Statistical linear estimation with penalized estimators: an application to reinforcement learning. - Bernardo Ávila Pires; Csaba Szepesvári (ICML, 2012)

Cost-sensitive Multiclass Classification Risk Bounds. - Bernardo Ávila Pires; Csaba Szepesvári; Mohammad Ghavamzadeh (ICML (3), 2013)

Pseudo-MDPs and factored linear action models. - Hengshuai Yao; Csaba Szepesvári; Bernardo Ávila Pires; Xinhua Zhang (ADPRL, 2014)

Pathological Effects of Variance on Classification-Based Policy Iteration. - Bernardo Ávila Pires; Csaba Szepesvári (AAAI Workshop: Learning for General Competency in Video Games, 2015)

Policy Error Bounds for Model-Based Reinforcement Learning with Factored Linear Models. - Bernardo Ávila Pires (COLT, 2016)

Policy Error Bounds for Model-Based Reinforcement Learning with Factored Linear Models. - Bernardo Ávila Pires; Csaba Szepesvári (CoRR, 2016)

Multiclass Classification Calibration Functions. - Bernardo Ávila Pires; Csaba Szepesvári (CoRR, 2016)

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

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

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)

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)

Neural Recursive Belief States in Multi-Agent Reinforcement Learning. - Pol Moreno; Edward Hughes 001; Kevin R. McKee; Bernardo Ávila Pires; Théophane Weber (CoRR, 2021)

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)

The Nature of Temporal Difference Errors in Multi-step Distributional Reinforcement Learning. - Yunhao Tang; Rémi Munos; Mark Rowland 001; Bernardo Ávila Pires; Will Dabney; Marc G. Bellemare (NeurIPS, 2022)

Understanding Plasticity in Neural Networks. - Clare Lyle; Zeyu Zheng; Evgenii Nikishin; Bernardo Ávila Pires; Razvan Pascanu; Will Dabney (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)

DoMo-AC: Doubly Multi-step Off-policy Actor-Critic Algorithm. - Yunhao Tang; Tadashi Kozuno; Mark Rowland 001; Anna Harutyunyan; Rémi Munos; Bernardo Ávila Pires; Michal Valko (ICML, 2023)

Hierarchical Reinforcement Learning in Complex 3D Environments. - Bernardo Ávila Pires; Feryal M. P. Behbahani; Hubert Soyer; Kyriacos Nikiforou; Thomas Keck; Satinder Singh 001 (CoRR, 2023)

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)

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)

Off-policy Distributional Q(λ): Distributional RL without Importance Sampling. - Yunhao Tang; Mark Rowland 001; Rémi Munos; Bernardo Ávila Pires; Will Dabney (CoRR, 2024)

Understanding the performance gap between online and offline alignment algorithms. - Yunhao Tang; Zhaohan Daniel Guo; Zeyu Zheng; Daniele Calandriello; Yuan Cao; Eugene Tarassov; Rémi Munos; Bernardo Ávila Pires; Michal Valko; Yong Cheng; Will Dabney (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)

A Unifying Framework for Action-Conditional Self-Predictive Reinforcement Learning. - Khimya Khetarpal; Zhaohan Daniel Guo; Bernardo Ávila Pires; Yunhao Tang; Clare Lyle; Mark Rowland 001; Nicolas Heess; Diana L. Borsa; Arthur Guez; Will Dabney (AISTATS, 2025)

Optimizing Return Distributions with Distributional Dynamic Programming. - Bernardo Ávila Pires; Mark Rowland 001; Diana Borsa; Zhaohan Daniel Guo; Khimya Khetarpal; André Barreto 001; David Abel; Rémi Munos; Will Dabney (CoRR, 2025)

Representation Learning via Non-Contrastive Mutual Information. - Zhaohan Daniel Guo; Bernardo Ávila Pires; Khimya Khetarpal; Dale Schuurmans; Bo Dai 001 (CoRR, 2025)