Matteo Pirotta

Publication Activity (10 Years)

Years Active: 2011-2024
Publications (10 Years): 90

Top Venues

CoRR

NeurIPS

ICML

AISTATS

Publications

Matteo Pirotta, Andrea Tirinzoni, Ahmed Touati, Alessandro Lazaric, Yann Ollivier
Fast Imitation via Behavior Foundation Models. ICLR (2024)
Edoardo Cetin, Andrea Tirinzoni, Matteo Pirotta, Alessandro Lazaric, Yann Ollivier, Ahmed Touati
Simple Ingredients for Offline Reinforcement Learning. CoRR (2024)
Virginie Do, Elvis Dohmatob, Matteo Pirotta, Alessandro Lazaric, Nicolas Usunier
Contextual bandits with concave rewards, and an application to fair ranking. ICLR (2023)
Harsh Satija, Alessandro Lazaric, Matteo Pirotta, Joelle Pineau
Group Fairness in Reinforcement Learning. Trans. Mach. Learn. Res. 2023 (2023)
Liyu Chen, Andrea Tirinzoni, Matteo Pirotta, Alessandro Lazaric
Reaching Goals is Hard: Settling the Sample Complexity of the Stochastic Shortest Path. ALT (2023)
Liyu Chen, Andrea Tirinzoni, Alessandro Lazaric, Matteo Pirotta
Layered State Discovery for Incremental Autonomous Exploration. ICML (2023)
Andrea Tirinzoni, Matteo Pirotta, Alessandro Lazaric
On the Complexity of Representation Learning in Contextual Linear Bandits. AISTATS (2023)
Liyu Chen, Andrea Tirinzoni, Alessandro Lazaric, Matteo Pirotta
Layered State Discovery for Incremental Autonomous Exploration. CoRR (2023)
Matteo Papini, Matteo Pirotta, Marcello Restelli
Smoothing policies and safe policy gradients. Mach. Learn. 111 (11) (2022)
Virginie Do, Elvis Dohmatob, Matteo Pirotta, Alessandro Lazaric, Nicolas Usunier
Contextual bandits with concave rewards, and an application to fair ranking. CoRR (2022)
Jean Tarbouriech, Omar Darwiche Domingues, Pierre Ménard, Matteo Pirotta, Michal Valko, Alessandro Lazaric
Adaptive Multi-Goal Exploration. AISTATS (2022)
Andrea Tirinzoni, Matteo Papini, Ahmed Touati, Alessandro Lazaric, Matteo Pirotta
Scalable Representation Learning in Linear Contextual Bandits with Constant Regret Guarantees. NeurIPS (2022)
Yunchang Yang, Tianhao Wu, Han Zhong, Evrard Garcelon, Matteo Pirotta, Alessandro Lazaric, Liwei Wang, Simon Shaolei Du
A Reduction-Based Framework for Conservative Bandits and Reinforcement Learning. ICLR (2022)
Evrard Garcelon, Kamalika Chaudhuri, Vianney Perchet, Matteo Pirotta
Privacy Amplification via Shuffling for Linear Contextual Bandits. ALT (2022)
Andrea Tirinzoni, Matteo Papini, Ahmed Touati, Alessandro Lazaric, Matteo Pirotta
Scalable Representation Learning in Linear Contextual Bandits with Constant Regret Guarantees. CoRR (2022)
Liyu Chen, Andrea Tirinzoni, Matteo Pirotta, Alessandro Lazaric
Reaching Goals is Hard: Settling the Sample Complexity of the Stochastic Shortest Path. CoRR (2022)
Evrard Garcelon, Matteo Pirotta, Vianney Perchet
Encrypted Linear Contextual Bandit. AISTATS (2022)
Evrard Garcelon, Vashist Avadhanula, Alessandro Lazaric, Matteo Pirotta
Top K Ranking for Multi-Armed Bandit with Noisy Evaluations. AISTATS (2022)
Yifang Chen, Karthik Abinav Sankararaman, Alessandro Lazaric, Matteo Pirotta, Dmytro Karamshuk, Qifan Wang, Karishma Mandyam, Sinong Wang, Han Fang
Improved Adaptive Algorithm for Scalable Active Learning with Weak Labeler. CoRR (2022)
