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Matteo Pirotta
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2020 – today
- 2024
- [c50]Matteo Pirotta, Andrea Tirinzoni, Ahmed Touati, Alessandro Lazaric, Yann Ollivier:
Fast Imitation via Behavior Foundation Models. ICLR 2024 - [c49]Edoardo Cetin, Andrea Tirinzoni, Matteo Pirotta, Alessandro Lazaric, Yann Ollivier, Ahmed Touati:
Simple Ingredients for Offline Reinforcement Learning. ICML 2024 - [i42]Edoardo Cetin, Andrea Tirinzoni, Matteo Pirotta, Alessandro Lazaric, Yann Ollivier, Ahmed Touati:
Simple Ingredients for Offline Reinforcement Learning. CoRR abs/2403.13097 (2024) - 2023
- [j9]Harsh Satija, Alessandro Lazaric, Matteo Pirotta, Joelle Pineau:
Group Fairness in Reinforcement Learning. Trans. Mach. Learn. Res. 2023 (2023) - [c48]Andrea Tirinzoni, Matteo Pirotta, Alessandro Lazaric:
On the Complexity of Representation Learning in Contextual Linear Bandits. AISTATS 2023: 7871-7896 - [c47]Liyu Chen, Andrea Tirinzoni, Matteo Pirotta, Alessandro Lazaric:
Reaching Goals is Hard: Settling the Sample Complexity of the Stochastic Shortest Path. ALT 2023: 310-357 - [c46]Virginie Do, Elvis Dohmatob, Matteo Pirotta, Alessandro Lazaric, Nicolas Usunier:
Contextual bandits with concave rewards, and an application to fair ranking. ICLR 2023 - [c45]Liyu Chen, Andrea Tirinzoni, Alessandro Lazaric, Matteo Pirotta:
Layered State Discovery for Incremental Autonomous Exploration. ICML 2023: 4953-5001 - [i41]Liyu Chen, Andrea Tirinzoni, Alessandro Lazaric, Matteo Pirotta:
Layered State Discovery for Incremental Autonomous Exploration. CoRR abs/2302.03789 (2023) - 2022
- [j8]Matteo Papini
, Matteo Pirotta, Marcello Restelli:
Smoothing policies and safe policy gradients. Mach. Learn. 111(11): 4081-4137 (2022) - [c44]Evrard Garcelon, Matteo Pirotta, Vianney Perchet:
Encrypted Linear Contextual Bandit. AISTATS 2022: 2519-2551 - [c43]Evrard Garcelon, Vashist Avadhanula, Alessandro Lazaric, Matteo Pirotta:
Top K Ranking for Multi-Armed Bandit with Noisy Evaluations. AISTATS 2022: 6242-6269 - [c42]Jean Tarbouriech, Omar Darwiche Domingues, Pierre Ménard, Matteo Pirotta, Michal Valko, Alessandro Lazaric:
Adaptive Multi-Goal Exploration. AISTATS 2022: 7349-7383 - [c41]Evrard Garcelon, Kamalika Chaudhuri, Vianney Perchet, Matteo Pirotta:
Privacy Amplification via Shuffling for Linear Contextual Bandits. ALT 2022: 381-407 - [c40]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 - [c39]Andrea Tirinzoni, Matteo Papini, Ahmed Touati, Alessandro Lazaric, Matteo Pirotta:
Scalable Representation Learning in Linear Contextual Bandits with Constant Regret Guarantees. NeurIPS 2022 - [i40]Liyu Chen, Andrea Tirinzoni, Matteo Pirotta, Alessandro Lazaric:
Reaching Goals is Hard: Settling the Sample Complexity of the Stochastic Shortest Path. CoRR abs/2210.04946 (2022) - [i39]Virginie Do, Elvis Dohmatob, Matteo Pirotta, Alessandro Lazaric, Nicolas Usunier:
Contextual bandits with concave rewards, and an application to fair ranking. CoRR abs/2210.09957 (2022) - [i38]Andrea Tirinzoni, Matteo Papini, Ahmed Touati, Alessandro Lazaric, Matteo Pirotta:
Scalable Representation Learning in Linear Contextual Bandits with Constant Regret Guarantees. CoRR abs/2210.13083 (2022) - [i37]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 abs/2211.02233 (2022) - [i36]Andrea Tirinzoni, Matteo Pirotta, Alessandro Lazaric:
On the Complexity of Representation Learning in Contextual Linear Bandits. CoRR abs/2212.09429 (2022) - 2021
- [j7]Alberto Maria Metelli, Matteo Pirotta, Daniele Calandriello, Marcello Restelli:
Safe Policy Iteration: A Monotonically Improving Approximate Policy Iteration Approach. J. Mach. Learn. Res. 22: 97:1-97:83 (2021) - [j6]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: 277:1-277:51 (2021) - [c38]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: 3538-3546 - [c37]Jean Tarbouriech, Matteo Pirotta, Michal Valko, Alessandro Lazaric:
Sample Complexity Bounds for Stochastic Shortest Path with a Generative Model. ALT 2021: 1157-1178 - [c36]Omar Darwiche Domingues, Pierre Ménard, Matteo Pirotta, Emilie Kaufmann, Michal Valko:
Kernel-Based Reinforcement Learning: A Finite-Time Analysis. ICML 2021: 2783-2792 - [c35]Matteo Papini, Andrea Tirinzoni, Marcello Restelli, Alessandro Lazaric, Matteo Pirotta:
Leveraging Good Representations in Linear Contextual Bandits. ICML 2021: 8371-8380 - [c34]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: 6843-6855 - [c33]Jean Tarbouriech, Matteo Pirotta, Michal Valko, Alessandro Lazaric:
A Provably Efficient Sample Collection Strategy for Reinforcement Learning. NeurIPS 2021: 7611-7624 - [c32]Evrard Garcelon, Vianney Perchet, Ciara Pike-Burke, Matteo Pirotta:
Local Differential Privacy for Regret Minimization in Reinforcement Learning. NeurIPS 2021: 10561-10573 - [c31]Matteo Papini, Andrea Tirinzoni, Aldo Pacchiano, Marcello Restelli, Alessandro Lazaric, Matteo Pirotta:
Reinforcement Learning in Linear MDPs: Constant Regret and Representation Selection. NeurIPS 2021: 16371-16383 - [i35]Evrard Garcelon, Vianney Perchet, Matteo Pirotta:
Homomorphically Encrypted Linear Contextual Bandit. CoRR abs/2103.09927 (2021) - [i34]Matteo Papini, Andrea Tirinzoni, Marcello Restelli, Alessandro Lazaric, Matteo Pirotta:
Leveraging Good Representations in Linear Contextual Bandits. CoRR abs/2104.03781 (2021) - [i33]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 abs/2104.11186 (2021) - [i32]Yunchang Yang, Tianhao Wu, Han Zhong, Evrard Garcelon, Matteo Pirotta, Alessandro Lazaric, Liwei Wang, Simon S. Du:
A Unified Framework for Conservative Exploration. CoRR abs/2106.11692 (2021) - [i31]Andrea Tirinzoni, Matteo Pirotta, Alessandro Lazaric:
A Fully Problem-Dependent Regret Lower Bound for Finite-Horizon MDPs. CoRR abs/2106.13013 (2021) - [i30]Matteo Papini, Andrea Tirinzoni, Aldo Pacchiano, Marcello Restelli, Alessandro Lazaric, Matteo Pirotta:
Reinforcement Learning in Linear MDPs: Constant Regret and Representation Selection. CoRR abs/2110.14798 (2021) - [i29]Jean Tarbouriech, Omar Darwiche Domingues, Pierre Ménard, Matteo Pirotta, Michal Valko, Alessandro Lazaric:
Adaptive Multi-Goal Exploration. CoRR abs/2111.12045 (2021) - [i28]Paul Luyo, Evrard Garcelon, Alessandro Lazaric, Matteo Pirotta:
Differentially Private Exploration in Reinforcement Learning with Linear Representation. CoRR abs/2112.01585 (2021) - [i27]Evrard Garcelon, Kamalika Chaudhuri, Vianney Perchet, Matteo Pirotta:
Privacy Amplification via Shuffling for Linear Contextual Bandits. CoRR abs/2112.06008 (2021) - [i26]Evrard Garcelon, Vashist Avadhanula, Alessandro Lazaric, Matteo Pirotta:
Top K Ranking for Multi-Armed Bandit with Noisy Evaluations. CoRR abs/2112.06517 (2021) - 2020
- [j5]Alberto Maria Metelli
