Bilal Piot

According to our database, Bilal Piot authored at least 66 papers between 2012 and 2024.

Collaborative distances:

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

2024

Preference Optimization as Probabilistic Inference.

Abbas Abdolmaleki, Bilal Piot, Bobak Shahriari, Jost Tobias Springenberg, Tim Hertweck, Rishabh Joshi, Junhyuk Oh, Michael Bloesch, Thomas Lampe, Nicolas Heess, Jonas Buchli, Martin A. Riedmiller

CoRR, 2024

RRM: Robust Reward Model Training Mitigates Reward Hacking.

Tianqi Liu, Wei Xiong, Jie Ren, Lichang Chen, Junru Wu, Rishabh Joshi, Yang Gao, Jiaming Shen, Zhen Qin, Tianhe Yu, Daniel Sohn, Anastasiia Makarova, Jeremiah Z. Liu, Yuan Liu, Bilal Piot, Abe Ittycheriah, Aviral Kumar, Mohammad Saleh

CoRR, 2024

Building Math Agents with Multi-Turn Iterative Preference Learning.

Wei Xiong, Chengshuai Shi, Jiaming Shen, Aviv Rosenberg, Zhen Qin, Daniele Calandriello, Misha Khalman, Rishabh Joshi, Bilal Piot, Mohammad Saleh, Chi Jin, Tong Zhang, Tianqi Liu

CoRR, 2024

Gemma 2: Improving Open Language Models at a Practical Size.

Morgane Rivière, Shreya Pathak, Pier Giuseppe Sessa, Cassidy Hardin, Surya Bhupatiraju, Léonard Hussenot, Thomas Mesnard, Bobak Shahriari, Alexandre Ramé, Johan Ferret, Peter Liu, Pouya Tafti, Abe Friesen, Michelle Casbon, Sabela Ramos, Ravin Kumar, Charline Le Lan, Sammy Jerome, Anton Tsitsulin, Nino Vieillard, Piotr Stanczyk, Sertan Girgin, Nikola Momchev, Matt Hoffman, Shantanu Thakoor, Jean-Bastien Grill, Behnam Neyshabur, Olivier Bachem, Alanna Walton, Aliaksei Severyn, Alicia Parrish, Aliya Ahmad, Allen Hutchison, Alvin Abdagic, Amanda Carl, Amy Shen, Andy Brock, Andy Coenen, Anthony Laforge, Antonia Paterson, Ben Bastian, Bilal Piot, Bo Wu, Brandon Royal, Charlie Chen, Chintu Kumar, Chris Perry, Chris Welty, Christopher A. Choquette-Choo, Danila Sinopalnikov, David Weinberger, Dimple Vijaykumar, Dominika Rogozinska, Dustin Herbison, Elisa Bandy, Emma Wang, Eric Noland, Erica Moreira, Evan Senter, Evgenii Eltyshev, Francesco Visin, Gabriel Rasskin, Gary Wei, Glenn Cameron, Gus Martins, Hadi Hashemi, Hanna Klimczak-Plucinska, Harleen Batra, Harsh Dhand, Ivan Nardini, Jacinda Mein, Jack Zhou, James Svensson, Jeff Stanway, Jetha Chan, Jin Peng Zhou, Joana Carrasqueira, Joana Iljazi, Jocelyn Becker, Joe Fernandez, Joost van Amersfoort, Josh Gordon, Josh Lipschultz, Josh Newlan, Ju-yeong Ji, Kareem Mohamed, Kartikeya Badola, Kat Black, Katie Millican, Keelin McDonell, Kelvin Nguyen, Kiranbir Sodhia, Kish Greene, Lars Lowe Sjösund, Lauren Usui, Laurent Sifre, Lena Heuermann, Leticia Lago, Lilly McNealus

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

Multi-turn Reinforcement Learning from Preference Human Feedback.

Lior Shani, Aviv Rosenberg, Asaf B. Cassel, Oran Lang, Daniele Calandriello, Avital Zipori, Hila Noga, Orgad Keller, Bilal Piot, Idan Szpektor, Avinatan Hassidim, Yossi Matias, Rémi Munos

CoRR, 2024

Human Alignment of Large Language Models through Online Preference Optimisation.