Andrea Tirinzoni, Matteo Pirotta, Alessandro Lazaric
On the Complexity of Representation Learning in Contextual Linear Bandits. CoRR (2022)
Jean Tarbouriech, Matteo Pirotta, Michal Valko, Alessandro Lazaric
Sample Complexity Bounds for Stochastic Shortest Path with a Generative Model. ALT (2021)
Carlo D'Eramo, Andrea Cini, Alessandro Nuara, Matteo Pirotta, Cesare Alippi, Jan Peters, Marcello Restelli
Gaussian Approximation for Bias Reduction in Q-Learning. J. Mach. Learn. Res. 22 (2021)
Jean Tarbouriech, Matteo Pirotta, Michal Valko, Alessandro Lazaric
A Provably Efficient Sample Collection Strategy for Reinforcement Learning. NeurIPS (2021)
Evrard Garcelon, Kamalika Chaudhuri, Vianney Perchet, Matteo Pirotta
Privacy Amplification via Shuffling for Linear Contextual Bandits. CoRR (2021)
Omar Darwiche Domingues, Pierre Ménard, Matteo Pirotta, Emilie Kaufmann, Michal Valko
A Kernel-Based Approach to Non-Stationary Reinforcement Learning in Metric Spaces. AISTATS (2021)
Matteo Papini, Andrea Tirinzoni, Aldo Pacchiano, Marcello Restelli, Alessandro Lazaric, Matteo Pirotta
Reinforcement Learning in Linear MDPs: Constant Regret and Representation Selection. NeurIPS (2021)
Yunchang Yang, Tianhao Wu, Han Zhong, Evrard Garcelon, Matteo Pirotta, Alessandro Lazaric, Liwei Wang, Simon S. Du
A Unified Framework for Conservative Exploration. CoRR (2021)
Matteo Papini, Andrea Tirinzoni, Marcello Restelli, Alessandro Lazaric, Matteo Pirotta
Leveraging Good Representations in Linear Contextual Bandits. CoRR (2021)
Jean Tarbouriech, Omar Darwiche Domingues, Pierre Ménard, Matteo Pirotta, Michal Valko, Alessandro Lazaric
Adaptive Multi-Goal Exploration. CoRR (2021)
Jean Tarbouriech, Runlong Zhou, Simon S. Du, Matteo Pirotta, Michal Valko, Alessandro Lazaric
Stochastic Shortest Path: Minimax, Parameter-Free and Towards Horizon-Free Regret. CoRR (2021)
Evrard Garcelon, Vashist Avadhanula, Alessandro Lazaric, Matteo Pirotta
Top K Ranking for Multi-Armed Bandit with Noisy Evaluations. CoRR (2021)
Evrard Garcelon, Vianney Perchet, Ciara Pike-Burke, Matteo Pirotta
Local Differential Privacy for Regret Minimization in Reinforcement Learning. NeurIPS (2021)
Jean Tarbouriech, Runlong Zhou, Simon S. Du, Matteo Pirotta, Michal Valko, Alessandro Lazaric
Stochastic Shortest Path: Minimax, Parameter-Free and Towards Horizon-Free Regret. NeurIPS (2021)
Andrea Tirinzoni, Matteo Pirotta, Alessandro Lazaric
A Fully Problem-Dependent Regret Lower Bound for Finite-Horizon MDPs. CoRR (2021)
Alberto Maria Metelli, Matteo Pirotta, Daniele Calandriello, Marcello Restelli
Safe Policy Iteration: A Monotonically Improving Approximate Policy Iteration Approach. J. Mach. Learn. Res. 22 (2021)
Matteo Papini, Andrea Tirinzoni, Aldo Pacchiano, Marcello Restelli, Alessandro Lazaric, Matteo Pirotta
Reinforcement Learning in Linear MDPs: Constant Regret and Representation Selection. CoRR (2021)
Omar Darwiche Domingues, Pierre Ménard, Matteo Pirotta, Emilie Kaufmann, Michal Valko
Kernel-Based Reinforcement Learning: A Finite-Time Analysis. ICML (2021)
Paul Luyo, Evrard Garcelon, Alessandro Lazaric, Matteo Pirotta
Differentially Private Exploration in Reinforcement Learning with Linear Representation. CoRR (2021)
