Matteo Pirotta

Research Scientist at Meta

Google Scholar
Preprints
  • Andrea Tirinzoni, Matteo Pirotta, Alessandro Lazaric:
    A Fully Problem-Dependent Regret Lower Bound for Finite-Horizon MDPs. arXiv:2106.13013, 2021. [arXiv]
  • Yonathan Efroni, Shie Mannor and Matteo Pirotta:
    Exploration-Exploitation in Constrained MDPs. arXiv:2003.02189, 2020. [arXiv]
Conference Papers
  • Yunchang Yang, Tianhao Wu, Han Zhong, Evrard Garcelon, Matteo Pirotta, Alessandro Lazaric, Liwei Wang, Simon S. Du:
    A Reduction-Based Framework for Conservative Bandits and Reinforcement Learning. ICLR 2022, Virtual. [paper], [arXiv]
  • Jean Tarbouriech, Omar Darwiche Domingues, Pierre Menard, Matteo Pirotta, Michal Valko, Alessandro Lazaric:
    Adaptive Multi-Goal Exploration. AISTATS 2021, Virtual. [paper], [arXiv]
  • Evrard Garcelon, Vashist Avadhanula, Alessandro Lazaric, Matteo Pirotta:
    Top K Ranking for Multi-Armed Bandit with Noisy Evaluations. AISTATS 2021, Virtual. [paper], [arXiv]
  • Evrard Garcelon, Vianney Perchet, Matteo Pirotta:
    Homomorphically Encrypted Linear Contextual Bandit. AISTATS 2021, Virtual. [paper], [arXiv]
  • Evrard Garcelon, Kamalika Chaudhuri, Vianney Perchet, Matteo Pirotta:
    Privacy Amplification via Shuffling for Linear Contextual Bandits. ALT 2022, Virtual. [paper], [arXiv]
  • Matteo Papini, Andrea Tirinzoni, Aldo Pacchiano, Marcello Restelli, Alessandro Lazaric, Matteo Pirotta:
    Reinforcement Learning in Linear MDPs: Constant Regret and Representation Selection. NeurIPS 2021, Virtual. [paper], [arXiv]
  • 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, Virtual. [paper], [arXiv]
  • Jean Tarbouriech, Matteo Pirotta, Michal Valko and Alessandro Lazaric:
    A Provably Efficient Sample Collection Strategy for Reinforcement Learning. NeurIPS 2021, Virtual. [paper], [arXiv]
  • Evrard Garcelon, Vianney Perchet, Ciara Pike-Burke and Matteo Pirotta:
    Local Differentially Private Regret Minimization in Reinforcement Learning. NeurIPS 2021, Virtual. [paper], [arXiv]
  • Matteo Papini, Andrea Tirinzoni, Marcello Restelli, Alessandro Lazaric, Matteo Pirotta:
    Leveraging Good Representations in Linear Contextual Bandits. ICML 2021, Virtual. [paper], [arXiv]
  • Omar Darwiche Domingues, Pierre Menar, Matteo Pirotta, Emilie Kaufmann and Michal Valko:
    Kernel-Based Reinforcement Learning: A Finite-Time Analysis. ICML 2021, Virtual. [arXiv]
  • Jean Tarbouriech, Matteo Pirotta, Michal Valko and Alessandro Lazaric:
    Sample Complexity Bounds for Stochastic Shortest Path with a Generative Model. ALT 2021, Virtual. [paper]
  • Omar Darwiche Domingues, Pierre Menar, Matteo Pirotta, Emilie Kaufmann and Michal Valko:
    A Kernel-Based Approach to Non-Stationary Reinforcement Learning in Metric Spaces AISTATS 2021, Virtual. [arXiv]
  • Andrea Tirinzoni, Matteo Pirotta, Marcello Restelli and Alessandro Lazaric:
    An Asymptotically Optimal Primal-Dual Incremental Algorithm for Linear Contextual Bandits. NeurIPS 2020, Virtual. [arXiv]
  • Jean Tarbouriech, Matteo Pirotta, Michal Valko and Alessandro Lazaric:
    Improved Sample Complexity for Incremental Autonomous Exploration in MDPs. NeurIPS 2020, Virtual. [arXiv]
  • Evrard Garcelon, Baptiste Roziere, Laurent Meunier, Jean Tarbouriech, Olivier Teytaud, Alessandro Lazaric and Matteo Pirotta:
    Adversarial Attacks on Linear Contextual Bandits. NeurIPS 2020, Virtual. [arXiv]
  • Jean Tarbouriech, Shubhanshu Shekhar, Matteo Pirotta, Mohammad Ghavamzadeh, Alessandro Lazaric:
    Active Model Estimation in Markov Decision Processes UAI 2020, Virtual. [arXiv][paper]
  • Evrard Garcelon, Mohammad Ghavamzadeh, Alessandro Lazaric and Matteo Pirotta:
    Conservative Exploration in Reinforcement Learning. AISTATS 2020, Palermo, Italy. [arXiv]
  • Andrea Zanette, David Brandfonbrener, Emma Brunskill, Matteo Pirotta and Alessandro Lazaric:
    Frequentist Regret Bounds for Randomized Least-Squares Value Iteration. AISTATS 2020, Palermo, Italy. [arXiv]
  • Evrard Garcelon, Mohammad Ghavamzadeh, Alessandro Lazaric and Matteo Pirotta:
    Improved Algorithms for Conservative Exploration in Bandits. AAAI 2020, New York, USA. [arXiv]
  • Ronald Ortner, Matteo Pirotta, Alessandro Lazaric, Ronald Fruit and Odalrici-Ambrym Maillard:
    Regret Bounds for Learning State Representations in Reinforcement Learning. NeurIPS 2019, Vancouver, Canada.
