Fitted policy search. - Martino Migliavacca; Alessio Pecorino; Matteo Pirotta; Marcello Restelli; Andrea Bonarini (ADPRL, 2011)

Safe Policy Iteration. - Matteo Pirotta; Marcello Restelli; Alessio Pecorino; Daniele Calandriello (ICML (3), 2013)

Adaptive Step-Size for Policy Gradient Methods. - Matteo Pirotta; Marcello Restelli; Luca Bascetta (NIPS, 2013)

Policy gradient approaches for multi-objective sequential decision making: A comparison. - Simone Parisi; Matteo Pirotta; Nicola Smacchia; Luca Bascetta; Marcello Restelli (ADPRL, 2014)

Policy gradient approaches for multi-objective sequential decision making. - Simone Parisi; Matteo Pirotta; Nicola Smacchia; Luca Bascetta; Marcello Restelli (IJCNN, 2014)

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

Optimal control to reduce emissions in gasoline engines: an iterative learning control approach for ECU calibration maps improvement. - Danilo Caporale; Luca Deori; Roberto Mura; Alessandro Falsone; Riccardo Vignali; Luca Giulioni; Matteo Pirotta; Giorgio Manganini (ECC, 2015)

Following Newton direction in Policy Gradient with parameter exploration. - Giorgio Manganini; Matteo Pirotta; Marcello Restelli; Luca Bascetta (IJCNN, 2015)

Policy gradient in Lipschitz Markov Decision Processes. - Matteo Pirotta; Marcello Restelli; Luca Bascetta (Mach. Learn., 2015)

Inverse Reinforcement Learning through Policy Gradient Minimization. - Matteo Pirotta; Marcello Restelli (AAAI, 2016)

Multi-objective Reinforcement Learning through Continuous Pareto Manifold Approximation. - Simone Parisi; Matteo Pirotta; Marcello Restelli (J. Artif. Intell. Res., 2016)

Policy Search for the Optimal Control of Markov Decision Processes: A Novel Particle-Based Iterative Scheme. - Giorgio Manganini; Matteo Pirotta; Marcello Restelli; Luigi Piroddi; Maria Prandini (IEEE Trans. Cybern., 2016)

Reinforcement learning: from theory to algorithms. - Matteo Pirotta (2016)

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

Boosted Fitted Q-Iteration. - Samuele Tosatto; Matteo Pirotta; Carlo D'Eramo; Marcello Restelli (ICML, 2017)

Regret Minimization in MDPs with Options without Prior Knowledge. - Ronan Fruit; Matteo Pirotta; Alessandro Lazaric; Emma Brunskill (NIPS, 2017)

Compatible Reward Inverse Reinforcement Learning. - Alberto Maria Metelli; Matteo Pirotta; Marcello Restelli (NIPS, 2017)

Adaptive Batch Size for Safe Policy Gradients. - Matteo Papini; Matteo Pirotta; Marcello Restelli (NIPS, 2017)

Gradient-based minimization for multi-expert Inverse Reinforcement Learning. - Davide Tateo; Matteo Pirotta; Marcello Restelli; Andrea Bonarini (SSCI, 2017)

Cost-Sensitive Approach to Batch Size Adaptation for Gradient Descent. - Matteo Pirotta; Marcello Restelli (CoRR, 2017)

Manifold-based multi-objective policy search with sample reuse. - Simone Parisi; Matteo Pirotta; Jan Peters 001 (Neurocomputing, 2017)

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

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

Importance Weighted Transfer of Samples in Reinforcement Learning. - Andrea Tirinzoni; Andrea Sessa; Matteo Pirotta; Marcello Restelli (ICML, 2018)

Does Reinforcement Learning outperform PID in the control of FES-induced elbow flex-extension? - Davide Di Febbo; Emilia Ambrosini; Matteo Pirotta; Eric Rojas 002; Marcello Restelli; Alessandra Laura Giulia Pedrocchi; Simona Ferrante (MeMeA, 2018)

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

Exploration Bonus for Regret Minimization in Undiscounted Discrete and Continuous Markov Decision Processes. - Jian Qian; Ronan Fruit; Matteo Pirotta; Alessandro Lazaric (CoRR, 2018)

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

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

Frequentist Regret Bounds for Randomized Least-Squares Value Iteration. - Andrea Zanette; David Brandfonbrener; Matteo Pirotta; Alessandro Lazaric (CoRR, 2019)

Improved Algorithms for Conservative Exploration in Bandits. - Evrard Garcelon; Mohammad Ghavamzadeh; Alessandro Lazaric; Matteo Pirotta (AAAI, 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 (AISTATS, 2020)

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

Adversarial Attacks on Linear Contextual Bandits. - Evrard Garcelon; Baptiste Rozière; Laurent Meunier; Jean Tarbouriech; Olivier Teytaud; Alessandro Lazaric; Matteo Pirotta (NeurIPS, 2020)

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

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

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

Exploiting Language Instructions for Interpretable and Compositional Reinforcement Learning. - Michiel van der Meer; Matteo Pirotta; Elia Bruni (CoRR, 2020)

Concentration Inequalities for Multinoulli Random Variables. - Jian Qian; Ronan Fruit; Matteo Pirotta; Alessandro Lazaric (CoRR, 2020)

Exploration-Exploitation in Constrained MDPs. - Yonathan Efroni; Shie Mannor; Matteo Pirotta (CoRR, 2020)

Regret Bounds for Kernel-Based Reinforcement Learning. - Omar Darwiche Domingues; Pierre Ménard; Matteo Pirotta; Emilie Kaufmann; Michal Valko (CoRR, 2020)