Matteo Papini, Andrea Tirinzoni, Marcello Restelli, Alessandro Lazaric, Matteo Pirotta
Leveraging Good Representations in Linear Contextual Bandits. ICML (2021)
Evrard Garcelon, Vianney Perchet, Matteo Pirotta
Homomorphically Encrypted Linear Contextual Bandit. CoRR (2021)
Evrard Garcelon, Vianney Perchet, Ciara Pike-Burke, Matteo Pirotta
Local Differentially Private Regret Minimization in Reinforcement Learning. CoRR (2020)
Alberto Maria Metelli, Matteo Pirotta, Marcello Restelli
On the use of the policy gradient and Hessian in inverse reinforcement learning. Intelligenza Artificiale 14 (1) (2020)
Evrard Garcelon, Mohammad Ghavamzadeh, Alessandro Lazaric, Matteo Pirotta
Improved Algorithms for Conservative Exploration in Bandits. AAAI (2020)
Jian Qian, Ronan Fruit, Matteo Pirotta, Alessandro Lazaric
Concentration Inequalities for Multinoulli Random Variables. CoRR (2020)
Evrard Garcelon, Mohammad Ghavamzadeh, Alessandro Lazaric, Matteo Pirotta
Conservative Exploration in Reinforcement Learning. CoRR (2020)
Jean Tarbouriech, Shubhanshu Shekhar, Matteo Pirotta, Mohammad Ghavamzadeh, Alessandro Lazaric
Active Model Estimation in Markov Decision Processes. CoRR (2020)
Andrea Tirinzoni, Matteo Pirotta, Marcello Restelli, Alessandro Lazaric
An Asymptotically Optimal Primal-Dual Incremental Algorithm for Contextual Linear Bandits. NeurIPS (2020)
Pierre-Alexandre Kamienny, Matteo Pirotta, Alessandro Lazaric, Thibault Lavril, Nicolas Usunier, Ludovic Denoyer
Learning Adaptive Exploration Strategies in Dynamic Environments Through Informed Policy Regularization. CoRR (2020)
Omar Darwiche Domingues, Pierre Ménard, Matteo Pirotta, Emilie Kaufmann, Michal Valko
A Kernel-Based Approach to Non-Stationary Reinforcement Learning in Metric Spaces. CoRR (2020)
Ronan Fruit, Matteo Pirotta, Alessandro Lazaric
Improved Analysis of UCRL2 with Empirical Bernstein Inequality. CoRR (2020)
Michiel van der Meer, Matteo Pirotta, Elia Bruni
Exploiting Language Instructions for Interpretable and Compositional Reinforcement Learning. CoRR (2020)
Jean Tarbouriech, Matteo Pirotta, Michal Valko, Alessandro Lazaric
Improved Sample Complexity for Incremental Autonomous Exploration in MDPs. NeurIPS (2020)
Evrard Garcelon, Mohammad Ghavamzadeh, Alessandro Lazaric, Matteo Pirotta
Improved Algorithms for Conservative Exploration in Bandits. CoRR (2020)
Andrea Zanette, David Brandfonbrener, Emma Brunskill, Matteo Pirotta, Alessandro Lazaric
Frequentist Regret Bounds for Randomized Least-Squares Value Iteration. AISTATS (2020)
Jean Tarbouriech, Evrard Garcelon, Michal Valko, Matteo Pirotta, Alessandro Lazaric
No-Regret Exploration in Goal-Oriented Reinforcement Learning. ICML (2020)
Evrard Garcelon, Mohammad Ghavamzadeh, Alessandro Lazaric, Matteo Pirotta
Conservative Exploration in Reinforcement Learning. AISTATS (2020)
Andrea Tirinzoni, Matteo Pirotta, Marcello Restelli, Alessandro Lazaric
An Asymptotically Optimal Primal-Dual Incremental Algorithm for Contextual Linear Bandits. CoRR (2020)
Evrard Garcelon, Baptiste Rozière, Laurent Meunier, Jean Tarbouriech, Olivier Teytaud, Alessandro Lazaric, Matteo Pirotta
Adversarial Attacks on Linear Contextual Bandits. NeurIPS (2020)
Jean Tarbouriech, Matteo Pirotta, Michal Valko, Alessandro Lazaric
