Matteo Pirotta

According to our database, Matteo Pirotta authored at least 74 papers between 2011 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Simple Ingredients for Offline Reinforcement Learning.

Edoardo Cetin, Andrea Tirinzoni, Matteo Pirotta, Alessandro Lazaric, Yann Ollivier, Ahmed Touati

CoRR, 2024

2023

Group Fairness in Reinforcement Learning.

Harsh Satija, Alessandro Lazaric, Matteo Pirotta, Joelle Pineau

Trans. Mach. Learn. Res., 2023

Layered State Discovery for Incremental Autonomous Exploration.

Liyu Chen, Andrea Tirinzoni, Alessandro Lazaric, Matteo Pirotta

Proceedings of the International Conference on Machine Learning, 2023

Contextual bandits with concave rewards, and an application to fair ranking.

Virginie Do, Elvis Dohmatob, Matteo Pirotta, Alessandro Lazaric, Nicolas Usunier

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Reaching Goals is Hard: Settling the Sample Complexity of the Stochastic Shortest Path.

Liyu Chen, Andrea Tirinzoni, Matteo Pirotta, Alessandro Lazaric

Proceedings of the International Conference on Algorithmic Learning Theory, 2023

On the Complexity of Representation Learning in Contextual Linear Bandits.

Andrea Tirinzoni, Matteo Pirotta, Alessandro Lazaric

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022

Smoothing policies and safe policy gradients.

Matteo Papini, Matteo Pirotta, Marcello Restelli

Mach. Learn., 2022

Improved Adaptive Algorithm for Scalable Active Learning with Weak Labeler.

Yifang Chen, Karthik Abinav Sankararaman, Alessandro Lazaric, Matteo Pirotta, Dmytro Karamshuk, Qifan Wang, Karishma Mandyam, Sinong Wang, Han Fang

CoRR, 2022

Scalable Representation Learning in Linear Contextual Bandits with Constant Regret Guarantees.

Andrea Tirinzoni, Matteo Papini, Ahmed Touati, Alessandro Lazaric, Matteo Pirotta

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 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

Proceedings of the Tenth International Conference on Learning Representations, 2022

Privacy Amplification via Shuffling for Linear Contextual Bandits.

Evrard Garcelon, Kamalika Chaudhuri, Vianney Perchet, Matteo Pirotta

Proceedings of the International Conference on Algorithmic Learning Theory, 29 March, 2022

Adaptive Multi-Goal Exploration.

Jean Tarbouriech, Omar Darwiche Domingues, Pierre Ménard, Matteo Pirotta, Michal Valko, Alessandro Lazaric

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

Encrypted Linear Contextual Bandit.

Evrard Garcelon, Matteo Pirotta, Vianney Perchet

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

Top K Ranking for Multi-Armed Bandit with Noisy Evaluations.

Evrard Garcelon, Vashist Avadhanula, Alessandro Lazaric, Matteo Pirotta

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

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

Gaussian Approximation for Bias Reduction in Q-Learning.

Carlo D'Eramo, Andrea Cini, Alessandro Nuara, Matteo Pirotta, Cesare Alippi, Jan Peters, Marcello Restelli

J. Mach. Learn. Res., 2021

Differentially Private Exploration in Reinforcement Learning with Linear Representation.

Paul Luyo, Evrard Garcelon, Alessandro Lazaric, Matteo Pirotta

CoRR, 2021

A Fully Problem-Dependent Regret Lower Bound for Finite-Horizon MDPs.

Andrea Tirinzoni, Matteo Pirotta, Alessandro Lazaric

CoRR, 2021

A Unified Framework for Conservative Exploration.

Yunchang Yang, Tianhao Wu, Han Zhong, Evrard Garcelon, Matteo Pirotta, Alessandro Lazaric, Liwei Wang, Simon S. Du

CoRR, 2021

Homomorphically Encrypted Linear Contextual Bandit.

Evrard Garcelon, Vianney Perchet, Matteo Pirotta

CoRR, 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

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

A Provably Efficient Sample Collection Strategy for Reinforcement Learning.

Jean Tarbouriech, Matteo Pirotta, Michal Valko, Alessandro Lazaric

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Reinforcement Learning in Linear MDPs: Constant Regret and Representation Selection.

Matteo Papini, Andrea Tirinzoni, Aldo Pacchiano, Marcello Restelli, Alessandro Lazaric, Matteo Pirotta

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Local Differential Privacy for Regret Minimization in Reinforcement Learning.

Evrard Garcelon, Vianney Perchet, Ciara Pike-Burke, Matteo Pirotta

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Leveraging Good Representations in Linear Contextual Bandits.

