Matteo Pirotta
Publication record assembled from the DBLP archive of ranked conferences.
Papers indexed
32
Venues
7
Active years
2013–2025
Best venue rank
A*
Where they publish
Papers
32 indexed papers, newest first.
| Year | Venue | Title | Authors |
|---|---|---|---|
| 2025 | ICLR | Zero-Shot Whole-Body Humanoid Control via Behavioral Foundation Models. | Andrea Tirinzoni, Ahmed Touati, Jesse Farebrother, Mateusz Guzek, Anssi Kanervisto, Yingchen Xu, Alessandro Lazaric, Matteo Pirotta |
| 2025 | ICML | Temporal Difference Flows. | Jesse Farebrother, Matteo Pirotta, Andrea Tirinzoni, Rmi Munos, Alessandro Lazaric, Ahmed Touati |
| 2024 | ICLR | Fast Imitation via Behavior Foundation Models. | Matteo Pirotta, Andrea Tirinzoni, Ahmed Touati, Alessandro Lazaric, Yann Ollivier |
| 2024 | ICML | Simple Ingredients for Offline Reinforcement Learning. | Edoardo Cetin, Andrea Tirinzoni, Matteo Pirotta, Alessandro Lazaric, Yann Ollivier, Ahmed Touati |
| 2023 | AISTATS | On the Complexity of Representation Learning in Contextual Linear Bandits. | Andrea Tirinzoni, Matteo Pirotta, Alessandro Lazaric |
| 2023 | ALT | Reaching Goals is Hard: Settling the Sample Complexity of the Stochastic Shortest Path. | Liyu Chen, Andrea Tirinzoni, Matteo Pirotta, Alessandro Lazaric |
| 2023 | ICLR | Contextual bandits with concave rewards, and an application to fair ranking. | Virginie Do, Elvis Dohmatob, Matteo Pirotta, Alessandro Lazaric, Nicolas Usunier |
| 2023 | ICML | Layered State Discovery for Incremental Autonomous Exploration. | Liyu Chen, Andrea Tirinzoni, Alessandro Lazaric, Matteo Pirotta |
| 2022 | AISTATS | Top K Ranking for Multi-Armed Bandit with Noisy Evaluations. | Evrard Garcelon, Vashist Avadhanula, Alessandro Lazaric, Matteo Pirotta |
| 2022 | AISTATS | Encrypted Linear Contextual Bandit. | Evrard Garcelon, Matteo Pirotta, Vianney Perchet |
| 2022 | AISTATS | Adaptive Multi-Goal Exploration. | Jean Tarbouriech, Omar Darwiche Domingues, Pierre Mnard, Matteo Pirotta, Michal Valko, Alessandro Lazaric |
| 2022 | ALT | Privacy Amplification via Shuffling for Linear Contextual Bandits. | Evrard Garcelon, Kamalika Chaudhuri, Vianney Perchet, Matteo Pirotta |
| 2022 | ICLR | 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 |
| 2021 | AISTATS | A Kernel-Based Approach to Non-Stationary Reinforcement Learning in Metric Spaces. | Omar Darwiche Domingues, Pierre Mnard, Matteo Pirotta, Emilie Kaufmann, Michal Valko |
| 2021 | ALT | Sample Complexity Bounds for Stochastic Shortest Path with a Generative Model. | Jean Tarbouriech, Matteo Pirotta, Michal Valko, Alessandro Lazaric |
| 2021 | ICML | Kernel-Based Reinforcement Learning: A Finite-Time Analysis. | Omar Darwiche Domingues, Pierre Mnard, Matteo Pirotta, Emilie Kaufmann, Michal Valko |
| 2021 | ICML | Leveraging Good Representations in Linear Contextual Bandits. | Matteo Papini, Andrea Tirinzoni, Marcello Restelli, Alessandro Lazaric, Matteo Pirotta |
| 2020 | AAAI | Improved Algorithms for Conservative Exploration in Bandits. | Evrard Garcelon, Mohammad Ghavamzadeh, Alessandro Lazaric, Matteo Pirotta |
| 2020 | AISTATS | Conservative Exploration in Reinforcement Learning. | Evrard Garcelon, Mohammad Ghavamzadeh, Alessandro Lazaric, Matteo Pirotta |
| 2020 | AISTATS | Frequentist Regret Bounds for Randomized Least-Squares Value Iteration. | Andrea Zanette, David Brandfonbrener, Emma Brunskill, Matteo Pirotta, Alessandro Lazaric |
| 2020 | ICML | No-Regret Exploration in Goal-Oriented Reinforcement Learning. | Jean Tarbouriech, Evrard Garcelon, Michal Valko, Matteo Pirotta, Alessandro Lazaric |
| 2020 | UAI | Active Model Estimation in Markov Decision Processes. | Jean Tarbouriech, Shubhanshu Shekhar, Matteo Pirotta, Mohammad Ghavamzadeh, Alessandro Lazaric |
| 2018 | ICML | Efficient Bias-Span-Constrained Exploration-Exploitation in Reinforcement Learning. | Ronan Fruit, Matteo Pirotta, Alessandro Lazaric, Ronald Ortner |
| 2018 | ICML | Stochastic Variance-Reduced Policy Gradient. | Matteo Papini, Damiano Binaghi, Giuseppe Canonaco, Matteo Pirotta, Marcello Restelli |
| 2018 | ICML | Importance Weighted Transfer of Samples in Reinforcement Learning. | Andrea Tirinzoni, Andrea Sessa, Matteo Pirotta, Marcello Restelli |
| 2017 | AAAI | Estimating the Maximum Expected Value in Continuous Reinforcement Learning Problems. | Carlo D'Eramo, Alessandro Nuara, Matteo Pirotta, Marcello Restelli |
| 2017 | ICML | Boosted Fitted Q-Iteration. | Samuele Tosatto, Matteo Pirotta, Carlo D'Eramo, Marcello Restelli |
| 2016 | AAAI | Inverse Reinforcement Learning through Policy Gradient Minimization. | Matteo Pirotta, Marcello Restelli |
| 2015 | AAAI | Multi-Objective Reinforcement Learning with Continuous Pareto Frontier Approximation. | Matteo Pirotta, Simone Parisi, Marcello Restelli |
| 2015 | IJCNN | Following Newton direction in Policy Gradient with parameter exploration. | Giorgio Manganini, Matteo Pirotta, Marcello Restelli, Luca Bascetta |
| 2014 | IJCNN | Policy gradient approaches for multi-objective sequential decision making. | Simone Parisi, Matteo Pirotta, Nicola Smacchia, Luca Bascetta, Marcello Restelli |
| 2013 | ICML | Safe Policy Iteration. | Matteo Pirotta, Marcello Restelli, Alessio Pecorino, Daniele Calandriello |