Daniele Calandriello, Daniel Guo, Rémi Munos, Mark Rowland, Yunhao Tang, Bernardo Ávila Pires, Pierre Harvey Richemond, Charline Le Lan, Michal Valko, Tianqi Liu, Rishabh Joshi, Zeyu Zheng, Bilal Piot

CoRR, 2024

Direct Language Model Alignment from Online AI Feedback.

Shangmin Guo, Biao Zhang, Tianlin Liu, Tianqi Liu, Misha Khalman, Felipe Llinares, Alexandre Ramé, Thomas Mesnard, Yao Zhao, Bilal Piot, Johan Ferret, Mathieu Blondel

CoRR, 2024

Generalized Preference Optimization: A Unified Approach to Offline Alignment.

Yunhao Tang, Zhaohan Daniel Guo, Zeyu Zheng, Daniele Calandriello, Rémi Munos, Mark Rowland, Pierre Harvey Richemond, Michal Valko, Bernardo Ávila Pires, Bilal Piot

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

Human Alignment of Large Language Models through Online Preference Optimisation.

Daniele Calandriello, Zhaohan Daniel Guo, Rémi Munos, Mark Rowland, Yunhao Tang, Bernardo Ávila Pires, Pierre Harvey Richemond, Charline Le Lan, Michal Valko, Tianqi Liu, Rishabh Joshi, Zeyu Zheng, Bilal Piot

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

Unlocking the Power of Representations in Long-term Novelty-based Exploration.

Alaa Saade, Steven Kapturowski, Daniele Calandriello, Charles Blundell, Pablo Sprechmann, Leopoldo Sarra, Oliver Groth, Michal Valko, Bilal Piot

Proceedings of the Twelfth International Conference on Learning Representations, 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

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

The Edge of Orthogonality: A Simple View of What Makes BYOL Tick.

Pierre Harvey Richemond, Allison C. Tam, Yunhao Tang, Florian Strub, Bilal Piot, Felix Hill

Proceedings of the International Conference on Machine Learning, 2023

2022

Figure Data for the paper "Mastering the Game of Stratego with Model-Free Multiagent Reinforcement Learning".

Julien Pérolat, Bart De Vylder, Daniel Hennes, Eugene Tarassov, Florian Strub, Vincent de Boer, Paul Muller, Jerome T. Connor, Neil Burch, Thomas Anthony, Stephen McAleer, Romuald Elie, Sarah H. Cen, Zhe Wang, Audrunas Gruslys, Aleksandra Malysheva, Mina Khan, Sherjil Ozair, Finbarr Timbers, Toby Pohlen, Tom Eccles, Mark Rowland, Marc Lanctot, Jean-Baptiste Lespiau, Bilal Piot, Shayegan Omidshafiei, Edward Lockhart, Laurent Sifre, Nathalie Beauguerlange, Rémi Munos, David Silver, Satinder Singh, Demis Hassabis, Karl Tuyls

Dataset, October, 2022

Mastering the Game of Stratego with Model-Free Multiagent Reinforcement Learning.

Julien Pérolat, Bart De Vylder, Daniel Hennes, Eugene Tarassov, Florian Strub, Vincent de Boer, Paul Muller, Jerome T. Connor, Neil Burch, Thomas W. Anthony, Stephen McAleer, Romuald Elie, Sarah H. Cen, Zhe Wang, Audrunas Gruslys, Aleksandra Malysheva, Mina Khan, Sherjil Ozair, Finbarr Timbers, Toby Pohlen, Tom Eccles, Mark Rowland, Marc Lanctot, Jean-Baptiste Lespiau, Bilal Piot, Shayegan Omidshafiei, Edward Lockhart, Laurent Sifre, Nathalie Beauguerlange, Rémi Munos, David Silver, Satinder Singh, Demis Hassabis, Karl Tuyls

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

Emergent Communication at Scale.

Rahma Chaabouni, Florian Strub, Florent Altché, Eugene Tarassov, Corentin Tallec, Elnaz Davoodi, Kory Wallace Mathewson, Olivier Tieleman, Angeliki Lazaridou, Bilal Piot

Proceedings of the Tenth International Conference on Learning Representations, 2022

2021

Shaking the foundations: delusions in sequence models for interaction and control.

