edouard Leurent *, Michal Valko
ICML
2021-07-18
Online Learning
Theory & foundations
Information Theory
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Publication
Multi-Agent Training beyond Zero-Sum with Correlated Equilibrium Meta-Solvers
Luke Marris, Paul Muller, Marc Lanctot, Karl Tuyls, Thore Graepel
ICML
2021-07-18
Multi-agent learning
Games
Theory & foundations
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Publication
A Distribution-Dependent Analysis of Meta-Learning
Mikhail Konobeev *, Ilja Kuzborskij, Csaba Szepesvari
ICML
2021-06-14
Meta-Learning
Theory & foundations
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Publication
Learning in two-player zero-sum partially observable Markov games with perfect recall
Tadashi Kozuno *, Pierre Ménard *, Remi Munos, Michal Valko
NeurIPS
2021-06-13
Games
Theory & foundations
Online Learning
Reinforcement learning
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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
2021-04-22
Theory & foundations
Reinforcement learning
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Publication
UCB Momentum Q-learning: Correcting the bias without forgetting
Pierre Menard *, O Domingues *, X Shang *, Michal Valko
ICML
2021-03-01
Reinforcement learning
Theory & foundations
Online Learning
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Publication
A note on a confidence bound of Kuzborskij and Szsepesvári
Omar Rivasplata
arXiv
2021-02-12
Theory & foundations
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Publication
Equilibrium Refinements for Multi-Agent Influence Diagrams: Theory and Practice
James Fox *, Lewis Hammond *, Tom Everitt, Alessandro Abate *, Michael Wooldridge *
AAMAS
2021-02-09
Theory & foundations
Games
Safety
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