Mohammad Gheshlaghi Azar

According to our database, Mohammad Gheshlaghi Azar authored at least 45 papers between 2010 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

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

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

Self-Improving Robust Preference Optimization.

Eugene Choi, Arash Ahmadian, Matthieu Geist, Olivier Pietquin, Mohammad Gheshlaghi Azar

CoRR, 2024

Offline Regularised Reinforcement Learning for Large Language Models Alignment.

Pierre Harvey Richemond, Yunhao Tang, Daniel Guo, Daniele Calandriello, Mohammad Gheshlaghi Azar, Rafael Rafailov, Bernardo Ávila Pires, Eugene Tarassov, Lucas Spangher, Will Ellsworth, Aliaksei Severyn, Jonathan Mallinson, Lior Shani, Gil Shamir, Rishabh Joshi, Tianqi Liu, Rémi Munos, Bilal Piot

CoRR, 2024

Nash Learning from Human Feedback.

Rémi Munos, Michal Valko, Daniele Calandriello, Mohammad Gheshlaghi Azar, Mark Rowland, Zhaohan Daniel Guo, Yunhao Tang, Matthieu Geist, Thomas Mesnard, Côme Fiegel, Andrea Michi, Marco Selvi, Sertan Girgin, Nikola Momchev, Olivier Bachem, Daniel J. Mankowitz, Doina Precup, Bilal Piot

Proceedings of the Forty-first International Conference on Machine Learning, 2024

A General Theoretical Paradigm to Understand Learning from Human Preferences.

Mohammad Gheshlaghi Azar, Zhaohan Daniel Guo, Bilal Piot, Rémi Munos, Mark Rowland, Michal Valko, Daniele Calandriello

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

2023

Nash Learning from Human Feedback.

Rémi Munos, Michal Valko, Daniele Calandriello, Mohammad Gheshlaghi Azar, Mark Rowland, Zhaohan Daniel Guo, Yunhao Tang, Matthieu Geist, Thomas Mesnard, Andrea Michi, Marco Selvi, Sertan Girgin, Nikola Momchev, Olivier Bachem, Daniel J. Mankowitz, Doina Precup, Bilal Piot

CoRR, 2023

A General Theoretical Paradigm to Understand Learning from Human Preferences.

Mohammad Gheshlaghi Azar, Mark Rowland, Bilal Piot, Daniel Guo, Daniele Calandriello, Michal Valko, Rémi Munos

CoRR, 2023

An Analysis of Quantile Temporal-Difference Learning.

Mark Rowland, Rémi Munos, Mohammad Gheshlaghi Azar, Yunhao Tang, Georg Ostrovski, Anna Harutyunyan, Karl Tuyls, Marc G. Bellemare, Will Dabney

CoRR, 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, Mohammad Gheshlaghi Azar, Charline Le Lan, Clare Lyle, András György, Shantanu Thakoor, Will Dabney, Bilal Piot, Daniele Calandriello, Michal Valko

Proceedings of the International Conference on Machine Learning, 2023

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, Csaba Szepesvári, Wataru Kumagai, Yutaka Matsuo

Proceedings of the International Conference on Machine Learning, 2023

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, Csaba Szepesvári

CoRR, 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, Mohammad Gheshlaghi Azar, Bilal Piot

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

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

Proceedings of the Tenth International Conference on Learning Representations, 2022

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

Bootstrapped Representation Learning on Graphs.

Shantanu Thakoor, Corentin Tallec, Mohammad Gheshlaghi Azar, Rémi Munos, Petar Velickovic, Michal Valko

CoRR, 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

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

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

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, Karl Tuyls

CoRR, 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, Rémi Munos, Michal Valko

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Fast computation of Nash Equilibria in Imperfect Information Games.

Rémi Munos, Julien Pérolat, Jean-Baptiste Lespiau, Mark Rowland, Bart De Vylder, Marc Lanctot, Finbarr Timbers, Daniel Hennes, Shayegan Omidshafiei, Audrunas Gruslys, Mohammad Gheshlaghi Azar, Edward Lockhart, Karl Tuyls

Proceedings of the 37th International Conference on Machine Learning, 2020

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

Proceedings of the 37th International Conference on Machine Learning, 2020

2019

Meta-learning of Sequential Strategies.