On the use of the policy gradient and Hessian in inverse reinforcement learning. Intelligenza Artificiale 14(1): 117-150 (2020) - [c30]Evrard Garcelon, Mohammad Ghavamzadeh, Alessandro Lazaric, Matteo Pirotta:
Improved Algorithms for Conservative Exploration in Bandits. AAAI 2020: 3962-3969 - [c29]Evrard Garcelon, Mohammad Ghavamzadeh, Alessandro Lazaric, Matteo Pirotta:
Conservative Exploration in Reinforcement Learning. AISTATS 2020: 1431-1441 - [c28]Andrea Zanette, David Brandfonbrener, Emma Brunskill, Matteo Pirotta, Alessandro Lazaric:
Frequentist Regret Bounds for Randomized Least-Squares Value Iteration. AISTATS 2020: 1954-1964 - [c27]Jean Tarbouriech, Evrard Garcelon, Michal Valko, Matteo Pirotta, Alessandro Lazaric:
No-Regret Exploration in Goal-Oriented Reinforcement Learning. ICML 2020: 9428-9437 - [c26]Evrard Garcelon, Baptiste Rozière, Laurent Meunier, Jean Tarbouriech, Olivier Teytaud, Alessandro Lazaric, Matteo Pirotta:
Adversarial Attacks on Linear Contextual Bandits. NeurIPS 2020 - [c25]Jean Tarbouriech, Matteo Pirotta, Michal Valko, Alessandro Lazaric:
Improved Sample Complexity for Incremental Autonomous Exploration in MDPs. NeurIPS 2020 - [c24]Andrea Tirinzoni, Matteo Pirotta, Marcello Restelli, Alessandro Lazaric:
An Asymptotically Optimal Primal-Dual Incremental Algorithm for Contextual Linear Bandits. NeurIPS 2020 - [c23]Jean Tarbouriech, Shubhanshu Shekhar, Matteo Pirotta, Mohammad Ghavamzadeh, Alessandro Lazaric:
Active Model Estimation in Markov Decision Processes. UAI 2020: 1019-1028 - [i25]Michiel van der Meer, Matteo Pirotta, Elia Bruni:
Exploiting Language Instructions for Interpretable and Compositional Reinforcement Learning. CoRR abs/2001.04418 (2020) - [i24]Jian Qian, Ronan Fruit, Matteo Pirotta, Alessandro Lazaric:
Concentration Inequalities for Multinoulli Random Variables. CoRR abs/2001.11595 (2020) - [i23]Evrard Garcelon, Mohammad Ghavamzadeh, Alessandro Lazaric, Matteo Pirotta:
Conservative Exploration in Reinforcement Learning. CoRR abs/2002.03218 (2020) - [i22]Evrard Garcelon, Mohammad Ghavamzadeh, Alessandro Lazaric, Matteo Pirotta:
Improved Algorithms for Conservative Exploration in Bandits. CoRR abs/2002.03221 (2020) - [i21]Evrard Garcelon, Baptiste Rozière, Laurent Meunier, Jean Tarbouriech, Olivier Teytaud, Alessandro Lazaric, Matteo Pirotta:
Adversarial Attacks on Linear Contextual Bandits. CoRR abs/2002.03839 (2020) - [i20]Yonathan Efroni, Shie Mannor, Matteo Pirotta:
Exploration-Exploitation in Constrained MDPs. CoRR abs/2003.02189 (2020) - [i19]Jean Tarbouriech, Shubhanshu Shekhar, Matteo Pirotta, Mohammad Ghavamzadeh, Alessandro Lazaric:
Active Model Estimation in Markov Decision Processes. CoRR abs/2003.03297 (2020) - [i18]Omar Darwiche Domingues, Pierre Ménard, Matteo Pirotta, Emilie Kaufmann, Michal Valko:
Regret Bounds for Kernel-Based Reinforcement Learning. CoRR abs/2004.05599 (2020) - [i17]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 abs/2005.02934 (2020) - [i16]Omar Darwiche Domingues, Pierre Ménard, Matteo Pirotta, Emilie Kaufmann, Michal Valko:
A Kernel-Based Approach to Non-Stationary Reinforcement Learning in Metric Spaces. CoRR abs/2007.05078 (2020) - [i15]Ronan Fruit, Matteo Pirotta, Alessandro Lazaric:
Improved Analysis of UCRL2 with Empirical Bernstein Inequality. CoRR abs/2007.05456 (2020) - [i14]Jean Tarbouriech, Matteo Pirotta, Michal Valko, Alessandro Lazaric:
A Provably Efficient Sample Collection Strategy for Reinforcement Learning. CoRR abs/2007.06437 (2020) - [i13]Evrard Garcelon, Vianney Perchet, Ciara Pike-Burke, Matteo Pirotta:
Local Differentially Private Regret Minimization in Reinforcement Learning. CoRR abs/2010.07778 (2020) - [i12]Andrea Tirinzoni, Matteo Pirotta, Marcello Restelli, Alessandro Lazaric:
An Asymptotically Optimal Primal-Dual Incremental Algorithm for Contextual Linear Bandits. CoRR abs/2010.12247 (2020) - [i11]Jean Tarbouriech, Matteo Pirotta, Michal Valko, Alessandro Lazaric:
Improved Sample Complexity for Incremental Autonomous Exploration in MDPs. CoRR abs/2012.14755 (2020)
2010 – 2019
- 2019
- [c22]Jian Qian, Ronan Fruit, Matteo Pirotta, Alessandro Lazaric:
Exploration Bonus for Regret Minimization in Discrete and Continuous Average Reward MDPs. NeurIPS 2019: 4891-4900 - [c21]Ronald Ortner, Matteo Pirotta, Alessandro Lazaric, Ronan Fruit, Odalric-Ambrym Maillard:
Regret Bounds for Learning State Representations in Reinforcement Learning. NeurIPS 2019: 12717-12727 - [i10]Matteo Papini, Matteo Pirotta, Marcello Restelli:
Smoothing Policies and Safe Policy Gradients. CoRR abs/1905.03231 (2019) - [i9]Andrea Zanette, David Brandfonbrener, Matteo Pirotta, Alessandro Lazaric:
Frequentist Regret Bounds for Randomized Least-Squares Value Iteration. CoRR abs/1911.00567 (2019) - [i8]Jean Tarbouriech, Evrard Garcelon, Michal Valko, Matteo Pirotta, Alessandro Lazaric:
No-Regret Exploration in Goal-Oriented Reinforcement Learning. CoRR abs/1912.03517 (2019) - 2018
- [c20]Ronan Fruit, Matteo Pirotta, Alessandro Lazaric, Ronald Ortner:
Efficient Bias-Span-Constrained Exploration-Exploitation in Reinforcement Learning. ICML 2018: 1573-1581 - [c19]Matteo Papini, Damiano Binaghi, Giuseppe Canonaco, Matteo Pirotta, Marcello Restelli:
Stochastic Variance-Reduced Policy Gradient. ICML 2018: 4023-4032 - [c18]Andrea Tirinzoni, Andrea Sessa, Matteo Pirotta, Marcello Restelli:
Importance Weighted Transfer of Samples in Reinforcement Learning. ICML 2018: 4943-4952 - [c17]Davide Di Febbo, Emilia Ambrosini
Does Reinforcement Learning outperform PID in the control of FES-induced elbow flex-extension? MeMeA 2018: 1-6 - [c16]Ronan Fruit, Matteo Pirotta, Alessandro Lazaric:
Near Optimal Exploration-Exploitation in Non-Communicating Markov Decision Processes. NeurIPS 2018: 2998-3008 - [i7]Ronan Fruit, Matteo Pirotta, Alessandro Lazaric, Ronald Ortner:
Efficient Bias-Span-Constrained Exploration-Exploitation in Reinforcement Learning. CoRR abs/1802.04020 (2018) - [i6]Andrea Tirinzoni, Andrea Sessa, Matteo Pirotta, Marcello Restelli:
Importance Weighted Transfer of Samples in Reinforcement Learning. CoRR abs/1805.10886 (2018) - [i5]Matteo Papini, Damiano Binaghi, Giuseppe Canonaco, Matteo Pirotta, Marcello Restelli:
Stochastic Variance-Reduced Policy Gradient. CoRR abs/1806.05618 (2018) - [i4]Ronan Fruit, Matteo Pirotta, Alessandro Lazaric:
Near Optimal Exploration-Exploitation in Non-Communicating Markov Decision Processes. CoRR abs/1807.02373 (2018) - [i3]Jian Qian, Ronan Fruit, Matteo Pirotta, Alessandro Lazaric:
Exploration Bonus for Regret Minimization in Undiscounted Discrete and Continuous Markov Decision Processes. CoRR abs/1812.04363 (2018) - 2017
- [j4]Simone Parisi, Matteo Pirotta, Jan Peters:
Manifold-based multi-objective policy search with sample reuse. Neurocomputing 263: 3-14 (2017) - [c15]Carlo D'Eramo, Alessandro Nuara, Matteo Pirotta, Marcello Restelli:
Estimating the Maximum Expected Value in Continuous Reinforcement Learning Problems. AAAI 2017: 1840-1846 - [c14]Samuele Tosatto, Matteo Pirotta, Carlo D'Eramo, Marcello Restelli:
Boosted Fitted Q-Iteration. ICML 2017: 3434-3443 - [c13]Alberto Maria Metelli, Matteo Pirotta, Marcello Restelli:
Compatible Reward Inverse Reinforcement Learning. NIPS 2017: 2050-2059 - [c12]Ronan Fruit, Matteo Pirotta, Alessandro Lazaric, Emma Brunskill:
Regret Minimization in MDPs with Options without Prior Knowledge. NIPS 2017: 3166-3176 - [c11]Matteo Papini, Matteo Pirotta, Marcello Restelli:
Adaptive Batch Size for Safe Policy Gradients. NIPS 2017: 3591-3600 - [c10]Davide Tateo
Gradient-based minimization for multi-expert Inverse Reinforcement Learning. SSCI 2017: 1-8 - [i2]Matteo Pirotta, Marcello Restelli:
Cost-Sensitive Approach to Batch Size Adaptation for Gradient Descent. CoRR abs/1712.03428 (2017) - 2016
- [b1]Matteo Pirotta:
Reinforcement learning: from theory to algorithms. Polytechnic University of Milan, Italy, 2016 - [j3]Simone Parisi, Matteo Pirotta, Marcello Restelli:
Multi-objective Reinforcement Learning through Continuous Pareto Manifold Approximation. J. Artif. Intell. Res. 57: 187-227 (2016) - [j2]Giorgio Manganini
Policy Search for the Optimal Control of Markov Decision Processes: A Novel Particle-Based Iterative Scheme. IEEE Trans. Cybern. 46(11): 2643-2655 (2016) - [c9]Matteo Pirotta, Marcello Restelli:
Inverse Reinforcement Learning through Policy Gradient Minimization. AAAI 2016: 1993-1999 - 2015
- [j1]Matteo Pirotta, Marcello Restelli
Policy gradient in Lipschitz Markov Decision Processes. Mach. Learn. 100(2-3): 255-283 (2015) - [c8]Matteo Pirotta, Simone Parisi, Marcello Restelli:
Multi-Objective Reinforcement Learning with Continuous Pareto Frontier Approximation. AAAI 2015: 2928-2934 - [c7]Danilo Caporale
Optimal control to reduce emissions in gasoline engines: an iterative learning control approach for ECU calibration maps improvement. ECC 2015: 1420-1425 - [c6]Giorgio Manganini
Following Newton direction in Policy Gradient with parameter exploration. IJCNN 2015: 1-8 - 2014
- [c5]Simone Parisi, Matteo Pirotta, Nicola Smacchia, Luca Bascetta
Policy gradient approaches for multi-objective sequential decision making: A comparison. ADPRL 2014: 1-8 - [c4]Simone Parisi, Matteo Pirotta, Nicola Smacchia, Luca Bascetta
Policy gradient approaches for multi-objective sequential decision making. IJCNN 2014: 2323-2330 - [i1]Matteo Pirotta, Simone Parisi, Marcello Restelli:
Multi-objective Reinforcement Learning with Continuous Pareto Frontier Approximation. CoRR abs/1406.3497 (2014) - 2013
- [c3]Matteo Pirotta, Marcello Restelli, Alessio Pecorino, Daniele Calandriello:
Safe Policy Iteration. ICML (3) 2013: 307-315 - [c2]Matteo Pirotta, Marcello Restelli, Luca Bascetta:
Adaptive Step-Size for Policy Gradient Methods. NIPS 2013: 1394-1402 - 2011
- [c1]Martino Migliavacca, Alessio Pecorino, Matteo Pirotta, Marcello Restelli
Fitted policy search. ADPRL 2011: 287-294
Coauthor Index
[c50] [c49] [i42] [j9] [c48] [c47] [c46] [c45] [i41] [c43] [c42] [c40] [c39] [i40] [i39] [i38] [i37] [i36] [c37] [c35] [c34] [c33] [c31] [i34] [i33] [i32] [i31] [i30] [i29] [i28] [i26] [c30] [c29] [c28] [c27] [c26] [c25] [c24] [c23] [i24] [i23] [i22] [i21] [i19] [i17] [i15] [i14] [i12] [i11] [c22] [c21] [i9] [i8] [c20] [c16] [i7] [i4] [i3] [c12]
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