  • Jian Qian, Ronan Fruit, Matteo Pirotta and Alessandro Lazaric:
    Exploration Bonus for Regret Minimization in Discrete and Continuous Average Reward MDPs. NeurIPS 2019, Vancouver, Canada. [arXiv] [Paper]
  • Ronan Fruit, Matteo Pirotta and Alessandro Lazaric:
    Near Optimal Exploration-Exploitation in Non-Communicating Markov Decision Processes. NeurIPS 2018, Montréal, Canada. [arXiv] [Paper]
  • Ronan Fruit, Matteo Pirotta, Alessandro Lazaric and Ronald Ortner:
    Efficient Bias-Span-Constrained Exploration-Exploitation in Reinforcement Learning. ICML 2018, Stockholm, Sweden. [arXiv]
  • Matteo Papini, Damiano Binaghi, Giuseppe Canonaco, Matteo Pirotta and Marcello Restelli:
    Stochastic Variance-Reduced Policy Gradient. ICML 2018, Stockholm, Sweden. [arXiv] [Paper]
  • Andrea Tirinzoni, Andrea Sessa, Matteo Pirotta and Marcello Restelli:
    Importance Weighted Transfer of Samples in Reinforcement Learning. ICML 2018, Stockholm, Sweden. [arXiv] [Paper]
  • Davide Di Febbo, Emilia Ambrosini, Matteo Pirotta, Eric Rojas, Marcello Restelli, Alessandra Pedrocchi and Simona Ferrante:
    Does Reinforcement Learning Outperform PID in the Control of FES Induced Elbow Flex-Extension? MeMeA 2018, Rome, Italy.
  • Ronan Fruit, Matteo Pirotta, Alessandro Lazaric, and Emma Brunskill:
    Regret Minimization in MDPs with Options without Prior Knowledge. NIPS 2017, Long Beach, California, USA. [Poster] [Full Paper]
  • Alberto Metelli, Matteo Pirotta, and Marcello Restelli:
    Compatible Reward Inverse Reinforcement Learning. NIPS 2017, Long Beach, California, USA. [Poster] [Paper]
  • Matteo Papini, Matteo Pirotta, and Marcello Restelli:
    Adaptive Batch Size for Safe Policy Gradients. NIPS 2017, Long Beach, California, USA. [Poster] [Paper]
  • Davide Tateo, Matteo Pirotta, Andrea Bonarini and Marcello Restelli:
    Gradient-Based Minimization for Multi-Expert Inverse Reinforcement Learning. IEEE SSCI 2017, Hawaii, USA.
  • Samuele Tosatto, Matteo Pirotta, Carlo D'Eramo, and Marcello Restelli:
    Boosted Fitted Q-Iteration. ICML 2017, Sydney, New South Wales, Australia.
  • Carlo D'Eramo, Alessandro Nuara, Matteo Pirotta, and Marcello Restelli:
    Estimating the Maximum Expected Value in Continuous Reinforcement Learning Problems. AAAI 2017, San Francisco, California, USA.
  • Matteo Pirotta, and Marcello Restelli:
    Inverse Reinforcement Learning through Policy Gradient Minimization. AAAI 2016, Phoenix, Arizona, USA.