Learning Adaptive Exploration Strategies in Dynamic Environments Through Informed Policy Regularization. - Pierre-Alexandre Kamienny; Matteo Pirotta; Alessandro Lazaric; Thibault Lavril; Nicolas Usunier; Ludovic Denoyer (CoRR, 2020)

Improved Analysis of UCRL2 with Empirical Bernstein Inequality. - Ronan Fruit; Matteo Pirotta; Alessandro Lazaric (CoRR, 2020)

Local Differentially Private Regret Minimization in Reinforcement Learning. - Evrard Garcelon; Vianney Perchet; Ciara Pike-Burke; Matteo Pirotta (CoRR, 2020)

On the use of the policy gradient and Hessian in inverse reinforcement learning. - Alberto Maria Metelli; Matteo Pirotta; Marcello Restelli (Intelligenza Artificiale, 2020)

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

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

Kernel-Based Reinforcement Learning: A Finite-Time Analysis. - Omar Darwiche Domingues; Pierre Ménard; 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 (ICML, 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 (NeurIPS, 2021)

A Provably Efficient Sample Collection Strategy for Reinforcement Learning. - 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 (NeurIPS, 2021)

Homomorphically Encrypted Linear Contextual Bandit. - Evrard Garcelon; Vianney Perchet; Matteo Pirotta (CoRR, 2021)

A Unified Framework for Conservative Exploration. - Yunchang Yang; Tianhao Wu 002; Han Zhong 001; Evrard Garcelon; Matteo Pirotta; Alessandro Lazaric; Liwei Wang 001; Simon S. Du (CoRR, 2021)

A Fully Problem-Dependent Regret Lower Bound for Finite-Horizon MDPs. - Andrea Tirinzoni; Matteo Pirotta; Alessandro Lazaric (CoRR, 2021)

Differentially Private Exploration in Reinforcement Learning with Linear Representation. - Paul Luyo; Evrard Garcelon; Alessandro Lazaric; Matteo Pirotta (CoRR, 2021)

Gaussian Approximation for Bias Reduction in Q-Learning. - Carlo D'Eramo; Andrea Cini; Alessandro Nuara; Matteo Pirotta; Cesare Alippi; Jan Peters 001; Marcello Restelli (J. Mach. Learn. Res., 2021)

Safe Policy Iteration: A Monotonically Improving Approximate Policy Iteration Approach. - Alberto Maria Metelli; Matteo Pirotta; Daniele Calandriello; Marcello Restelli (J. Mach. Learn. Res., 2021)

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

Encrypted Linear Contextual Bandit. - Evrard Garcelon; Matteo Pirotta; Vianney Perchet (AISTATS, 2022)

Adaptive Multi-Goal Exploration. - Jean Tarbouriech; Omar Darwiche Domingues; Pierre Ménard; Matteo Pirotta; Michal Valko; Alessandro Lazaric (AISTATS, 2022)

Privacy Amplification via Shuffling for Linear Contextual Bandits. - Evrard Garcelon; Kamalika Chaudhuri; Vianney Perchet; Matteo Pirotta (ALT, 2022)

A Reduction-Based Framework for Conservative Bandits and Reinforcement Learning. - Yunchang Yang; Tianhao Wu 002; Han Zhong 001; Evrard Garcelon; Matteo Pirotta; Alessandro Lazaric; Liwei Wang 001; Simon Shaolei Du (ICLR, 2022)

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

Improved Adaptive Algorithm for Scalable Active Learning with Weak Labeler. - Yifang Chen; Karthik Abinav Sankararaman; Alessandro Lazaric; Matteo Pirotta; Dmytro Karamshuk; Qifan Wang 001; Karishma Mandyam; Sinong Wang; Han Fang (CoRR, 2022)

Smoothing policies and safe policy gradients. - Matteo Papini; Matteo Pirotta; Marcello Restelli (Mach. Learn., 2022)

On the Complexity of Representation Learning in Contextual Linear Bandits. - Andrea Tirinzoni; Matteo Pirotta; Alessandro Lazaric (AISTATS, 2023)

Reaching Goals is Hard: Settling the Sample Complexity of the Stochastic Shortest Path. - Liyu Chen; Andrea Tirinzoni; Matteo Pirotta; Alessandro Lazaric (ALT, 2023)

Contextual bandits with concave rewards, and an application to fair ranking. - Virginie Do; Elvis Dohmatob; Matteo Pirotta; Alessandro Lazaric; Nicolas Usunier (ICLR, 2023)

Layered State Discovery for Incremental Autonomous Exploration. - Liyu Chen; Andrea Tirinzoni; Alessandro Lazaric; Matteo Pirotta (ICML, 2023)

Group Fairness in Reinforcement Learning. - Harsh Satija; Alessandro Lazaric; Matteo Pirotta; Joelle Pineau (Trans. Mach. Learn. Res., 2023)

Fast Imitation via Behavior Foundation Models. - Matteo Pirotta; Andrea Tirinzoni; Ahmed Touati; Alessandro Lazaric; Yann Ollivier (ICLR, 2024)

Simple Ingredients for Offline Reinforcement Learning. - Edoardo Cetin; Andrea Tirinzoni; Matteo Pirotta; Alessandro Lazaric; Yann Ollivier; Ahmed Touati (ICML, 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, 2025)

Temporal Difference Flows. - Jesse Farebrother; Matteo Pirotta; Andrea Tirinzoni; Rémi Munos; Alessandro Lazaric; Ahmed Touati (CoRR, 2025)

Fast Adaptation with Behavioral Foundation Models. - Harshit Sikchi; Andrea Tirinzoni; Ahmed Touati; Yingchen Xu; Anssi Kanervisto; Scott Niekum; Amy Zhang 001; Alessandro Lazaric; Matteo Pirotta (CoRR, 2025)