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Matteo Papini
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2020 – today
- 2024
- [j3]Gabor Paczolay, Matteo Papini, Alberto Maria Metelli, István Á. Harmati, Marcello Restelli:
Sample complexity of variance-reduced policy gradient: weaker assumptions and lower bounds. Mach. Learn. 113(9): 6475-6510 (2024) - [c23]Germano Gabbianelli, Gergely Neu, Matteo Papini, Nneka Okolo:
Offline Primal-Dual Reinforcement Learning for Linear MDPs. AISTATS 2024: 3169-3177 - [c22]Germano Gabbianelli, Gergely Neu, Matteo Papini:
Importance-Weighted Offline Learning Done Right. ALT 2024: 614-634 - [c21]Davide Maran, Alberto Maria Metelli, Matteo Papini, Marcello Restelli:
Projection by Convolution: Optimal Sample Complexity for Reinforcement Learning in Continuous-Space MDPs. COLT 2024: 3743-3774 - [c20]Gergely Neu, Matteo Papini, Ludovic Schwartz:
Optimistic Information Directed Sampling. COLT 2024: 3970-4006 - [c19]Davide Maran, Alberto Maria Metelli, Matteo Papini, Marcello Restelli:
No-Regret Reinforcement Learning in Smooth MDPs. ICML 2024 - [c18]Alessandro Montenegro, Marco Mussi, Alberto Maria Metelli, Matteo Papini:
Learning Optimal Deterministic Policies with Stochastic Policy Gradients. ICML 2024 - [c17]Francesco Bacchiocchi, Francesco Emanuele Stradi, Matteo Papini, Alberto Maria Metelli, Nicola Gatti:
Online Learning with Off-Policy Feedback in Adversarial MDPs. IJCAI 2024: 3697-3705 - [c16]Matteo Papini, Giorgio Manganini, Alberto Maria Metelli, Marcello Restelli:
Policy Gradient with Active Importance Sampling. RLC 2024: 645-675 - [i20]Davide Maran, Alberto Maria Metelli, Matteo Papini, Marcello Restelli:
No-Regret Reinforcement Learning in Smooth MDPs. CoRR abs/2402.03792 (2024) - [i19]Gergely Neu, Matteo Papini, Ludovic Schwartz:
Optimistic Information Directed Sampling. CoRR abs/2402.15411 (2024) - [i18]Alessandro Montenegro, Marco Mussi, Alberto Maria Metelli, Matteo Papini:
Learning Optimal Deterministic Policies with Stochastic Policy Gradients. CoRR abs/2405.02235 (2024) - [i17]Matteo Papini, Giorgio Manganini, Alberto Maria Metelli, Marcello Restelli:
Policy Gradient with Active Importance Sampling. CoRR abs/2405.05630 (2024) - [i16]Davide Maran, Alberto Maria Metelli, Matteo Papini, Marcello Restelli:
Projection by Convolution: Optimal Sample Complexity for Reinforcement Learning in Continuous-Space MDPs. CoRR abs/2405.06363 (2024) - [i15]Alessandro Montenegro, Marco Mussi, Matteo Papini, Alberto Maria Metelli:
Last-Iterate Global Convergence of Policy Gradients for Constrained Reinforcement Learning. CoRR abs/2407.10775 (2024) - 2023
- [c15]Germano Gabbianelli, Gergely Neu, Matteo Papini:
Online Learning with Off-Policy Feedback. ALT 2023: 620-641 - [i14]Germano Gabbianelli, Gergely Neu, Nneka Okolo, Matteo Papini:
Offline Primal-Dual Reinforcement Learning for Linear MDPs. CoRR abs/2305.12944 (2023) - [i13]Germano Gabbianelli, Gergely Neu, Matteo Papini:
Importance-Weighted Offline Learning Done Right. CoRR abs/2309.15771 (2023) - 2022
- [j2]Matteo Papini, Matteo Pirotta, Marcello Restelli:
Smoothing policies and safe policy gradients. Mach. Learn. 111(11): 4081-4137 (2022) - [c14]Gergely Neu, Julia Olkhovskaya, Matteo Papini, Ludovic Schwartz:
Lifting the Information Ratio: An Information-Theoretic Analysis of Thompson Sampling for Contextual Bandits. NeurIPS 2022 - [c13]Andrea Tirinzoni, Matteo Papini, Ahmed Touati, Alessandro Lazaric, Matteo Pirotta:
Scalable Representation Learning in Linear Contextual Bandits with Constant Regret Guarantees. NeurIPS 2022 - [i12]Gergely Neu, Julia Olkhovskaya, Matteo Papini, Ludovic Schwartz:
Lifting the Information Ratio: An Information-Theoretic Analysis of Thompson Sampling for Contextual Bandits. CoRR abs/2205.13924 (2022) - [i11]Germano Gabbianelli, Matteo Papini, Gergely Neu:
Online Learning with Off-Policy Feedback. CoRR abs/2207.08956 (2022) - [i10]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) - 2021
- [b1]Matteo Papini:
Safe policy optimization. Polytechnic University of Milan, Italy, 2021 - [c12]Alberto Maria Metelli, Matteo Papini, Pierluca D'Oro, Marcello Restelli:
Policy Optimization as Online Learning with Mediator Feedback. AAAI 2021: 8958-8966 - [c11]Alessandro Gianola, Marco Montali, Matteo Papini:
Automated Reasoning for Reinforcement Learning Agents in Structured Environments. OVERLAY@GandALF 2021: 43-48 - [c10]Matteo Papini, Andrea Tirinzoni, Marcello Restelli, Alessandro Lazaric, Matteo Pirotta:
Leveraging Good Representations in Linear Contextual Bandits. ICML 2021: 8371-8380 - [c9]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 - [i9]Matteo Papini, Andrea Tirinzoni, Marcello Restelli, Alessandro Lazaric, Matteo Pirotta:
Leveraging Good Representations in Linear Contextual Bandits. CoRR abs/2104.03781 (2021) - [i8]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) - 2020
- [j1]Alberto Maria Metelli, Matteo Papini, Nico Montali, Marcello Restelli:
Importance Sampling Techniques for Policy Optimization. J. Mach. Learn. Res. 21: 141:1-141:75 (2020) - [c8]Pierluca D'Oro, Alberto Maria Metelli, Andrea Tirinzoni, Matteo Papini, Marcello Restelli:
Gradient-Aware Model-Based Policy Search. AAAI 2020: 3801-3808 - [c7]Matteo Papini, Andrea Battistello, Marcello Restelli:
Balancing Learning Speed and Stability in Policy Gradient via Adaptive Exploration. AISTATS 2020: 1188-1199 - [c6]Lorenzo Bisi, Luca Sabbioni, Edoardo Vittori, Matteo Papini, Marcello Restelli:
Risk-Averse Trust Region Optimization for Reward-Volatility Reduction. IJCAI 2020: 4583-4589 - [i7]Alberto Maria Metelli, Matteo Papini, Pierluca D'Oro, Marcello Restelli:
Policy Optimization as Online Learning with Mediator Feedback. CoRR abs/2012.08225 (2020)
2010 – 2019
- 2019
- [c5]Matteo Papini, Alberto Maria Metelli, Lorenzo Lupo, Marcello Restelli:
Optimistic Policy Optimization via Multiple Importance Sampling. ICML 2019: 4989-4999 - [c4]Mario Beraha, Alberto Maria Metelli, Matteo Papini, Andrea Tirinzoni, Marcello Restelli:
Feature Selection via Mutual Information: New Theoretical Insights. IJCNN 2019: 1-9 - [i6]Matteo Papini, Matteo Pirotta, Marcello Restelli:
Smoothing Policies and Safe Policy Gradients. CoRR abs/1905.03231 (2019) - [i5]Mario Beraha, Alberto Maria Metelli, Matteo Papini, Andrea Tirinzoni, Marcello Restelli:
Feature Selection via Mutual Information: New Theoretical Insights. CoRR abs/1907.07384 (2019) - [i4]Pierluca D'Oro, Alberto Maria Metelli, Andrea Tirinzoni, Matteo Papini, Marcello Restelli:
Gradient-Aware Model-based Policy Search. CoRR abs/1909.04115 (2019) - [i3]Lorenzo Bisi, Luca Sabbioni, Edoardo Vittori, Matteo Papini, Marcello Restelli:
Risk-Averse Trust Region Optimization for Reward-Volatility Reduction. CoRR abs/1912.03193 (2019) - 2018
- [c3]Matteo Papini, Damiano Binaghi, Giuseppe Canonaco, Matteo Pirotta, Marcello Restelli:
Stochastic Variance-Reduced Policy Gradient. ICML 2018: 4023-4032 - [c2]Alberto Maria Metelli, Matteo Papini, Francesco Faccio, Marcello Restelli:
Policy Optimization via Importance Sampling. NeurIPS 2018: 5447-5459 - [i2]Matteo Papini, Damiano Binaghi, Giuseppe Canonaco, Matteo Pirotta, Marcello Restelli:
Stochastic Variance-Reduced Policy Gradient. CoRR abs/1806.05618 (2018) - [i1]Alberto Maria Metelli, Matteo Papini, Francesco Faccio, Marcello Restelli:
Policy Optimization via Importance Sampling. CoRR abs/1809.06098 (2018) - 2017
- [c1]Matteo Papini, Matteo Pirotta, Marcello Restelli:
Adaptive Batch Size for Safe Policy Gradients. NIPS 2017: 3591-3600
Coauthor Index
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last updated on 2024-11-13 23:47 CET by the dblp team
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