Improved Sample Complexity for Incremental Autonomous Exploration in MDPs. CoRR (2020)
Jean Tarbouriech, Shubhanshu Shekhar, Matteo Pirotta, Mohammad Ghavamzadeh, Alessandro Lazaric
Active Model Estimation in Markov Decision Processes. UAI (2020)
Yonathan Efroni, Shie Mannor, Matteo Pirotta
Exploration-Exploitation in Constrained MDPs. CoRR (2020)
Jean Tarbouriech, Matteo Pirotta, Michal Valko, Alessandro Lazaric
A Provably Efficient Sample Collection Strategy for Reinforcement Learning. CoRR (2020)
Evrard Garcelon, Baptiste Rozière, Laurent Meunier, Jean Tarbouriech, Olivier Teytaud, Alessandro Lazaric, Matteo Pirotta
Adversarial Attacks on Linear Contextual Bandits. CoRR (2020)
Omar Darwiche Domingues, Pierre Ménard, Matteo Pirotta, Emilie Kaufmann, Michal Valko
Regret Bounds for Kernel-Based Reinforcement Learning. CoRR (2020)
Jean Tarbouriech, Evrard Garcelon, Michal Valko, Matteo Pirotta, Alessandro Lazaric
No-Regret Exploration in Goal-Oriented Reinforcement Learning. CoRR (2019)
Jian Qian, Ronan Fruit, Matteo Pirotta, Alessandro Lazaric
Exploration Bonus for Regret Minimization in Discrete and Continuous Average Reward MDPs. NeurIPS (2019)
Ronald Ortner, Matteo Pirotta, Alessandro Lazaric, Ronan Fruit, Odalric-Ambrym Maillard
Regret Bounds for Learning State Representations in Reinforcement Learning. NeurIPS (2019)
Matteo Papini, Matteo Pirotta, Marcello Restelli
Smoothing Policies and Safe Policy Gradients. CoRR (2019)
Andrea Zanette, David Brandfonbrener, Matteo Pirotta, Alessandro Lazaric
Frequentist Regret Bounds for Randomized Least-Squares Value Iteration. CoRR (2019)
Matteo Papini, Damiano Binaghi, Giuseppe Canonaco, Matteo Pirotta, Marcello Restelli
Stochastic Variance-Reduced Policy Gradient. CoRR (2018)
Davide Di Febbo, Emilia Ambrosini, Matteo Pirotta, Eric Rojas, Marcello Restelli, Alessandra L. G. Pedrocchi, Simona Ferrante
Does Reinforcement Learning outperform PID in the control of FES-induced elbow flex-extension? MeMeA (2018)
Andrea Tirinzoni, Andrea Sessa, Matteo Pirotta, Marcello Restelli
Importance Weighted Transfer of Samples in Reinforcement Learning. ICML (2018)
Matteo Papini, Damiano Binaghi, Giuseppe Canonaco, Matteo Pirotta, Marcello Restelli
Stochastic Variance-Reduced Policy Gradient. ICML (2018)
Ronan Fruit, Matteo Pirotta, Alessandro Lazaric
Near Optimal Exploration-Exploitation in Non-Communicating Markov Decision Processes. NeurIPS (2018)
Ronan Fruit, Matteo Pirotta, Alessandro Lazaric, Ronald Ortner
Efficient Bias-Span-Constrained Exploration-Exploitation in Reinforcement Learning. CoRR (2018)
Ronan Fruit, Matteo Pirotta, Alessandro Lazaric
Near Optimal Exploration-Exploitation in Non-Communicating Markov Decision Processes. CoRR (2018)
Andrea Tirinzoni, Andrea Sessa, Matteo Pirotta, Marcello Restelli
Importance Weighted Transfer of Samples in Reinforcement Learning. CoRR (2018)
Jian Qian, Ronan Fruit, Matteo Pirotta, Alessandro Lazaric
Exploration Bonus for Regret Minimization in Undiscounted Discrete and Continuous Markov Decision Processes. CoRR (2018)
Ronan Fruit, Matteo Pirotta, Alessandro Lazaric, Ronald Ortner
Efficient Bias-Span-Constrained Exploration-Exploitation in Reinforcement Learning. ICML (2018)
