Dynamic Policy Programming - Mohammad Gheshlaghi Azar; Hilbert J. Kappen (CoRR, 2010)

Speedy Q-Learning. - Mohammad Gheshlaghi Azar; Rémi Munos; Mohammad Ghavamzadeh; Hilbert J. Kappen (NIPS, 2011)

Dynamic Policy Programming with Function Approximation. - Mohammad Gheshlaghi Azar; Vicenç Gómez; Bert Kappen (AISTATS, 2011)

On the Sample Complexity of Reinforcement Learning with a Generative Model . - Mohammad Gheshlaghi Azar; Rémi Munos; Bert Kappen (ICML, 2012)

Dynamic policy programming. - Mohammad Gheshlaghi Azar; Vicenç Gómez; Hilbert J. Kappen (J. Mach. Learn. Res., 2012)

Sequential Transfer in Multi-armed Bandit with Finite Set of Models. - Mohammad Gheshlaghi Azar; Alessandro Lazaric; Emma Brunskill (NIPS, 2013)

Regret Bounds for Reinforcement Learning with Policy Advice. - Mohammad Gheshlaghi Azar; Alessandro Lazaric; Emma Brunskill (ECML/PKDD (1), 2013)

Minimax PAC bounds on the sample complexity of reinforcement learning with a generative model. - Mohammad Gheshlaghi Azar; Rémi Munos; Hilbert J. Kappen (Mach. Learn., 2013)

Online Stochastic Optimization under Correlated Bandit Feedback. - Mohammad Gheshlaghi Azar; Alessandro Lazaric; Emma Brunskill (ICML, 2014)

Stochastic Optimization of a Locally Smooth Function under Correlated Bandit Feedback. - Mohammad Gheshlaghi Azar; Alessandro Lazaric; Emma Brunskill (CoRR, 2014)

Correcting Multivariate Auto-Regressive Models for the Influence of Unobserved Common Input. - Vicenç Gómez; Mohammad Gheshlaghi Azar; Hilbert J. Kappen (CCIA, 2016)

Convex Relaxation Regression: Black-Box Optimization of Smooth Functions by Learning Their Convex Envelopes. - Mohammad Gheshlaghi Azar; Eva L. Dyer; Konrad P. Körding (UAI, 2016)

Minimax Regret Bounds for Reinforcement Learning. - Mohammad Gheshlaghi Azar; Ian Osband; Rémi Munos (ICML, 2017)

Noisy Networks for Exploration. - Meire Fortunato; Mohammad Gheshlaghi Azar; Bilal Piot; Jacob Menick; Ian Osband; Alex Graves; Vlad Mnih; Rémi Munos; Demis Hassabis; Olivier Pietquin; Charles Blundell; Shane Legg (CoRR, 2017)

The Reactor: A Sample-Efficient Actor-Critic Architecture. - Audrunas Gruslys; Mohammad Gheshlaghi Azar; Marc G. Bellemare; Rémi Munos (CoRR, 2017)

Rainbow: Combining Improvements in Deep Reinforcement Learning. - Matteo Hessel; Joseph Modayil; Hado van Hasselt; Tom Schaul; Georg Ostrovski; Will Dabney; Dan Horgan; Bilal Piot; Mohammad Gheshlaghi Azar; David Silver (AAAI, 2018)

Noisy Networks For Exploration. - Meire Fortunato; Mohammad Gheshlaghi Azar; Bilal Piot; Jacob Menick; Matteo Hessel; Ian Osband; Alex Graves; Volodymyr Mnih; Rémi Munos; Demis Hassabis; Olivier Pietquin; Charles Blundell; et al. (ICLR (Poster), 2018)

The Reactor: A fast and sample-efficient Actor-Critic agent for Reinforcement Learning. - Audrunas Gruslys; Will Dabney; Mohammad Gheshlaghi Azar; Bilal Piot; Marc G. Bellemare; Rémi Munos (ICLR (Poster), 2018)

Observe and Look Further: Achieving Consistent Performance on Atari. - Tobias Pohlen; Bilal Piot; Todd Hester; Mohammad Gheshlaghi Azar; Dan Horgan; David Budden; Gabriel Barth-Maron; Hado van Hasselt; John Quan; Mel Vecerík; Matteo Hessel; Rémi Munos; et al. (CoRR, 2018)