  • Matteo Pirotta, Simone Parisi, and Marcello Restelli:
    Multi-Objective Reinforcement Learning with Continuous Pareto Frontier Approximation. AAAI 2015, Austin, Texas, USA.
  • Caporale Danilo, Luca Deori, Roberto Mura, Alessandro Falsone, Riccardo Vignali, Luca Giulioni, Matteo Pirotta and Giorgio Manganini:
    Optimal Control to Reduce Emissions in Gasoline Engines: An Iterative Learning Control Approach for ECU Calibration Maps Improvement. ECC 2015, Linz, Austria.
  • Giorgio Manganini, Matteo Pirotta, Marcello Restelli, Luca Bascetta:
    Following Newton Direction in Policy Gradient with Parameter Exploration. IJCNN 2015, Killarney, Ireland.
  • Simone Parisi, Matteo Pirotta, Nicola Smacchia, Luca Bascetta, Marcello Restelli:
    Policy Gradient Approaches for Multi-Objective Sequential Decision Making: A Comparison. ADPRL 2014, Orlando, Florida, United States.
  • Simone Parisi, Matteo Pirotta, Nicola Smacchia, Luca Bascetta and Marcello Restelli:
    Policy Gradient Approaches for Multi-Objective Sequential Decision Making. IJCNN 2014, Beijing, China.
  • Matteo Pirotta, Giorgio Manganini, Luigi Piroddi, Maria Prandini and Marcello Restelli:
    A particle-based policy for the optimal control of Markov decision processes. IFAC 2014, Cape Town, South Africa.
  • Matteo Pirotta, Marcello Restelli, Luca Bascetta:
    Adaptive Step-Size for Policy Gradient Methods. NIPS 2013, Lake Tahoe, Nevada, USA.
  • Matteo Pirotta, Marcello Restelli, Alessio Pecorino, and Daniele Calandriello:
    Safe policy iteration. ICML 2013, Atlanta, Georgia, USA. [Paper]
  • Martino Migliavacca, Alessio Pecorino, Matteo Pirotta, Marcello Restelli, and Andrea Bonarini:
    Fitted Policy Search. ADPRL 2011, Paris, France.
  • Martino Migliavacca, Alessio Pecorino, Matteo Pirotta, Marcello Restelli, and Andrea Bonarini:
    Fitted Policy Search: Direct Policy Search using a Batch Reinforcement Learning Approach. ERLARS 2010, Lisboa, Portugal.
Journal Papers
  • Alberto Maria Metelli, Matteo Pirotta, Daniele Calandriello, Marcello Restelli:
    Safe Policy Iteration: A Monotonically Improving Approximate Policy Iteration Approach. JMLR 22(97), 2021. [Paper]
  • Simone Parisi, Matteo Pirotta and Jan Peters:
    Manifold-based Multi-objective Policy Search with Sample Reuse. Neurocomputing 263, 2017. [Paper]
  • Giorgio Manganini, Matteo Pirotta, Marcello Restelli, Luigi Piroddi, and Maria Prandini:
    Policy search for the optimal control of Markov decision processes: a novel particle-based iterative scheme. IEEE Transactions on Cybernetics 46, 2016. [Paper]
  • Simone Parisi, Matteo Pirotta and Marcello Restelli:
    Multi-objective Reinforcement Learning through Continuous Pareto Manifold Approximation. Journal of Artificial Intelligence Research 57, 2016. [Paper]
  • Matteo Pirotta, Marcello Restelli and Luca Bascetta:
    Policy Gradient in Lipschitz Markov Decision Processes. Machine Learning 100, 2015. [Paper]
Technical Reports
  • Pierre-Alexandre Kamienny, Matteo Pirotta, Alessandro Lazaric, Thibault Lavril, Nicolas Usunier, Ludovic Denoyer:
    Learning Adaptive Exploration Strategies in Dynamic Environments Through Informed Policy Regularization. arXiv:2005.02934, 2020. [arXiv]
  • Ronan Fruit, Matteo Pirotta and Alessandro Lazaric:
    Improved Analysis of UCRL2 with empirical Bernstein bounds. ALT Tutorial, 2019. [arXiv]
  • Jian Qian, Ronan Fruit, Matteo Pirotta and Alessandro Lazaric:
    Concentration Inequalities for Multinoulli Random Variables. ALT Tutorial, 2019. [arXiv]
  • Matteo Pirotta and Marcello Restelli:
    Cost-Sensitive Approach to Batch Size Adaptation for Gradient Descent. Optimizing the optimizers, NIPS 2016 Workshop, Barcelona, Spain. [arXiv]