Ronan Fruit, Matteo Pirotta, Alessandro Lazaric, Emma Brunskill
Regret Minimization in MDPs with Options without Prior Knowledge. NIPS (2017)
Carlo D'Eramo, Alessandro Nuara, Matteo Pirotta, Marcello Restelli
Estimating the Maximum Expected Value in Continuous Reinforcement Learning Problems. AAAI (2017)
Matteo Papini, Matteo Pirotta, Marcello Restelli
Adaptive Batch Size for Safe Policy Gradients. NIPS (2017)
Simone Parisi, Matteo Pirotta, Jan Peters
Manifold-based multi-objective policy search with sample reuse. Neurocomputing 263 (2017)
Davide Tateo, Matteo Pirotta, Marcello Restelli, Andrea Bonarini
Gradient-based minimization for multi-expert Inverse Reinforcement Learning. SSCI (2017)
Alberto Maria Metelli, Matteo Pirotta, Marcello Restelli
Compatible Reward Inverse Reinforcement Learning. NIPS (2017)
Samuele Tosatto, Matteo Pirotta, Carlo D'Eramo, Marcello Restelli
Boosted Fitted Q-Iteration. ICML (2017)
Matteo Pirotta, Marcello Restelli
Cost-Sensitive Approach to Batch Size Adaptation for Gradient Descent. CoRR (2017)
Matteo Pirotta, Marcello Restelli
Inverse Reinforcement Learning through Policy Gradient Minimization. AAAI (2016)
Simone Parisi, Matteo Pirotta, Marcello Restelli
Multi-objective Reinforcement Learning through Continuous Pareto Manifold Approximation. J. Artif. Intell. Res. 57 (2016)
Giorgio Manganini, Matteo Pirotta, Marcello Restelli, Luigi Piroddi, Maria Prandini
Policy Search for the Optimal Control of Markov Decision Processes: A Novel Particle-Based Iterative Scheme. IEEE Trans. Cybern. 46 (11) (2016)
Giorgio Manganini, Matteo Pirotta, Marcello Restelli, Luca Bascetta
Following Newton direction in Policy Gradient with parameter exploration. IJCNN (2015)
Matteo Pirotta, Marcello Restelli, Luca Bascetta
Policy gradient in Lipschitz Markov Decision Processes. Mach. Learn. 100 (2-3) (2015)
Matteo Pirotta, Simone Parisi, Marcello Restelli
Multi-Objective Reinforcement Learning with Continuous Pareto Frontier Approximation. AAAI (2015)
Danilo Caporale, Luca Deori, Roberto Mura, Alessandro Falsone, Riccardo Vignali, Luca Giulioni, Matteo Pirotta, Giorgio Manganini
Optimal control to reduce emissions in gasoline engines: an iterative learning control approach for ECU calibration maps improvement. ECC (2015)
Simone Parisi, Matteo Pirotta, Nicola Smacchia, Luca Bascetta, Marcello Restelli
Policy gradient approaches for multi-objective sequential decision making: A comparison. ADPRL (2014)
Matteo Pirotta, Simone Parisi, Marcello Restelli
Multi-objective Reinforcement Learning with Continuous Pareto Frontier Approximation. CoRR (2014)
Simone Parisi, Matteo Pirotta, Nicola Smacchia, Luca Bascetta, Marcello Restelli
Policy gradient approaches for multi-objective sequential decision making. IJCNN (2014)
Matteo Pirotta, Marcello Restelli, Luca Bascetta
Adaptive Step-Size for Policy Gradient Methods. NIPS (2013)
Matteo Pirotta, Marcello Restelli, Alessio Pecorino, Daniele Calandriello
Safe Policy Iteration. ICML (3) (2013)
Martino Migliavacca, Alessio Pecorino, Matteo Pirotta, Marcello Restelli, Andrea Bonarini
Fitted policy search. ADPRL (2011)

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Publication Activity (10 Years)

Top Topics

Top Venues

Matteo Pirotta