Neural Predictive Belief Representations. - Zhaohan Daniel Guo; Mohammad Gheshlaghi Azar; Bilal Piot; Bernardo A. Pires; Toby Pohlen; Rémi Munos (CoRR, 2018)

Hindsight Credit Assignment. - Anna Harutyunyan; Will Dabney; Thomas Mesnard; Mohammad Gheshlaghi Azar; Bilal Piot; Nicolas Heess; Hado van Hasselt; Gregory Wayne; Satinder Singh 001; Doina Precup; Rémi Munos (NeurIPS, 2019)

World Discovery Models. - Mohammad Gheshlaghi Azar; Bilal Piot; Bernardo A. Pires; Jean-Bastien Grill; Florent Altché; Rémi Munos (CoRR, 2019)

Meta-learning of Sequential Strategies. - Pedro A. Ortega; Jane X. Wang; Mark Rowland 001; Tim Genewein; Zeb Kurth-Nelson; Razvan Pascanu; Nicolas Heess; Joel Veness; Alexander Pritzel; Pablo Sprechmann; Siddhant M. Jayakumar; Tom McGrath; et al. (CoRR, 2019)

Bootstrap Latent-Predictive Representations for Multitask Reinforcement Learning. - Zhaohan Daniel Guo; Bernardo Ávila Pires; Bilal Piot; Jean-Bastien Grill; Florent Altché; Rémi Munos; Mohammad Gheshlaghi Azar (ICML, 2020)

Fast computation of Nash Equilibria in Imperfect Information Games. - Rémi Munos; Julien Pérolat; Jean-Baptiste Lespiau; Mark Rowland 001; Bart De Vylder; Marc Lanctot; Finbarr Timbers; Daniel Hennes; Shayegan Omidshafiei; Audrunas Gruslys; Mohammad Gheshlaghi Azar; Edward Lockhart; et al. (ICML, 2020)

Bootstrap Your Own Latent - A New Approach to Self-Supervised Learning. - 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; et al. (NeurIPS, 2020)

The Advantage Regret-Matching Actor-Critic. - Audrunas Gruslys; Marc Lanctot; Rémi Munos; Finbarr Timbers; Martin Schmid; Julien Pérolat; Dustin Morrill; Vinícius Flores Zambaldi; Jean-Baptiste Lespiau; John Schultz; Mohammad Gheshlaghi Azar; Michael Bowling; et al. (CoRR, 2020)

Drop, Swap, and Generate: A Self-Supervised Approach for Generating Neural Activity. - Ran Liu; Mehdi Azabou; Max Dabagia; Chi-Heng Lin; Mohammad Gheshlaghi Azar; Keith B. Hengen; Michal Valko; Eva L. Dyer (NeurIPS, 2021)

Geometric Entropic Exploration. - Zhaohan Daniel Guo; Mohammad Gheshlaghi Azar; Alaa Saade; Shantanu Thakoor; Bilal Piot; Bernardo Ávila Pires; Michal Valko; Thomas Mesnard; Tor Lattimore; Rémi Munos (CoRR, 2021)

Bootstrapped Representation Learning on Graphs. - Shantanu Thakoor; Corentin Tallec; Mohammad Gheshlaghi Azar; Rémi Munos; Petar Velickovic; Michal Valko (CoRR, 2021)

Mine Your Own vieW: Self-Supervised Learning Through Across-Sample Prediction. - 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 (CoRR, 2021)

Large-Scale Representation Learning on Graphs via Bootstrapping. - Shantanu Thakoor; Corentin Tallec; Mohammad Gheshlaghi Azar; Mehdi Azabou; Eva L. Dyer; Rémi Munos; Petar Velickovic; Michal Valko (ICLR, 2022)

BYOL-Explore: Exploration by Bootstrapped Prediction. - 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; et al. (NeurIPS, 2022)

KL-Entropy-Regularized RL with a Generative Model is Minimax Optimal. - 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; et al. (CoRR, 2022)