Pedro A. Ortega, Markus Kunesch, Grégoire Delétang, Tim Genewein, Jordi Grau-Moya, Joel Veness, Jonas Buchli, Jonas Degrave, Bilal Piot, Julien Pérolat, Tom Everitt, Corentin Tallec, Emilio Parisotto, Tom Erez, Yutian Chen, Scott E. Reed, Marcus Hutter, Nando de Freitas, Shane Legg

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

2020

BYOL works even without batch statistics.

Pierre H. Richemond, Jean-Bastien Grill, Florent Altché, Corentin Tallec, Florian Strub, Andrew Brock, Samuel L. Smith, Soham De, Razvan Pascanu, Bilal Piot, Michal Valko

CoRR, 2020

Acme: A Research Framework for Distributed Reinforcement Learning.

Matt Hoffman, Bobak Shahriari, John Aslanides, Gabriel Barth-Maron, Feryal M. P. Behbahani, Tamara Norman, Abbas Abdolmaleki, Albin Cassirer, Fan Yang, Kate Baumli, Sarah Henderson, Alexander Novikov, Sergio Gómez Colmenarejo, Serkan Cabi, Çaglar Gülçehre, Tom Le Paine, Andrew Cowie, Ziyu Wang, Bilal Piot, Nando de Freitas

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

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

Agent57: Outperforming the Atari Human Benchmark.

Adrià Puigdomènech Badia, Bilal Piot, Steven Kapturowski, Pablo Sprechmann, Alex Vitvitskyi, Zhaohan Daniel Guo, Charles Blundell

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

Never Give Up: Learning Directed Exploration Strategies.

Adrià Puigdomènech Badia, Pablo Sprechmann, Alex Vitvitskyi, Zhaohan Daniel Guo, Bilal Piot, Steven Kapturowski, Olivier Tieleman, Martín Arjovsky, Alexander Pritzel, Andrew Bolt, Charles Blundell

Proceedings of the 8th International Conference on Learning Representations, 2020

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

Observational Learning by Reinforcement Learning.

Diana Borsa, Nicolas Heess, Bilal Piot, Siqi Liu, Leonard Hasenclever, Rémi Munos, Olivier Pietquin

Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems, 2019

2018

Neural Predictive Belief Representations.

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

CoRR, 2018

Playing the Game of Universal Adversarial Perturbations.

Julien Pérolat, Mateusz Malinowski, Bilal Piot, Olivier Pietquin

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

Actor-Critic Fictitious Play in Simultaneous Move Multistage Games.

Julien Pérolat, Bilal Piot, Olivier Pietquin

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2018

Deep Q-learning From Demonstrations.

Todd Hester, Matej Vecerík, Olivier Pietquin, Marc Lanctot, Tom Schaul, Bilal Piot, Dan Horgan, John Quan, Andrew Sendonaris, Ian Osband, Gabriel Dulac-Arnold, John P. Agapiou, Joel Z. Leibo, Audrunas Gruslys

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

Bridging the Gap Between Imitation Learning and Inverse Reinforcement Learning.

Bilal Piot, Matthieu Geist, Olivier Pietquin

IEEE Trans. Neural Networks Learn. Syst., 2017

Leveraging Demonstrations for Deep Reinforcement Learning on Robotics Problems with Sparse Rewards.

Matej Vecerík, Todd Hester, Jonathan Scholz, Fumin Wang, Olivier Pietquin, Bilal Piot, Nicolas Heess, Thomas Rothörl, Thomas Lampe, Martin A. Riedmiller

CoRR, 2017

Learning from Demonstrations for Real World Reinforcement Learning.

Todd Hester, Matej Vecerík, Olivier Pietquin, Marc Lanctot, Tom Schaul, Bilal Piot, Andrew Sendonaris, Gabriel Dulac-Arnold, Ian Osband, John P. Agapiou, Joel Z. Leibo, Audrunas Gruslys

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

Observational Learning by Reinforcement Learning.

Diana Borsa, Bilal Piot, Rémi Munos, Olivier Pietquin

CoRR, 2017

Is the Bellman residual a bad proxy?

Matthieu Geist, Bilal Piot, Olivier Pietquin

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

End-to-end optimization of goal-driven and visually grounded dialogue systems.

Florian Strub, Harm de Vries, Jérémie Mary, Bilal Piot, Aaron C. Courville, Olivier Pietquin

Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, 2017

Learning Nash Equilibrium for General-Sum Markov Games from Batch Data.

