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

Policy-Conditioned Uncertainty Sets for Robust Markov Decision Processes. - Andrea Tirinzoni; Marek Petrik; Xiangli Chen; Brian D. Ziebart (NeurIPS, 2018)

Transfer of Value Functions via Variational Methods. - Andrea Tirinzoni; Rafael Rodríguez-Sánchez 002; Marcello Restelli (NeurIPS, 2018)

Transfer of Samples in Policy Search via Multiple Importance Sampling. - Andrea Tirinzoni; Mattia Salvini; Marcello Restelli (ICML, 2019)

Feature Selection via Mutual Information: New Theoretical Insights. - Mario Beraha; Alberto Maria Metelli; Matteo Papini; Andrea Tirinzoni; Marcello Restelli (IJCNN, 2019)

Gradient-Aware Model-Based Policy Search. - Pierluca D'Oro; Alberto Maria Metelli; Andrea Tirinzoni; Matteo Papini; Marcello Restelli (AAAI, 2020)

Truly Batch Model-Free Inverse Reinforcement Learning about Multiple Intentions. - Giorgia Ramponi; Amarildo Likmeta; Alberto Maria Metelli; Andrea Tirinzoni; Marcello Restelli (AISTATS, 2020)

A Novel Confidence-Based Algorithm for Structured Bandits. - Andrea Tirinzoni; Alessandro Lazaric; Marcello Restelli (AISTATS, 2020)

Sequential Transfer in Reinforcement Learning with a Generative Model. - Andrea Tirinzoni; Riccardo Poiani; Marcello Restelli (ICML, 2020)

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

Combining reinforcement learning with rule-based controllers for transparent and general decision-making in autonomous driving. - Amarildo Likmeta; Alberto Maria Metelli; Andrea Tirinzoni; Riccardo Giol; Marcello Restelli; Danilo Romano (Robotics Auton. Syst., 2020)

Leveraging Good Representations in Linear Contextual Bandits. - Matteo Papini; Andrea Tirinzoni; Marcello Restelli; Alessandro Lazaric; Matteo Pirotta (ICML, 2021)

Meta-Reinforcement Learning by Tracking Task Non-stationarity. - Riccardo Poiani; Andrea Tirinzoni; Marcello Restelli (IJCAI, 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)

Dealing With Misspecification In Fixed-Confidence Linear Top-m Identification. - Clémence Réda; Andrea Tirinzoni; Rémy Degenne (NeurIPS, 2021)

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

Dealing with multiple experts and non-stationarity in inverse reinforcement learning: an application to real-life problems. - Amarildo Likmeta; Alberto Maria Metelli; Giorgia Ramponi; Andrea Tirinzoni; Matteo Giuliani; Marcello Restelli (Mach. Learn., 2021)

Exploiting structure for transfer in reinforcement learning. - Andrea Tirinzoni (2021)

On Elimination Strategies for Bandit Fixed-Confidence Identification. - Andrea Tirinzoni; Rémy Degenne (NeurIPS, 2022)

Near Instance-Optimal PAC Reinforcement Learning for Deterministic MDPs. - Andrea Tirinzoni; Aymen Al Marjani; Emilie Kaufmann (NeurIPS, 2022)

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

Risk-averse policy optimization via risk-neutral policy optimization. - Lorenzo Bisi; Davide Santambrogio; Federico Sandrelli; Andrea Tirinzoni; Brian D. Ziebart; Marcello Restelli (Artif. Intell., 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)

Optimistic PAC Reinforcement Learning: the Instance-Dependent View. - Andrea Tirinzoni; Aymen Al Marjani; Emilie Kaufmann (ALT, 2023)

Active Coverage for PAC Reinforcement Learning. - Aymen Al Marjani; Andrea Tirinzoni; Emilie Kaufmann (COLT, 2023)

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

Towards Instance-Optimality in Online PAC Reinforcement Learning. - Aymen Al Marjani; Andrea Tirinzoni; Emilie Kaufmann (CoRR, 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)