Matteo Papini, Andrea Tirinzoni, Marcello Restelli, Alessandro Lazaric, Matteo Pirotta

Proceedings of the 38th International Conference on Machine Learning, 2021

Kernel-Based Reinforcement Learning: A Finite-Time Analysis.

Omar Darwiche Domingues, Pierre Ménard, Matteo Pirotta, Emilie Kaufmann, Michal Valko

Proceedings of the 38th International Conference on Machine Learning, 2021

Sample Complexity Bounds for Stochastic Shortest Path with a Generative Model.

Jean Tarbouriech, Matteo Pirotta, Michal Valko, Alessandro Lazaric

Proceedings of the Algorithmic Learning Theory, 2021

A Kernel-Based Approach to Non-Stationary Reinforcement Learning in Metric Spaces.

Omar Darwiche Domingues, Pierre Ménard, Matteo Pirotta, Emilie Kaufmann, Michal Valko

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020

On the use of the policy gradient and Hessian in inverse reinforcement learning.

Alberto Maria Metelli, Matteo Pirotta, Marcello Restelli

Intelligenza Artificiale, 2020

Local Differentially Private Regret Minimization in Reinforcement Learning.

Evrard Garcelon, Vianney Perchet, Ciara Pike-Burke, Matteo Pirotta

CoRR, 2020

Improved Analysis of UCRL2 with Empirical Bernstein Inequality.

Ronan Fruit, Matteo Pirotta, Alessandro Lazaric

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

Regret Bounds for Kernel-Based Reinforcement Learning.

Omar Darwiche Domingues, Pierre Ménard, Matteo Pirotta, Emilie Kaufmann, Michal Valko

CoRR, 2020

Exploration-Exploitation in Constrained MDPs.

Yonathan Efroni, Shie Mannor, Matteo Pirotta

CoRR, 2020

Concentration Inequalities for Multinoulli Random Variables.

Jian Qian, Ronan Fruit, Matteo Pirotta, Alessandro Lazaric

CoRR, 2020

Exploiting Language Instructions for Interpretable and Compositional Reinforcement Learning.

Michiel van der Meer, Matteo Pirotta, Elia Bruni

CoRR, 2020

Active Model Estimation in Markov Decision Processes.

Jean Tarbouriech, Shubhanshu Shekhar, Matteo Pirotta, Mohammad Ghavamzadeh, Alessandro Lazaric

Proceedings of the Thirty-Sixth Conference on Uncertainty in Artificial Intelligence, 2020

An Asymptotically Optimal Primal-Dual Incremental Algorithm for Contextual Linear Bandits.

Andrea Tirinzoni, Matteo Pirotta, Marcello Restelli, Alessandro Lazaric

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Improved Sample Complexity for Incremental Autonomous Exploration in MDPs.

Jean Tarbouriech, Matteo Pirotta, Michal Valko, Alessandro Lazaric

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Adversarial Attacks on Linear Contextual Bandits.

Evrard Garcelon, Baptiste Rozière, Laurent Meunier, Jean Tarbouriech, Olivier Teytaud, Alessandro Lazaric, Matteo Pirotta

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

No-Regret Exploration in Goal-Oriented Reinforcement Learning.

Jean Tarbouriech, Evrard Garcelon, Michal Valko, Matteo Pirotta, Alessandro Lazaric

Proceedings of the 37th International Conference on Machine Learning, 2020

Frequentist Regret Bounds for Randomized Least-Squares Value Iteration.

Andrea Zanette, David Brandfonbrener, Emma Brunskill, Matteo Pirotta, Alessandro Lazaric

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

Conservative Exploration in Reinforcement Learning.

Evrard Garcelon, Mohammad Ghavamzadeh, Alessandro Lazaric, Matteo Pirotta

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

Improved Algorithms for Conservative Exploration in Bandits.

Evrard Garcelon, Mohammad Ghavamzadeh, Alessandro Lazaric, Matteo Pirotta

Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

2019

Frequentist Regret Bounds for Randomized Least-Squares Value Iteration.

Andrea Zanette, David Brandfonbrener, Matteo Pirotta, Alessandro Lazaric

CoRR, 2019

Exploration Bonus for Regret Minimization in Discrete and Continuous Average Reward MDPs.

Jian Qian, Ronan Fruit, Matteo Pirotta, Alessandro Lazaric

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Regret Bounds for Learning State Representations in Reinforcement Learning.

Ronald Ortner, Matteo Pirotta, Alessandro Lazaric, Ronan Fruit, Odalric-Ambrym Maillard

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

2018

Exploration Bonus for Regret Minimization in Undiscounted Discrete and Continuous Markov Decision Processes.

Jian Qian, Ronan Fruit, Matteo Pirotta, Alessandro Lazaric

CoRR, 2018

Near Optimal Exploration-Exploitation in Non-Communicating Markov Decision Processes.

