Inverse Reinforcement Learning through Structured Classification. - Edouard Klein; Matthieu Geist; Bilal Piot; Olivier Pietquin (NIPS, 2012)

Laugh-aware virtual agent and its impact on user amusement. - Radoslaw Niewiadomski; Jennifer Hofmann; Jérôme Urbain; Tracey Platt; Johannes Wagner 001; Bilal Piot; Hüseyin Çakmak; Sathish Pammi; Tobias Baur 001; Stéphane Dupont; Matthieu Geist; Florian Lingenfelser; et al. (AAMAS, 2013)

Laugh When You're Winning. - Maurizio Mancini; Laurent Ach; Emeline Bantegnie; Tobias Baur 001; Nadia Berthouze; Debajyoti Datta; Yu Ding 001; Stéphane Dupont; Harry J. Griffin; Florian Lingenfelser; Radoslaw Niewiadomski; Catherine Pelachaud; et al. (eNTERFACE, 2013)

A Cascaded Supervised Learning Approach to Inverse Reinforcement Learning. - Edouard Klein; Bilal Piot; Matthieu Geist; Olivier Pietquin (ECML/PKDD (1), 2013)

Learning from Demonstrations: Is It Worth Estimating a Reward Function? - Bilal Piot; Matthieu Geist; Olivier Pietquin (ECML/PKDD (1), 2013)

Classification structurée pour l'apprentissage par renforcement inverse. - Edouard Klein; Bilal Piot; Matthieu Geist; Olivier Pietquin (Rev. d'Intelligence Artif., 2013)

Boosted and reward-regularized classification for apprenticeship learning. - Bilal Piot; Matthieu Geist; Olivier Pietquin (AAMAS, 2014)

Predicting when to laugh with structured classification. - Bilal Piot; Olivier Pietquin; Matthieu Geist (INTERSPEECH, 2014)

Difference of Convex Functions Programming for Reinforcement Learning. - Bilal Piot; Matthieu Geist; Olivier Pietquin (NIPS, 2014)

Boosted Bellman Residual Minimization Handling Expert Demonstrations. - Bilal Piot; Matthieu Geist; Olivier Pietquin (ECML/PKDD (2), 2014)

Approximate Dynamic Programming for Two-Player Zero-Sum Markov Games. - Julien Pérolat; Bruno Scherrer; Bilal Piot; Olivier Pietquin (ICML, 2015)

Imitation Learning Applied to Embodied Conversational Agents. - Bilal Piot; Olivier Pietquin; Matthieu Geist (MLIS@ICML, 2015)

Inverse Reinforcement Learning in Relational Domains. - Thibaut Munzer; Bilal Piot; Matthieu Geist; Olivier Pietquin; Manuel Lopes 001 (IJCAI, 2015)

On the Use of Non-Stationary Strategies for Solving Two-Player Zero-Sum Markov Games. - Julien Pérolat; Bilal Piot; Bruno Scherrer; Olivier Pietquin (AISTATS, 2016)

Score-based Inverse Reinforcement Learning. - Layla El Asri; Bilal Piot; Matthieu Geist; Romain Laroche; Olivier Pietquin (AAMAS, 2016)

Softened Approximate Policy Iteration for Markov Games. - Julien Pérolat; Bilal Piot; Matthieu Geist; Bruno Scherrer; Olivier Pietquin (ICML, 2016)

Should one minimize the expected Bellman residual or maximize the mean value? - Matthieu Geist; Bilal Piot; Olivier Pietquin (CoRR, 2016)

Difference of Convex Functions Programming Applied to Control with Expert Data. - Bilal Piot; Matthieu Geist; Olivier Pietquin (CoRR, 2016)

Learning Nash Equilibrium for General-Sum Markov Games from Batch Data. - Julien Pérolat; Florian Strub; Bilal Piot; Olivier Pietquin (AISTATS, 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 (IJCAI, 2017)

Is the Bellman residual a bad proxy? - Matthieu Geist; Bilal Piot; Olivier Pietquin (NIPS, 2017)

Observational Learning by Reinforcement Learning. - Diana Borsa; Bilal Piot; Rémi Munos; Olivier Pietquin (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)

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)

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)

Bridging the Gap Between Imitation Learning and Inverse Reinforcement Learning. - Bilal Piot; Matthieu Geist; Olivier Pietquin (IEEE Trans. Neural Networks Learn. Syst., 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)

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; et al. (AAAI, 2018)

Actor-Critic Fictitious Play in Simultaneous Move Multistage Games. - Julien Pérolat; Bilal Piot; Olivier Pietquin (AISTATS, 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)

Playing the Game of Universal Adversarial Perturbations. - Julien Pérolat; Mateusz Malinowski; Bilal Piot; Olivier Pietquin (CoRR, 2018)

