Bilal Piot

Publication Activity (10 Years)

Years Active: 2012-2024
Publications (10 Years): 58

Top Topics

Convex Functions

Approximate Policy Iteration

Reinforcement Learning

Nash Equilibrium

Top Venues

Publications

Mohammad Gheshlaghi Azar, Zhaohan Daniel Guo, Bilal Piot, Rémi Munos, Mark Rowland, Michal Valko, Daniele Calandriello
A General Theoretical Paradigm to Understand Learning from Human Preferences. AISTATS (2024)
Yunhao Tang, Zhaohan Daniel Guo, Zeyu Zheng, Daniele Calandriello, Rémi Munos, Mark Rowland, Pierre Harvey Richemond, Michal Valko, Bernardo Ávila Pires, Bilal Piot
Generalized Preference Optimization: A Unified Approach to Offline Alignment. CoRR (2024)
Shangmin Guo, Biao Zhang, Tianlin Liu, Tianqi Liu, Misha Khalman, Felipe Llinares, Alexandre Ramé, Thomas Mesnard, Yao Zhao, Bilal Piot, Johan Ferret, Mathieu Blondel
Direct Language Model Alignment from Online AI Feedback. CoRR (2024)
Lior Shani, Aviv Rosenberg, Asaf Cassel, Oran Lang, Daniele Calandriello, Avital Zipori, Hila Noga, Orgad Keller, Bilal Piot, Idan Szpektor, Avinatan Hassidim, Yossi Matias, Rémi Munos
Multi-turn Reinforcement Learning from Preference Human Feedback. CoRR (2024)
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
Human Alignment of Large Language Models through Online Preference Optimisation. CoRR (2024)
Alaa Saade, Steven Kapturowski, Daniele Calandriello, Charles Blundell, Pablo Sprechmann, Leopoldo Sarra, Oliver Groth, Michal Valko, Bilal Piot
Unlocking the Power of Representations in Long-term Novelty-based Exploration. ICLR (2024)
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
Offline Regularised Reinforcement Learning for Large Language Models Alignment. CoRR (2024)
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
Nash Learning from Human Feedback. CoRR (2023)
Alaa Saade, Steven Kapturowski, Daniele Calandriello, Charles Blundell, Pablo Sprechmann, Leopoldo Sarra, Oliver Groth, Michal Valko, Bilal Piot
Unlocking the Power of Representations in Long-term Novelty-based Exploration. CoRR (2023)
Pierre Harvey Richemond, Allison C. Tam, Yunhao Tang, Florian Strub, Bilal Piot, Felix Hill
The Edge of Orthogonality: A Simple View of What Makes BYOL Tick. ICML (2023)
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
Understanding Self-Predictive Learning for Reinforcement Learning. ICML (2023)
Pierre H. Richemond, Allison C. Tam, Yunhao Tang, Florian Strub, Bilal Piot, Felix Hill
The Edge of Orthogonality: A Simple View of What Makes BYOL Tick. CoRR (2023)
Mohammad Gheshlaghi Azar, Mark Rowland, Bilal Piot, Daniel Guo, Daniele Calandriello, Michal Valko, Rémi Munos
A General Theoretical Paradigm to Understand Learning from Human Preferences. CoRR (2023)
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
BYOL-Explore: Exploration by Bootstrapped Prediction. NeurIPS (2022)
Zhaohan Daniel 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
BYOL-Explore: Exploration by Bootstrapped Prediction. CoRR (2022)
Rahma Chaabouni, Florian Strub, Florent Altché, Eugene Tarassov, Corentin Tallec, Elnaz Davoodi, Kory Wallace Mathewson, Olivier Tieleman, Angeliki Lazaridou, Bilal Piot
Emergent Communication at Scale. ICLR (2022)
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
Understanding Self-Predictive Learning for Reinforcement Learning. CoRR (2022)
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
Mastering the Game of Stratego with Model-Free Multiagent Reinforcement Learning. CoRR (2022)
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
Shaking the foundations: delusions in sequence models for interaction and control. CoRR (2021)
Zhaohan Daniel Guo, Mohammad Gheshlaghi Azar, Alaa Saade, Shantanu Thakoor, Bilal Piot, Bernardo Ávila Pires, Michal Valko, Thomas Mesnard, Tor Lattimore, Rémi Munos
Geometric Entropic Exploration. CoRR (2021)
Adrià Puigdomènech Badia, Bilal Piot, Steven Kapturowski, Pablo Sprechmann, Alex Vitvitskyi, Zhaohan Daniel Guo, Charles Blundell
Agent57: Outperforming the Atari Human Benchmark. ICML (2020)
