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Alberto Maria Metelli
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2020 – today
- 2024
- [j15]Matteo Papini, Giorgio Manganini, Alberto Maria Metelli, Marcello Restelli:
Policy Gradient with Active Importance Sampling. RLJ 2: 645-675 (2024) - [j14]Gianluca Drappo, Alberto Maria Metelli, Marcello Restelli:
A Provably Efficient Option-Based Algorithm for both High-Level and Low-Level Learning. RLJ 2: 819-839 (2024) - [j13]Paolo Bonetti
, Alberto Maria Metelli, Marcello Restelli:
Interpretable linear dimensionality reduction based on bias-variance analysis. Data Min. Knowl. Discov. 38(4): 1713-1781 (2024) - [j12]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) - [j11]Riccardo Poiani
Optimizing Empty Container Repositioning and Fleet Deployment via Configurable Semi-POMDPs. IEEE Trans. Intell. Transp. Syst. 25(5): 4704-4711 (2024) - [c48]Théo Vincent, Alberto Maria Metelli, Boris Belousov, Jan Peters, Marcello Restelli, Carlo D'Eramo:
Parameterized Projected Bellman Operator. AAAI 2024: 15402-15410 - [c47]Alberto Maria Metelli:
Recent Advancements in Inverse Reinforcement Learning. AAAI 2024: 22680 - [c46]Francesco Bacchiocchi, Gianmarco Genalti, Davide Maran, Marco Mussi, Marcello Restelli, Nicola Gatti, Alberto Maria Metelli:
Autoregressive Bandits. AISTATS 2024: 937-945 - [c45]Paolo Battellani, Alberto Maria Metelli, Francesco Trovò:
Dissimilarity Bandits. AISTATS 2024: 3637-3645 - [c44]Angelo Damiani, Gustavo Viera-López, Giorgio Manganini, Alberto Maria Metelli, Marcello Restelli:
Transfer Learning for Dynamical Systems Models via Autoencoders and GANs. ACC 2024: 8-14 - [c43]Gianmarco Genalti, Lupo Marsigli, Nicola Gatti, Alberto Maria Metelli:
(ε, u)-Adaptive Regret Minimization in Heavy-Tailed Bandits. COLT 2024: 1882-1915 - [c42]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 - [c41]Gianmarco Genalti, Marco Mussi, Nicola Gatti, Marcello Restelli, Matteo Castiglioni, Alberto Maria Metelli:
Graph-Triggered Rising Bandits. ICML 2024 - [c40]Filippo Lazzati, Mirco Mutti, Alberto Maria Metelli:
Offline Inverse RL: New Solution Concepts and Provably Efficient Algorithms. ICML 2024 - [c39]Davide Maran, Alberto Maria Metelli, Matteo Papini, Marcello Restelli:
No-Regret Reinforcement Learning in Smooth MDPs. ICML 2024 - [c38]Alessandro Montenegro, Marco Mussi, Alberto Maria Metelli, Matteo Papini:
Learning Optimal Deterministic Policies with Stochastic Policy Gradients. ICML 2024 - [c37]Marco Mussi, Simone Drago, Marcello Restelli, Alberto Maria Metelli:
Factored-Reward Bandits with Intermediate Observations. ICML 2024 - [c36]Marco Mussi, Alessandro Montenegro, Francesco Trovò, Marcello Restelli, Alberto Maria Metelli:
Best Arm Identification for Stochastic Rising Bandits. ICML 2024 - [c35]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 - [c34]Paolo Bonetti, Alberto Maria Metelli, Marcello Restelli:
Causal Feature Selection via Transfer Entropy. IJCNN 2024: 1-10 - [c33]Vincenzo De Paola, Giuseppe Calcagno, Alberto Maria Metelli, Marcello Restelli:
The Power of Hybrid Learning in Industrial Robotics: Efficient Grasping Strategies with Supervised-Driven Reinforcement Learning. IJCNN 2024: 1-9 - [c32]Paolo Bonetti, Alberto Maria Metelli, Marcello Restelli:
Interpetable Target-Feature Aggregation for Multi-task Learning Based on Bias-Variance Analysis. ECML/PKDD (6) 2024: 74-91 - [i48]Riccardo Poiani, Gabriele Curti, Alberto Maria Metelli, Marcello Restelli:
Inverse Reinforcement Learning with Sub-optimal Experts. CoRR abs/2401.03857 (2024) - [i47]Davide Maran, Alberto Maria Metelli, Matteo Papini, Marcello Restelli:
No-Regret Reinforcement Learning in Smooth MDPs. CoRR abs/2402.03792 (2024) - [i46]Khaled Eldowa, Nicolò Cesa-Bianchi, Alberto Maria Metelli, Marcello Restelli:
Information Capacity Regret Bounds for Bandits with Mediator Feedback. CoRR abs/2402.10282 (2024) - [i45]Alberto Maria Metelli:
Performance Improvement Bounds for Lipschitz Configurable Markov Decision Processes. CoRR abs/2402.13821 (2024) - [i44]Filippo Lazzati, Mirco Mutti, Alberto Maria Metelli:
Offline Inverse RL: New Solution Concepts and Provably Efficient Algorithms. CoRR abs/2402.15392 (2024) - [i43]Alessandro Montenegro, Marco Mussi, Alberto Maria Metelli, Matteo Papini:
Learning Optimal Deterministic Policies with Stochastic Policy Gradients. CoRR abs/2405.02235 (2024) - [i42]Matteo Papini, Giorgio Manganini, Alberto Maria Metelli, Marcello Restelli:
Policy Gradient with Active Importance Sampling. CoRR abs/2405.05630 (2024) - [i41]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) - [i40]Riccardo Poiani, Rémy Degenne, Emilie Kaufmann, Alberto Maria Metelli, Marcello Restelli:
Optimal Multi-Fidelity Best-Arm Identification. CoRR abs/2406.03033 (2024) - [i39]Filippo Lazzati, Mirco Mutti, Alberto Maria Metelli:
How to Scale Inverse RL to Large State Spaces? A Provably Efficient Approach. CoRR abs/2406.03812 (2024) - [i38]Paolo Bonetti, Alberto Maria Metelli, Marcello Restelli:
Interpetable Target-Feature Aggregation for Multi-Task Learning based on Bias-Variance Analysis. CoRR abs/2406.07991 (2024) - [i37]Gianluca Drappo, Alberto Maria Metelli, Marcello Restelli:
A Provably Efficient Option-Based Algorithm for both High-Level and Low-Level Learning. CoRR abs/2406.15124 (2024) - [i36]Marco Mussi, Simone Drago, Alberto Maria Metelli:
Open Problem: Tight Bounds for Kernelized Multi-Armed Bandits with Bernoulli Rewards. CoRR abs/2407.06321 (2024) - [i35]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) - [i34]Gianvito Losapio, Davide Beretta, Marco Mussi, Alberto Maria Metelli, Marcello Restelli:
State and Action Factorization in Power Grids. CoRR abs/2409.04467 (2024) - [i33]Marco Fiandri, Alberto Maria Metelli, Francesco Trovò:
Sliding-Window Thompson Sampling for Non-Stationary Settings. CoRR abs/2409.05181 (2024) - [i32]Gianmarco Genalti, Marco Mussi, Nicola Gatti, Marcello Restelli, Matteo Castiglioni, Alberto Maria Metelli:
Bridging Rested and Restless Bandits with Graph-Triggering: Rising and Rotting. CoRR abs/2409.05980 (2024) - [i31]Filippo Lazzati, Alberto Maria Metelli:
Learning Utilities from Demonstrations in Markov Decision Processes. CoRR abs/2409.17355 (2024) - [i30]Alessio Russo, Alberto Maria Metelli, Marcello Restelli:
Efficient Learning of POMDPs with Known Observation Model in Average-Reward Setting. CoRR abs/2410.01331 (2024) - 2023
- [j10]Marco Mussi
ARLO: A framework for Automated Reinforcement Learning. Expert Syst. Appl. 224: 119883 (2023) - [j9]Gianluca Drappo, Alberto Maria Metelli, Marcello Restelli:
An Option-Dependent Analysis of Regret Minimization Algorithms in Finite-Horizon Semi-MDP. Trans. Mach. Learn. Res. 2023 (2023) - [j8]Filippo Fedeli
IWDA: Importance Weighting for Drift Adaptation in Streaming Supervised Learning Problems. IEEE Trans. Neural Networks Learn. Syst. 34(10): 6813-6823 (2023) - [c31]Amarildo Likmeta, Matteo Sacco, Alberto Maria Metelli, Marcello Restelli:
Wasserstein Actor-Critic: Directed Exploration via Optimism for Continuous-Actions Control. AAAI 2023: 8782-8790 - [c30]Davide Maran, Alberto Maria Metelli, Marcello Restelli:
Tight Performance Guarantees of Imitator Policies with Continuous Actions. AAAI 2023: 9073-9080 - [c29]Luca Sabbioni, Luca Al Daire, Lorenzo Bisi, Alberto Maria Metelli, Marcello Restelli:
Simultaneously Updating All Persistence Values in Reinforcement Learning. AAAI 2023: 9668-9676 - [c28]Alberto Maria Metelli, Mirco Mutti, Marcello Restelli:
A Tale of Sampling and Estimation in Discounted Reinforcement Learning. AISTATS 2023: 4575-4601 - [c27]Alberto Maria Metelli, Filippo Lazzati, Marcello Restelli:
Towards Theoretical Understanding of Inverse Reinforcement Learning. ICML 2023: 24555-24591 - [c26]Marco Mussi, Alberto Maria Metelli, Marcello Restelli:
Dynamical Linear Bandits. ICML 2023: 25563-25587 - [c25]Riccardo Poiani, Alberto Maria Metelli, Marcello Restelli:
Truncating Trajectories in Monte Carlo Reinforcement Learning. ICML 2023: 27994-28042 - [c24]Khaled Eldowa
Information-Theoretic Regret Bounds for Bandits with Fixed Expert Advice. ITW 2023: 30-35 - [c23]Riccardo Poiani, Nicole Nobili, Alberto Maria Metelli, Marcello Restelli:
Truncating Trajectories in Monte Carlo Policy Evaluation: an Adaptive Approach. NeurIPS 2023 - [c22]Riccardo Zamboni, Alberto Maria Metelli, Marcello Restelli:
Distributional Policy Evaluation: a Maximum Entropy approach to Representation Learning. NeurIPS 2023 - [c21]Alberto Maria Metelli, Samuele Meta, Marcello Restelli:
On the Relation between Policy Improvement and Off-Policy Minimum-Variance Policy Evaluation. UAI 2023: 1423-1433 - [i29]Marco Mussi, Alessandro Montenegro, Francesco Trovò, Marcello Restelli, Alberto Maria Metelli:
Best Arm Identification for Stochastic Rising Bandits. CoRR abs/2302.07510 (2023) - [i28]Amarildo Likmeta, Matteo Sacco, Alberto Maria Metelli, Marcello Restelli:
Wasserstein Actor-Critic: Directed Exploration via Optimism for Continuous-Actions Control. CoRR abs/2303.02378 (2023) - [i27]Khaled Eldowa, Nicolò Cesa-Bianchi, Alberto Maria Metelli, Marcello Restelli:
Information-Theoretic Regret Bounds for Bandits with Fixed Expert Advice. CoRR abs/2303.08102 (2023) - [i26]Paolo Bonetti, Alberto Maria Metelli, Marcello Restelli:
Interpretable Linear Dimensionality Reduction based on Bias-Variance Analysis. CoRR abs/2303.14734 (2023) - [i25]Alberto Maria Metelli, Mirco Mutti, Marcello Restelli:
A Tale of Sampling and Estimation in Discounted Reinforcement Learning. CoRR abs/2304.05073 (2023) - [i24]Alberto Maria Metelli, Filippo Lazzati, Marcello Restelli:
Towards Theoretical Understanding of Inverse Reinforcement Learning. CoRR abs/2304.12966 (2023) - [i23]Riccardo Poiani, Alberto Maria Metelli, Marcello Restelli:
Truncating Trajectories in Monte Carlo Reinforcement Learning. CoRR abs/2305.04361 (2023) - [i22]Gianluca Drappo, Alberto Maria Metelli, Marcello Restelli:
An Option-Dependent Analysis of Regret Minimization Algorithms in Finite-Horizon Semi-Markov Decision Processes. CoRR abs/2305.06936 (2023) - [i21]Paolo Bonetti, Alberto Maria Metelli, Marcello Restelli:
Nonlinear Feature Aggregation: Two Algorithms driven by Theory. CoRR abs/2306.11143 (2023) - [i20]Riccardo Poiani, Alberto Maria Metelli, Marcello Restelli:
Pure Exploration under Mediators' Feedback. CoRR abs/2308.15552 (2023) - [i19]Gianmarco Genalti, Lupo Marsigli, Nicola Gatti, Alberto Maria Metelli:
Towards Fully Adaptive Regret Minimization in Heavy-Tailed Bandits. CoRR abs/2310.02975 (2023) - [i18]Paolo Bonetti, Alberto Maria Metelli, Marcello Restelli:
Causal Feature Selection via Transfer Entropy. CoRR abs/2310.11059 (2023) - [i17]Théo Vincent, Alberto Maria Metelli, Boris Belousov, Jan Peters, Marcello Restelli, Carlo D'Eramo:
Parameterized Projected Bellman Operator. CoRR abs/2312.12869 (2023) - 2022
- [b1]Alberto Maria Metelli:
Exploiting environment configurability in reinforcement learning. Polytechnic University of Milan, Italy, Frontiers in Artificial Intelligence and Applications 361, IOS Press 2022, ISBN 978-1-64368-362-1 - [j7]Alberto Maria Metelli:
A unified view of configurable Markov Decision Processes: Solution concepts, value functions, and operators. Intelligenza Artificiale 16(2): 165-184 (2022) - [j6]Alberto Maria Metelli
Policy space identification in configurable environments. Mach. Learn. 111(6): 2093-2145 (2022) - [c20]Pierre Liotet, Francesco Vidaich, Alberto Maria Metelli, Marcello Restelli:
Lifelong Hyper-Policy Optimization with Multiple Importance Sampling Regularization. AAAI 2022: 7525-7533 - [c19]Manuel Occorso, Luca Sabbioni, Alberto Maria Metelli, Marcello Restelli:
Trust Region Meta Learning for Policy Optimization. Meta-Knowledge Transfer @ ECML/PKDD 2022: 62-74 - [c18]Angelo Damiani, Giorgio Manganini
Balancing Sample Efficiency and Suboptimality in Inverse Reinforcement Learning. ICML 2022: 4618-4629 - [c17]Alberto Maria Metelli, Francesco Trovò, Matteo Pirola, Marcello Restelli:
Stochastic Rising Bandits. ICML 2022: 15421-15457 - [c16]Julen Cestero
Storehouse: a Reinforcement Learning Environment for Optimizing Warehouse Management. IJCNN 2022: 1-9 - [c15]Riccardo Poiani, Alberto Maria Metelli, Marcello Restelli:
Multi-Fidelity Best-Arm Identification. NeurIPS 2022 - [i16]Marco Mussi, Davide Lombarda, Alberto Maria Metelli, Francesco Trovò, Marcello Restelli:
ARLO: A Framework for Automated Reinforcement Learning. CoRR abs/2205.10416 (2022) - [i15]Julen Cestero
Storehouse: a Reinforcement Learning Environment for Optimizing Warehouse Management. CoRR abs/2207.03851 (2022) - [i14]Riccardo Poiani, Ciprian Stirbu, Alberto Maria Metelli, Marcello Restelli:
Optimizing Empty Container Repositioning and Fleet Deployment via Configurable Semi-POMDPs. CoRR abs/2207.12509 (2022) - [i13]Marco Mussi, Alberto Maria Metelli, Marcello Restelli:
Dynamical Linear Bandits. CoRR abs/2211.08997 (2022) - [i12]Luca Sabbioni, Luca Al Daire, Lorenzo Bisi, Alberto Maria Metelli, Marcello Restelli:
Simultaneously Updating All Persistence Values in Reinforcement Learning. CoRR abs/2211.11620 (2022) - [i11]Alberto Maria Metelli, Francesco Trovò, Matteo Pirola, Marcello Restelli:
Stochastic Rising Bandits. CoRR abs/2212.03798 (2022) - [i10]Davide Maran, Alberto Maria Metelli, Marcello Restelli:
Tight Performance Guarantees of Imitator Policies with Continuous Actions. CoRR abs/2212.03922 (2022) - [i9]Francesco Bacchiocchi, Gianmarco Genalti, Davide Maran, Marco Mussi, Marcello Restelli, Nicola Gatti, Alberto Maria Metelli:
Autoregressive Bandits. CoRR abs/2212.06251 (2022) - 2021
- [j5]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) - [j4]Amarildo Likmeta
Dealing with multiple experts and non-stationarity in inverse reinforcement learning: an application to real-life problems. Mach. Learn. 110(9): 2541-2576 (2021) - [c14]Alberto Maria Metelli, Matteo Papini, Pierluca D'Oro, Marcello Restelli:
Policy Optimization as Online Learning with Mediator Feedback. AAAI 2021: 8958-8966 - [c13]Alberto Maria Metelli, Giorgia Ramponi, Alessandro Concetti, Marcello Restelli:
Provably Efficient Learning of Transferable Rewards. ICML 2021: 7665-7676 - [c12]Alberto Maria Metelli, Alessio Russo, Marcello Restelli:
Subgaussian and Differentiable Importance Sampling for Off-Policy Evaluation and Learning. NeurIPS 2021: 8119-8132 - [c11]Giorgia Ramponi, Alberto Maria Metelli, Alessandro Concetti, Marcello Restelli:
Learning in Non-Cooperative Configurable Markov Decision Processes. NeurIPS 2021: 22808-22821 - [i8]Pierre Liotet, Francesco Vidaich, Alberto Maria Metelli, Marcello Restelli:
Lifelong Hyper-Policy Optimization with Multiple Importance Sampling Regularization. CoRR abs/2112.06625 (2021) - 2020
- [j3]Alberto Maria Metelli
On the use of the policy gradient and Hessian in inverse reinforcement learning. Intelligenza Artificiale 14(1): 117-150 (2020) - [j2]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) - [j1]Amarildo Likmeta
Combining reinforcement learning with rule-based controllers for transparent and general decision-making in autonomous driving. Robotics Auton. Syst. 131: 103568 (2020) - [c10]Pierluca D'Oro, Alberto Maria Metelli, Andrea Tirinzoni, Matteo Papini, Marcello Restelli:
Gradient-Aware Model-Based Policy Search. AAAI 2020: 3801-3808 - [c9]Giorgia Ramponi, Amarildo Likmeta
Truly Batch Model-Free Inverse Reinforcement Learning about Multiple Intentions. AISTATS 2020: 2359-2369 - [c8]Alberto Maria Metelli, Flavio Mazzolini, Lorenzo Bisi, Luca Sabbioni, Marcello Restelli:
Control Frequency Adaptation via Action Persistence in Batch Reinforcement Learning. ICML 2020: 6862-6873 - [i7]Alberto Maria Metelli, Flavio Mazzolini, Lorenzo Bisi, Luca Sabbioni, Marcello Restelli:
Control Frequency Adaptation via Action Persistence in Batch Reinforcement Learning. CoRR abs/2002.06836 (2020) - [i6]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
- [c7]Alberto Maria Metelli, Emanuele Ghelfi, Marcello Restelli:
Reinforcement Learning in Configurable Continuous Environments. ICML 2019: 4546-4555 - [c6]Matteo Papini
Optimistic Policy Optimization via Multiple Importance Sampling. ICML 2019: 4989-4999 - [c5]Mario Beraha, Alberto Maria Metelli
Feature Selection via Mutual Information: New Theoretical Insights. IJCNN 2019: 1-9 - [c4]Alberto Maria Metelli, Amarildo Likmeta, Marcello Restelli:
Propagating Uncertainty in Reinforcement Learning via Wasserstein Barycenters. NeurIPS 2019: 4335-4347 - [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]Alberto Maria Metelli, Guglielmo Manneschi, Marcello Restelli:
Policy Space Identification in Configurable Environments. CoRR abs/1909.03984 (2019) - [i3]Pierluca D'Oro, Alberto Maria Metelli, Andrea Tirinzoni, Matteo Papini, Marcello Restelli:
Gradient-Aware Model-based Policy Search. CoRR abs/1909.04115 (2019) - 2018
- [c3]Alberto Maria Metelli, Mirco Mutti, Marcello Restelli:
Configurable Markov Decision Processes. ICML 2018: 3488-3497 - [c2]Alberto Maria Metelli, Matteo Papini, Francesco Faccio, Marcello Restelli:
Policy Optimization via Importance Sampling. NeurIPS 2018: 5447-5459 - [i2]Alberto Maria Metelli, Mirco Mutti, Marcello Restelli:
Configurable Markov Decision Processes. CoRR abs/1806.05415 (2018) - [i1]Alberto Maria Metelli, Matteo Papini, Francesco Faccio, Marcello Restelli:
Policy Optimization via Importance Sampling. CoRR abs/1809.06098 (2018) - 2017
- [c1]Alberto Maria Metelli, Matteo Pirotta, Marcello Restelli:
Compatible Reward Inverse Reinforcement Learning. NIPS 2017: 2050-2059
Coauthor Index
[j15] [j14] [j13] [j12] [j11] [c48] [c46] [c44] [c42] [c41] [c39] [c37] [c36] [c34] [c33] [c32] [i48] [i47] [i46] [i42] [i41] [i40] [i38] [i37] [i34] [i32] [i30] [j10] [j9] [j8] [c31] [c30] [c29] [c28] [c27] [c26] [c25] [c24] [c23] [c22] [c21] [i29] [i28] [i27] [i26] [i25] [i24] [i23] [i22] [i21] [i20] [i18] [i17] [j6] [c20] [c19] [c18] [c17] [c16] [c15] [i16] [i15] [i14] [i13] [i12] [i11] [i10] [i9] [j5] [j4] [c14] [c13] [c12] [c11] [i8] [j3] [j2] [j1] [c10] [c9] [c8] [i7] [i6] [c7] [c6] [c5] [c4] [i5] [i4] [i3] [c3] [c2] [i2] [i1] [c1]
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