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 Ménard; Mohammad Gheshlaghi Azar; Rémi Munos; Olivier Pietquin; Matthieu Geist; et al. (ICML, 2023)

Understanding Self-Predictive Learning for Reinforcement Learning. - Yunhao Tang; Zhaohan Daniel Guo; Pierre Harvey Richemond; Bernardo Ávila Pires; Yash Chandak; Rémi Munos; Mark Rowland 001; Mohammad Gheshlaghi Azar; Charline Le Lan; Clare Lyle; András György 001; Shantanu Thakoor; et al. (ICML, 2023)

Nash Learning from Human Feedback. - Rémi Munos; Michal Valko; Daniele Calandriello; Mohammad Gheshlaghi Azar; Mark Rowland 001; Zhaohan Daniel Guo; Yunhao Tang; Matthieu Geist; Thomas Mesnard; Andrea Michi; Marco Selvi; Sertan Girgin; et al. (CoRR, 2023)

A General Theoretical Paradigm to Understand Learning from Human Preferences. - Mohammad Gheshlaghi Azar; Zhaohan Daniel Guo; Bilal Piot; Rémi Munos; Mark Rowland 001; Michal Valko; Daniele Calandriello (AISTATS, 2024)

Contrastive Policy Gradient: Aligning LLMs on sequence-level scores in a supervised-friendly fashion. - Yannis Flet-Berliac; Nathan Grinsztajn; Florian Strub; Eugene Choi; Bill Wu; Chris Cremer; Arash Ahmadian; Yash Chandak; Mohammad Gheshlaghi Azar; Olivier Pietquin; Matthieu Geist (EMNLP, 2024)

Nash Learning from Human Feedback. - Rémi Munos; Michal Valko; Daniele Calandriello; Mohammad Gheshlaghi Azar; Mark Rowland 001; Daniel Guo 001; Yunhao Tang; Matthieu Geist; Thomas Mesnard; Côme Fiegel; Andrea Michi; Marco Selvi; et al. (ICML, 2024)

Offline Regularised Reinforcement Learning for Large Language Models Alignment. - Pierre Harvey Richemond; Yunhao Tang; Daniel Guo 001; Daniele Calandriello; Mohammad Gheshlaghi Azar; Rafael Rafailov; Bernardo Ávila Pires; Eugene Tarassov; Lucas Spangher; Will Ellsworth; Aliaksei Severyn; Jonathan Mallinson; et al. (CoRR, 2024)

Contrastive Policy Gradient: Aligning LLMs on sequence-level scores in a supervised-friendly fashion. - Yannis Flet-Berliac; Nathan Grinsztajn; Florian Strub; Eugene Choi; Chris Cremer; Arash Ahmadian; Yash Chandak; Mohammad Gheshlaghi Azar; Olivier Pietquin; Matthieu Geist (CoRR, 2024)

Averaging log-likelihoods in direct alignment. - Nathan Grinsztajn; Yannis Flet-Berliac; Mohammad Gheshlaghi Azar; Florian Strub; Bill Wu; Eugene Choi; Chris Cremer; Arash Ahmadian; Yash Chandak; Olivier Pietquin; Matthieu Geist (CoRR, 2024)

An Analysis of Quantile Temporal-Difference Learning. - Mark Rowland 001; Rémi Munos; Mohammad Gheshlaghi Azar; Yunhao Tang; Georg Ostrovski; Anna Harutyunyan; Karl Tuyls; Marc G. Bellemare; Will Dabney (J. Mach. Learn. Res., 2024)

Self-Improving Robust Preference Optimization. - Eugene Choi; Arash Ahmadian; Matthieu Geist; Olivier Pietquin; Mohammad Gheshlaghi Azar (ICLR, 2025)

Command A: An Enterprise-Ready Large Language Model. - Aakanksha; Arash Ahmadian; Marwan Ahmed; Jay Alammar; Milad Alizadeh; Yazeed Alnumay; Sophia Althammer; Arkady Arkhangorodsky; Viraat Aryabumi; Dennis Aumiller; Raphaël Avalos; Zahara Aviv; et al. (CoRR, 2025)