Pirotta, Matteo

56 publications

ICLR 2026 BFM-Zero: A Promptable Behavioral Foundation Model for Humanoid Control Using Unsupervised Reinforcement Learning Yitang Li, Zhengyi Luo, Tonghe Zhang, Cunxi Dai, Anssi Kanervisto, Andrea Tirinzoni, Haoyang Weng, Kris Kitani, Mateusz Guzek, Ahmed Touati, Alessandro Lazaric, Matteo Pirotta, Guanya Shi

ICLR 2026 TD-JEPA: Latent-Predictive Representations for Zero-Shot Reinforcement Learning Marco Bagatella, Matteo Pirotta, Ahmed Touati, Alessandro Lazaric, Andrea Tirinzoni

ICML 2025 Temporal Difference Flows Jesse Farebrother, Matteo Pirotta, Andrea Tirinzoni, Remi Munos, Alessandro Lazaric, Ahmed Touati

ICLRW 2025 Temporal Difference Flows Jesse Farebrother, Matteo Pirotta, Andrea Tirinzoni, Remi Munos, Alessandro Lazaric, Ahmed Touati

ICLR 2025 Zero-Shot Whole-Body Humanoid Control via Behavioral Foundation Models Andrea Tirinzoni, Ahmed Touati, Jesse Farebrother, Mateusz Guzek, Anssi Kanervisto, Yingchen Xu, Alessandro Lazaric, Matteo Pirotta

ICLR 2024 Fast Imitation via Behavior Foundation Models Matteo Pirotta, Andrea Tirinzoni, Ahmed Touati, Alessandro Lazaric, Yann Ollivier

ICML 2024 Simple Ingredients for Offline Reinforcement Learning Edoardo Cetin, Andrea Tirinzoni, Matteo Pirotta, Alessandro Lazaric, Yann Ollivier, Ahmed Touati

NeurIPSW 2024 Zero-Shot Whole-Body Humanoid Control via Behavioral Foundation Models Andrea Tirinzoni, Ahmed Touati, Jesse Farebrother, Mateusz Guzek, Anssi Kanervisto, Yingchen Xu, Alessandro Lazaric, Matteo Pirotta

ICLR 2023 Contextual Bandits with Concave Rewards, and an Application to Fair Ranking Virginie Do, Elvis Dohmatob, Matteo Pirotta, Alessandro Lazaric, Nicolas Usunier

NeurIPSW 2023 Fast Imitation via Behavior Foundation Models Matteo Pirotta, Andrea Tirinzoni, Ahmed Touati, Alessandro Lazaric, Yann Ollivier

TMLR 2023 Group Fairness in Reinforcement Learning Harsh Satija, Alessandro Lazaric, Matteo Pirotta, Joelle Pineau

ICML 2023 Layered State Discovery for Incremental Autonomous Exploration Liyu Chen, Andrea Tirinzoni, Alessandro Lazaric, Matteo Pirotta

AISTATS 2023 On the Complexity of Representation Learning in Contextual Linear Bandits Andrea Tirinzoni, Matteo Pirotta, Alessandro Lazaric

ALT 2023 Reaching Goals Is Hard: Settling the Sample Complexity of the Stochastic Shortest Path Liyu Chen, Andrea Tirinzoni, Matteo Pirotta, Alessandro Lazaric

AISTATS 2022 Adaptive Multi-Goal Exploration Jean Tarbouriech, Omar Darwiche Domingues, Pierre Menard, Matteo Pirotta, Michal Valko, Alessandro Lazaric

AISTATS 2022 Encrypted Linear Contextual Bandit Evrard Garcelon, Matteo Pirotta, Vianney Perchet

AISTATS 2022 Top K Ranking for Multi-Armed Bandit with Noisy Evaluations Evrard Garcelon, Vashist Avadhanula, Alessandro Lazaric, Matteo Pirotta

ICLR 2022 A Reduction-Based Framework for Conservative Bandits and Reinforcement Learning Yunchang Yang, Tianhao Wu, Han Zhong, Evrard Garcelon, Matteo Pirotta, Alessandro Lazaric, Liwei Wang, Simon Shaolei Du

ALT 2022 Privacy Amplification via Shuffling for Linear Contextual Bandits Evrard Garcelon, Kamalika Chaudhuri, Vianney Perchet, Matteo Pirotta

NeurIPS 2022 Scalable Representation Learning in Linear Contextual Bandits with Constant Regret Guarantees Andrea Tirinzoni, Matteo Papini, Ahmed Touati, Alessandro Lazaric, Matteo Pirotta

MLJ 2022 Smoothing Policies and Safe Policy Gradients Matteo Papini, Matteo Pirotta, Marcello Restelli

AISTATS 2021 A Kernel-Based Approach to Non-Stationary Reinforcement Learning in Metric Spaces Omar Darwiche Domingues, Pierre Menard, Matteo Pirotta, Emilie Kaufmann, Michal Valko

NeurIPS 2021 A Provably Efficient Sample Collection Strategy for Reinforcement Learning Jean Tarbouriech, Matteo Pirotta, Michal Valko, Alessandro Lazaric

JMLR 2021 Gaussian Approximation for Bias Reduction in Q-Learning Carlo D'Eramo, Andrea Cini, Alessandro Nuara, Matteo Pirotta, Cesare Alippi, Jan Peters, Marcello Restelli

ICML 2021 Kernel-Based Reinforcement Learning: A Finite-Time Analysis Omar Darwiche Domingues, Pierre Menard, Matteo Pirotta, Emilie Kaufmann, Michal Valko

ICML 2021 Leveraging Good Representations in Linear Contextual Bandits Matteo Papini, Andrea Tirinzoni, Marcello Restelli, Alessandro Lazaric, Matteo Pirotta