Julien Pérolat, Florian Strub, Bilal Piot, Olivier Pietquin

Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, 2017

2016

Difference of Convex Functions Programming Applied to Control with Expert Data.

Bilal Piot, Matthieu Geist, Olivier Pietquin

CoRR, 2016

Should one minimize the expected Bellman residual or maximize the mean value?

Matthieu Geist, Bilal Piot, Olivier Pietquin

CoRR, 2016

Softened Approximate Policy Iteration for Markov Games.

Julien Pérolat, Bilal Piot, Matthieu Geist, Bruno Scherrer, Olivier Pietquin

Proceedings of the 33nd International Conference on Machine Learning, 2016

Score-based Inverse Reinforcement Learning.

Layla El Asri, Bilal Piot, Matthieu Geist, Romain Laroche, Olivier Pietquin

Proceedings of the 2016 International Conference on Autonomous Agents & Multiagent Systems, 2016

On the Use of Non-Stationary Strategies for Solving Two-Player Zero-Sum Markov Games.

Julien Pérolat, Bilal Piot, Bruno Scherrer, Olivier Pietquin

Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, 2016

2015

Inverse Reinforcement Learning in Relational Domains.

Thibaut Munzer, Bilal Piot, Matthieu Geist, Olivier Pietquin, Manuel Lopes

Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, 2015

Imitation Learning Applied to Embodied Conversational Agents.

Bilal Piot, Olivier Pietquin, Matthieu Geist

Proceedings of the 4th Workshop on Machine Learning for Interactive Systems, 2015

Approximate Dynamic Programming for Two-Player Zero-Sum Markov Games.

Julien Pérolat, Bruno Scherrer, Bilal Piot, Olivier Pietquin

Proceedings of the 32nd International Conference on Machine Learning, 2015

2014

Boosted Bellman Residual Minimization Handling Expert Demonstrations.

Bilal Piot, Matthieu Geist, Olivier Pietquin

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2014

Difference of Convex Functions Programming for Reinforcement Learning.

Bilal Piot, Matthieu Geist, Olivier Pietquin

Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

Predicting when to laugh with structured classification.

Bilal Piot, Olivier Pietquin, Matthieu Geist

Proceedings of the 15th Annual Conference of the International Speech Communication Association, 2014

Boosted and reward-regularized classification for apprenticeship learning.

Bilal Piot, Matthieu Geist, Olivier Pietquin

Proceedings of the International conference on Autonomous Agents and Multi-Agent Systems, 2014

2013

Classification structurée pour l'apprentissage par renforcement inverse.

Edouard Klein, Bilal Piot, Matthieu Geist, Olivier Pietquin

Rev. d'Intelligence Artif., 2013

Learning from Demonstrations: Is It Worth Estimating a Reward Function?

Bilal Piot, Matthieu Geist, Olivier Pietquin

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

A Cascaded Supervised Learning Approach to Inverse Reinforcement Learning.

Edouard Klein, Bilal Piot, Matthieu Geist, Olivier Pietquin

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

Laugh When You're Winning.

Maurizio Mancini, Laurent Ach, Emeline Bantegnie, Tobias Baur, Nadia Berthouze, Debajyoti Datta, Yu Ding, Stéphane Dupont, Harry J. Griffin, Florian Lingenfelser, Radoslaw Niewiadomski, Catherine Pelachaud, Olivier Pietquin, Bilal Piot, Jérôme Urbain, Gualtiero Volpe, Johannes Wagner

Proceedings of the Innovative and Creative Developments in Multimodal Interaction Systems, 2013

Laugh-aware virtual agent and its impact on user amusement.

Radoslaw Niewiadomski, Jennifer Hofmann, Jérôme Urbain, Tracey Platt, Johannes Wagner, Bilal Piot, Hüseyin Çakmak, Sathish Pammi, Tobias Baur, Stéphane Dupont, Matthieu Geist, Florian Lingenfelser, Gary McKeown, Olivier Pietquin, Willibald Ruch

Proceedings of the International conference on Autonomous Agents and Multi-Agent Systems, 2013

2012

Inverse Reinforcement Learning through Structured Classification.

Edouard Klein, Matthieu Geist, Bilal Piot, Olivier Pietquin

Proceedings of the Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3-6, 2012