Pedro A. Ortega, Jane X. Wang, Mark Rowland, Tim Genewein, Zeb Kurth-Nelson, Razvan Pascanu, Nicolas Heess, Joel Veness, Alexander Pritzel, Pablo Sprechmann, Siddhant M. Jayakumar, Tom McGrath, Kevin J. Miller, Mohammad Gheshlaghi Azar, Ian Osband, Neil C. Rabinowitz, András György, Silvia Chiappa, Simon Osindero, Yee Whye Teh, Hado van Hasselt, Nando de Freitas, Matthew M. Botvinick, Shane Legg

CoRR, 2019

World Discovery Models.

Mohammad Gheshlaghi Azar, Bilal Piot, Bernardo A. Pires, Jean-Bastien Grill, Florent Altché, Rémi Munos

CoRR, 2019

Hindsight Credit Assignment.

Anna Harutyunyan, Will Dabney, Thomas Mesnard, Mohammad Gheshlaghi Azar, Bilal Piot, Nicolas Heess, Hado van Hasselt, Gregory Wayne, Satinder Singh, Doina Precup, Rémi Munos

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

2018

Neural Predictive Belief Representations.

Zhaohan Daniel Guo, Mohammad Gheshlaghi Azar, Bilal Piot, Bernardo A. Pires, Toby Pohlen, Rémi Munos

CoRR, 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, Olivier Pietquin

CoRR, 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

Proceedings of the 6th International Conference on Learning Representations, 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, Shane Legg

Proceedings of the 6th International Conference on Learning Representations, 2018

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

Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, 2018

2017

The Reactor: A Sample-Efficient Actor-Critic Architecture.

Audrunas Gruslys, Mohammad Gheshlaghi Azar, Marc G. Bellemare, Rémi Munos

CoRR, 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

Minimax Regret Bounds for Reinforcement Learning.

Mohammad Gheshlaghi Azar, Ian Osband, Rémi Munos

Proceedings of the 34th International Conference on Machine Learning, 2017

2016

Convex Relaxation Regression: Black-Box Optimization of Smooth Functions by Learning Their Convex Envelopes.

Mohammad Gheshlaghi Azar, Eva L. Dyer, Konrad P. Körding

Proceedings of the Thirty-Second Conference on Uncertainty in Artificial Intelligence, 2016

Correcting Multivariate Auto-Regressive Models for the Influence of Unobserved Common Input.

Vicenç Gómez, Mohammad Gheshlaghi Azar, Hilbert J. Kappen

Proceedings of the Artificial Intelligence Research and Development, 2016

2014

Stochastic Optimization of a Locally Smooth Function under Correlated Bandit Feedback.

Mohammad Gheshlaghi Azar, Alessandro Lazaric, Emma Brunskill

CoRR, 2014

Online Stochastic Optimization under Correlated Bandit Feedback.

Mohammad Gheshlaghi Azar, Alessandro Lazaric, Emma Brunskill

Proceedings of the 31th International Conference on Machine Learning, 2014

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

Regret Bounds for Reinforcement Learning with Policy Advice.

Mohammad Gheshlaghi Azar, Alessandro Lazaric, Emma Brunskill

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2013

Sequential Transfer in Multi-armed Bandit with Finite Set of Models.

Mohammad Gheshlaghi Azar, Alessandro Lazaric, Emma Brunskill

Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013

2012

Dynamic policy programming.

Mohammad Gheshlaghi Azar, Vicenç Gómez, Hilbert J. Kappen

J. Mach. Learn. Res., 2012

On the Sample Complexity of Reinforcement Learning with a Generative Model .

Mohammad Gheshlaghi Azar, Rémi Munos, Bert Kappen

Proceedings of the 29th International Conference on Machine Learning, 2012

2011

Dynamic Policy Programming with Function Approximation.

Mohammad Gheshlaghi Azar, Vicenç Gómez, Bert Kappen

Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, 2011

Speedy Q-Learning.

Mohammad Gheshlaghi Azar, Rémi Munos, Mohammad Ghavamzadeh, Hilbert J. Kappen

Proceedings of the Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011. Proceedings of a meeting held 12-14 December 2011, 2011

2010

Dynamic Policy Programming

Mohammad Gheshlaghi Azar, Hilbert J. Kappen

CoRR, 2010