Ronan Fruit, Matteo Pirotta, Alessandro Lazaric

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Does Reinforcement Learning outperform PID in the control of FES-induced elbow flex-extension?

Davide Di Febbo, Emilia Ambrosini, Matteo Pirotta, Eric Rojas, Marcello Restelli, Alessandra Laura Giulia Pedrocchi, Simona Ferrante

Proceedings of the 2018 IEEE International Symposium on Medical Measurements and Applications, 2018

Importance Weighted Transfer of Samples in Reinforcement Learning.

Andrea Tirinzoni, Andrea Sessa, Matteo Pirotta, Marcello Restelli

Proceedings of the 35th International Conference on Machine Learning, 2018

Stochastic Variance-Reduced Policy Gradient.

Matteo Papini, Damiano Binaghi, Giuseppe Canonaco, Matteo Pirotta, Marcello Restelli

Proceedings of the 35th International Conference on Machine Learning, 2018

Efficient Bias-Span-Constrained Exploration-Exploitation in Reinforcement Learning.

Ronan Fruit, Matteo Pirotta, Alessandro Lazaric, Ronald Ortner

Proceedings of the 35th International Conference on Machine Learning, 2018

2017

Manifold-based multi-objective policy search with sample reuse.

Simone Parisi, Matteo Pirotta, Jan Peters

Neurocomputing, 2017

Cost-Sensitive Approach to Batch Size Adaptation for Gradient Descent.

Matteo Pirotta, Marcello Restelli

CoRR, 2017

Gradient-based minimization for multi-expert Inverse Reinforcement Learning.

Davide Tateo, Matteo Pirotta, Marcello Restelli, Andrea Bonarini

Proceedings of the 2017 IEEE Symposium Series on Computational Intelligence, 2017

Adaptive Batch Size for Safe Policy Gradients.

Matteo Papini, Matteo Pirotta, Marcello Restelli

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Compatible Reward Inverse Reinforcement Learning.

Alberto Maria Metelli, Matteo Pirotta, Marcello Restelli

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Regret Minimization in MDPs with Options without Prior Knowledge.

Ronan Fruit, Matteo Pirotta, Alessandro Lazaric, Emma Brunskill

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Boosted Fitted Q-Iteration.

Samuele Tosatto, Matteo Pirotta, Carlo D'Eramo, Marcello Restelli

Proceedings of the 34th International Conference on Machine Learning, 2017

Estimating the Maximum Expected Value in Continuous Reinforcement Learning Problems.

Carlo D'Eramo, Alessandro Nuara, Matteo Pirotta, Marcello Restelli

Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, 2017

2016

Reinforcement learning: from theory to algorithms.

Matteo Pirotta

PhD thesis, 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

Multi-objective Reinforcement Learning through Continuous Pareto Manifold Approximation.

Simone Parisi, Matteo Pirotta, Marcello Restelli

J. Artif. Intell. Res., 2016

Inverse Reinforcement Learning through Policy Gradient Minimization.

Matteo Pirotta, Marcello Restelli

Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, 2016

2015

Policy gradient in Lipschitz Markov Decision Processes.

Matteo Pirotta, Marcello Restelli, Luca Bascetta

Mach. Learn., 2015

Following Newton direction in Policy Gradient with parameter exploration.

Giorgio Manganini, Matteo Pirotta, Marcello Restelli, Luca Bascetta

Proceedings of the 2015 International Joint Conference on Neural Networks, 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

Proceedings of the 14th European Control Conference, 2015

Multi-Objective Reinforcement Learning with Continuous Pareto Frontier Approximation.

Matteo Pirotta, Simone Parisi, Marcello Restelli

Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, 2015

2014

Policy gradient approaches for multi-objective sequential decision making.

Simone Parisi, Matteo Pirotta, Nicola Smacchia, Luca Bascetta, Marcello Restelli

Proceedings of the 2014 International Joint Conference on Neural Networks, 2014

Policy gradient approaches for multi-objective sequential decision making: A comparison.

Simone Parisi, Matteo Pirotta, Nicola Smacchia, Luca Bascetta, Marcello Restelli

Proceedings of the 2014 IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning, 2014

2013

Adaptive Step-Size for Policy Gradient Methods.

Matteo Pirotta, Marcello Restelli, Luca Bascetta

Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013

Safe Policy Iteration.

Matteo Pirotta, Marcello Restelli, Alessio Pecorino, Daniele Calandriello

Proceedings of the 30th International Conference on Machine Learning, 2013

2011

Fitted policy search.

Martino Migliavacca, Alessio Pecorino, Matteo Pirotta, Marcello Restelli, Andrea Bonarini

Proceedings of the 2011 IEEE Symposium on Adaptive Dynamic Programming And Reinforcement Learning, 2011