Adrià Puigdomènech Badia, Bilal Piot, Steven Kapturowski, Pablo Sprechmann, Alex Vitvitskyi, Zhaohan Daniel Guo, Charles Blundell
Agent57: Outperforming the Atari Human Benchmark. CoRR (2020)
Zhaohan Daniel Guo, Bernardo Ávila Pires, Bilal Piot, Jean-Bastien Grill, Florent Altché, Rémi Munos, Mohammad Gheshlaghi Azar
Bootstrap Latent-Predictive Representations for Multitask Reinforcement Learning. ICML (2020)
Jean-Bastien Grill, Florian Strub, Florent Altché, Corentin Tallec, Pierre H. Richemond, Elena Buchatskaya, Carl Doersch, Bernardo Ávila Pires, Zhaohan Daniel Guo, Mohammad Gheshlaghi Azar, Bilal Piot, Koray Kavukcuoglu, Rémi Munos, Michal Valko
Bootstrap Your Own Latent: A New Approach to Self-Supervised Learning. CoRR (2020)
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
BYOL works even without batch statistics. CoRR (2020)
Adrià Puigdomènech Badia, Pablo Sprechmann, Alex Vitvitskyi, Zhaohan Daniel Guo, Bilal Piot, Steven Kapturowski, Olivier Tieleman, Martín Arjovsky, Alexander Pritzel, Andrew Bolt, Charles Blundell
Never Give Up: Learning Directed Exploration Strategies. CoRR (2020)
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
Bootstrap Your Own Latent - A New Approach to Self-Supervised Learning. NeurIPS (2020)
Adrià Puigdomènech Badia, Pablo Sprechmann, Alex Vitvitskyi, Zhaohan Daniel Guo, Bilal Piot, Steven Kapturowski, Olivier Tieleman, Martín Arjovsky, Alexander Pritzel, Andrew Bolt, Charles Blundell
Never Give Up: Learning Directed Exploration Strategies. ICLR (2020)
Matt Hoffman, Bobak Shahriari, John Aslanides, Gabriel Barth-Maron, Feryal 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
Acme: A Research Framework for Distributed Reinforcement Learning. CoRR (2020)
Zhaohan Daniel Guo, Bernardo Ávila Pires, Bilal Piot, Jean-Bastien Grill, Florent Altché, Rémi Munos, Mohammad Gheshlaghi Azar
Bootstrap Latent-Predictive Representations for Multitask Reinforcement Learning. CoRR (2020)
Diana Borsa, Nicolas Heess, Bilal Piot, Siqi Liu, Leonard Hasenclever, Rémi Munos, Olivier Pietquin
Observational Learning by Reinforcement Learning. AAMAS (2019)
Anna Harutyunyan, Will Dabney, Thomas Mesnard, Mohammad Gheshlaghi Azar, Bilal Piot, Nicolas Heess, Hado van Hasselt, Gregory Wayne, Satinder Singh, Doina Precup, Rémi Munos
Hindsight Credit Assignment. NeurIPS (2019)
Anna Harutyunyan, Will Dabney, Thomas Mesnard, Mohammad Gheshlaghi Azar, Bilal Piot, Nicolas Heess, Hado van Hasselt, Greg Wayne, Satinder Singh, Doina Precup, Rémi Munos
Hindsight Credit Assignment. CoRR (2019)
Mohammad Gheshlaghi Azar, Bilal Piot, Bernardo A. Pires, Jean-Bastien Grill, Florent Altché, Rémi Munos
World Discovery Models. CoRR (2019)
Julien Pérolat, Bilal Piot, Olivier Pietquin
Actor-Critic Fictitious Play in Simultaneous Move Multistage Games. AISTATS (2018)
Julien Pérolat, Mateusz Malinowski, Bilal Piot, Olivier Pietquin
Playing the Game of Universal Adversarial Perturbations. CoRR (2018)
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
Observe and Look Further: Achieving Consistent Performance on Atari. CoRR (2018)
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
Deep Q-learning From Demonstrations. AAAI (2018)
Matteo Hessel, Joseph Modayil, Hado van Hasselt, Tom Schaul, Georg Ostrovski, Will Dabney, Dan Horgan, Bilal Piot, Mohammad Gheshlaghi Azar, David Silver
Rainbow: Combining Improvements in Deep Reinforcement Learning. AAAI (2018)
Zhaohan Daniel Guo, Mohammad Gheshlaghi Azar, Bilal Piot, Bernardo A. Pires, Toby Pohlen, Rémi Munos
Neural Predictive Belief Representations. CoRR (2018)
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
Noisy Networks For Exploration. ICLR (Poster) (2018)
Audrunas Gruslys, Will Dabney, Mohammad Gheshlaghi Azar, Bilal Piot, Marc G. Bellemare, Rémi Munos
The Reactor: A fast and sample-efficient Actor-Critic agent for Reinforcement Learning. ICLR (Poster) (2018)
Julien Pérolat, Florian Strub, Bilal Piot, Olivier Pietquin
Learning Nash Equilibrium for General-Sum Markov Games from Batch Data. AISTATS (2017)
Bilal Piot, Matthieu Geist, Olivier Pietquin