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

Observational Learning by Reinforcement Learning. - Diana Borsa; Nicolas Heess; Bilal Piot; Siqi Liu 002; Leonard Hasenclever; Rémi Munos; Olivier Pietquin (AAMAS, 2019)

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)

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 (ICLR, 2020)

Agent57: Outperforming the Atari Human Benchmark. - Adrià Puigdomènech Badia; Bilal Piot; Steven Kapturowski; Pablo Sprechmann; Alex Vitvitskyi; Zhaohan Daniel Guo; Charles Blundell (ICML, 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 (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)

Acme: A Research Framework for Distributed Reinforcement Learning. - Matt Hoffman 001; 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 001; et al. (CoRR, 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)

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)

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; et al. (CoRR, 2021)

Emergent Communication at Scale. - Rahma Chaabouni 001; Florian Strub; Florent Altché; Eugene Tarassov; Corentin Tallec; Elnaz Davoodi; Kory Wallace Mathewson; Olivier Tieleman; Angeliki Lazaridou; Bilal Piot (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)

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 001; Stephen McAleer; Romuald Elie; et al. (CoRR, 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 001; Stephen McAleer; Romuald Elie; et al. (Zenodo, 2022)

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 (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)

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 (ICLR, 2024)

Human Alignment of Large Language Models through Online Preference Optimisation. - Daniele Calandriello; Zhaohan Daniel Guo; Rémi Munos; Mark Rowland 001; Yunhao Tang; Bernardo Ávila Pires; Pierre Harvey Richemond; Charline Le Lan; Michal Valko; Tianqi Liu 002; Rishabh Joshi; Zeyu Zheng; et al. (ICML, 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)

Generalized Preference Optimization: A Unified Approach to Offline Alignment. - Yunhao Tang; Zhaohan Daniel Guo; Zeyu Zheng; Daniele Calandriello; Rémi Munos; Mark Rowland 001; Pierre Harvey Richemond; Michal Valko; Bernardo Ávila Pires; Bilal Piot (ICML, 2024)

Multi-turn Reinforcement Learning with Preference Human Feedback. - Lior Shani; Aviv Rosenberg 002; Asaf Cassel; Oran Lang; Daniele Calandriello; Avital Zipori; Hila Noga; Orgad Keller; Bilal Piot; Idan Szpektor; Avinatan Hassidim; Yossi Matias; et al. (NeurIPS, 2024)

Direct Language Model Alignment from Online AI Feedback. - Shangmin Guo; Biao Zhang; Tianlin Liu; Tianqi Liu 002; Misha Khalman; Felipe Llinares; Alexandre Ramé; Thomas Mesnard; Yao Zhao; Bilal Piot; Johan Ferret; Mathieu Blondel (CoRR, 2024)

Multi-turn Reinforcement Learning from Preference Human Feedback. - Lior Shani; Aviv Rosenberg 002; Asaf B. Cassel; Oran Lang; Daniele Calandriello; Avital Zipori; Hila Noga; Orgad Keller; Bilal Piot; Idan Szpektor; Avinatan Hassidim; Yossi Matias; et al. (CoRR, 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)

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; et al. (CoRR, 2024)

RRM: Robust Reward Model Training Mitigates Reward Hacking. - Tianqi Liu 002; Wei Xiong 015; Jie Ren 006; Lichang Chen; Junru Wu; Rishabh Joshi; Yang Gao; Jiaming Shen; Zhen Qin 001; Tianhe Yu; Daniel Sohn; Anastasiia Makarova; et al. (CoRR, 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 002; Wei Xiong 015; Jie Ren 006; Lichang Chen; Junru Wu; Rishabh Joshi; Yang Gao; Jiaming Shen; Zhen Qin 001; Tianhe Yu; Daniel Sohn; Anastasia Makarova; et al. (ICLR, 2025)

Building Math Agents with Multi-Turn Iterative Preference Learning. - Wei Xiong 015; Chengshuai Shi; Jiaming Shen; Aviv Rosenberg 002; Zhen Qin 001; Daniele Calandriello; Misha Khalman; Rishabh Joshi; Bilal Piot; Mohammad Saleh; Chi Jin; Tong Zhang 001; et al. (ICLR, 2025)

Learning from negative feedback, or positive feedback or both. - Abbas Abdolmaleki; Bilal Piot; Bobak Shahriari; Jost Tobias Springenberg; Tim Hertweck; Michael Bloesch; Rishabh Joshi; Thomas Lampe; Junhyuk Oh; Nicolas Heess; Jonas Buchli; Martin A. Riedmiller (ICLR, 2025)

Gemma 3 Technical Report. - Aishwarya Kamath; Johan Ferret; Shreya Pathak; Nino Vieillard; Ramona Merhej; Sarah Perrin; Tatiana Matejovicova; Alexandre Ramé; Morgane Rivière; Louis Rouillard; Thomas Mesnard; Geoffrey Cideron; et al. (CoRR, 2025)