NeurIPS 2021 Local Differential Privacy for Regret Minimization in Reinforcement Learning Evrard Garcelon, Vianney Perchet, Ciara Pike-Burke, Matteo Pirotta

NeurIPS 2021 Reinforcement Learning in Linear MDPs: Constant Regret and Representation Selection Matteo Papini, Andrea Tirinzoni, Aldo Pacchiano, Marcello Restelli, Alessandro Lazaric, Matteo Pirotta

JMLR 2021 Safe Policy Iteration: A Monotonically Improving Approximate Policy Iteration Approach Alberto Maria Metelli, Matteo Pirotta, Daniele Calandriello, Marcello Restelli

ALT 2021 Sample Complexity Bounds for Stochastic Shortest Path with a Generative Model Jean Tarbouriech, Matteo Pirotta, Michal Valko, Alessandro Lazaric

NeurIPS 2021 Stochastic Shortest Path: Minimax, Parameter-Free and Towards Horizon-Free Regret Jean Tarbouriech, Runlong Zhou, Simon S Du, Matteo Pirotta, Michal Valko, Alessandro Lazaric

UAI 2020 Active Model Estimation in Markov Decision Processes Jean Tarbouriech, Shubhanshu Shekhar, Matteo Pirotta, Mohammad Ghavamzadeh, Alessandro Lazaric

NeurIPS 2020 Adversarial Attacks on Linear Contextual Bandits Evrard Garcelon, Baptiste Roziere, Laurent Meunier, Jean Tarbouriech, Olivier Teytaud, Alessandro Lazaric, Matteo Pirotta

NeurIPS 2020 An Asymptotically Optimal Primal-Dual Incremental Algorithm for Contextual Linear Bandits Andrea Tirinzoni, Matteo Pirotta, Marcello Restelli, Alessandro Lazaric

AISTATS 2020 Conservative Exploration in Reinforcement Learning Evrard Garcelon, Mohammad Ghavamzadeh, Alessandro Lazaric, Matteo Pirotta

AISTATS 2020 Frequentist Regret Bounds for Randomized Least-Squares Value Iteration Andrea Zanette, David Brandfonbrener, Emma Brunskill, Matteo Pirotta, Alessandro Lazaric

AAAI 2020 Improved Algorithms for Conservative Exploration in Bandits Evrard Garcelon, Mohammad Ghavamzadeh, Alessandro Lazaric, Matteo Pirotta

NeurIPS 2020 Improved Sample Complexity for Incremental Autonomous Exploration in MDPs Jean Tarbouriech, Matteo Pirotta, Michal Valko, Alessandro Lazaric

ICML 2020 No-Regret Exploration in Goal-Oriented Reinforcement Learning Jean Tarbouriech, Evrard Garcelon, Michal Valko, Matteo Pirotta, Alessandro Lazaric

NeurIPS 2019 Exploration Bonus for Regret Minimization in Discrete and Continuous Average Reward MDPs Jian Qian, Ronan Fruit, Matteo Pirotta, Alessandro Lazaric

NeurIPS 2019 Regret Bounds for Learning State Representations in Reinforcement Learning Ronald Ortner, Matteo Pirotta, Alessandro Lazaric, Ronan Fruit, Odalric-Ambrym Maillard

ICML 2018 Efficient Bias-Span-Constrained Exploration-Exploitation in Reinforcement Learning Ronan Fruit, Matteo Pirotta, Alessandro Lazaric, Ronald Ortner

ICML 2018 Importance Weighted Transfer of Samples in Reinforcement Learning Andrea Tirinzoni, Andrea Sessa, Matteo Pirotta, Marcello Restelli

NeurIPS 2018 Near Optimal Exploration-Exploitation in Non-Communicating Markov Decision Processes Ronan Fruit, Matteo Pirotta, Alessandro Lazaric

ICML 2018 Stochastic Variance-Reduced Policy Gradient Matteo Papini, Damiano Binaghi, Giuseppe Canonaco, Matteo Pirotta, Marcello Restelli

NeurIPS 2017 Adaptive Batch Size for Safe Policy Gradients Matteo Papini, Matteo Pirotta, Marcello Restelli

ICML 2017 Boosted Fitted Q-Iteration Samuele Tosatto, Matteo Pirotta, Carlo D’Eramo, Marcello Restelli

NeurIPS 2017 Compatible Reward Inverse Reinforcement Learning Alberto Maria Metelli, Matteo Pirotta, Marcello Restelli

AAAI 2017 Estimating the Maximum Expected Value in Continuous Reinforcement Learning Problems Carlo D'Eramo, Alessandro Nuara, Matteo Pirotta, Marcello Restelli

NeurIPS 2017 Regret Minimization in MDPs with Options Without Prior Knowledge Ronan Fruit, Matteo Pirotta, Alessandro Lazaric, Emma Brunskill

AAAI 2016 Inverse Reinforcement Learning Through Policy Gradient Minimization Matteo Pirotta, Marcello Restelli

JAIR 2016 Multi-Objective Reinforcement Learning Through Continuous Pareto Manifold Approximation Simone Parisi, Matteo Pirotta, Marcello Restelli

AAAI 2015 Multi-Objective Reinforcement Learning with Continuous Pareto Frontier Approximation Matteo Pirotta, Simone Parisi, Marcello Restelli

MLJ 2015 Policy Gradient in Lipschitz Markov Decision Processes Matteo Pirotta, Marcello Restelli, Luca Bascetta

NeurIPS 2013 Adaptive Step-Size for Policy Gradient Methods Matteo Pirotta, Marcello Restelli, Luca Bascetta

ICML 2013 Safe Policy Iteration Matteo Pirotta, Marcello Restelli, Alessio Pecorino, Daniele Calandriello