Bridging the Gap Between Imitation Learning and Inverse Reinforcement Learning. IEEE Trans. Neural Networks Learn. Syst. 28 (8) (2017)
Diana Borsa, Bilal Piot, Rémi Munos, Olivier Pietquin
Observational Learning by Reinforcement Learning. CoRR (2017)
Matthieu Geist, Bilal Piot, Olivier Pietquin
Is the Bellman residual a bad proxy? NIPS (2017)
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
Learning from Demonstrations for Real World Reinforcement Learning. CoRR (2017)
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
Noisy Networks for Exploration. CoRR (2017)
Florian Strub, Harm de Vries, Jérémie Mary, Bilal Piot, Aaron C. Courville, Olivier Pietquin
End-to-end optimization of goal-driven and visually grounded dialogue systems. IJCAI (2017)
Matej Vecerík, Todd Hester, Jonathan Scholz, Fumin Wang, Olivier Pietquin, Bilal Piot, Nicolas Heess, Thomas Rothörl, Thomas Lampe, Martin A. Riedmiller
Leveraging Demonstrations for Deep Reinforcement Learning on Robotics Problems with Sparse Rewards. CoRR (2017)
Florian Strub, Harm de Vries, Jérémie Mary, Bilal Piot, Aaron C. Courville, Olivier Pietquin
End-to-end optimization of goal-driven and visually grounded dialogue systems. CoRR (2017)
Matteo Hessel, Joseph Modayil, Hado van Hasselt, Tom Schaul, Georg Ostrovski, Will Dabney, Daniel Horgan, Bilal Piot, Mohammad Gheshlaghi Azar, David Silver
Rainbow: Combining Improvements in Deep Reinforcement Learning. CoRR (2017)
Julien Pérolat, Bilal Piot, Bruno Scherrer, Olivier Pietquin
On the Use of Non-Stationary Strategies for Solving Two-Player Zero-Sum Markov Games. AISTATS (2016)
Layla El Asri, Bilal Piot, Matthieu Geist, Romain Laroche, Olivier Pietquin
Score-based Inverse Reinforcement Learning. AAMAS (2016)
Julien Pérolat, Bilal Piot, Matthieu Geist, Bruno Scherrer, Olivier Pietquin
Softened Approximate Policy Iteration for Markov Games. ICML (2016)
Matthieu Geist, Bilal Piot, Olivier Pietquin
Should one minimize the expected Bellman residual or maximize the mean value? CoRR (2016)
Julien Pérolat, Florian Strub, Bilal Piot, Olivier Pietquin
Learning Nash Equilibrium for General-Sum Markov Games from Batch Data. CoRR (2016)
Bilal Piot, Matthieu Geist, Olivier Pietquin
Difference of Convex Functions Programming Applied to Control with Expert Data. CoRR (2016)
Bilal Piot, Olivier Pietquin, Matthieu Geist
Imitation Learning Applied to Embodied Conversational Agents. MLIS@ICML (2015)
Thibaut Munzer, Bilal Piot, Matthieu Geist, Olivier Pietquin, Manuel Lopes
Inverse Reinforcement Learning in Relational Domains. IJCAI (2015)
Julien Pérolat, Bruno Scherrer, Bilal Piot, Olivier Pietquin
Approximate Dynamic Programming for Two-Player Zero-Sum Markov Games. ICML (2015)
Bilal Piot, Matthieu Geist, Olivier Pietquin
Boosted Bellman Residual Minimization Handling Expert Demonstrations. ECML/PKDD (2) (2014)
Bilal Piot, Olivier Pietquin, Matthieu Geist
Predicting when to laugh with structured classification. INTERSPEECH (2014)
Bilal Piot, Matthieu Geist, Olivier Pietquin
Difference of Convex Functions Programming for Reinforcement Learning. NIPS (2014)
Bilal Piot, Matthieu Geist, Olivier Pietquin
Boosted and reward-regularized classification for apprenticeship learning. AAMAS (2014)
Bilal Piot, Matthieu Geist, Olivier Pietquin
Learning from Demonstrations: Is It Worth Estimating a Reward Function? ECML/PKDD (1) (2013)
Edouard Klein, Bilal Piot, Matthieu Geist, Olivier Pietquin
Classification structurée pour l'apprentissage par renforcement inverse. Rev. d'Intelligence Artif. 27 (2) (2013)
Edouard Klein, Bilal Piot, Matthieu Geist, Olivier Pietquin
A Cascaded Supervised Learning Approach to Inverse Reinforcement Learning. ECML/PKDD (1) (2013)
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
Laugh-aware virtual agent and its impact on user amusement. AAMAS (2013)
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
Laugh When You're Winning. eNTERFACE (2013)
Edouard Klein, Matthieu Geist, Bilal Piot, Olivier Pietquin
Inverse Reinforcement Learning through Structured Classification. NIPS (2012)