| 2025 | ICLR | A Theoretical Framework for Partially-Observed Reward States in RLHF. | Chinmaya Kausik, Mirco Mutti, Aldo Pacchiano, Ambuj Tewari |
| 2025 | ICML | A Classification View on Meta Learning Bandits. | Mirco Mutti, Jeongyeol Kwon, Shie Mannor, Aviv Tamar |
| 2025 | ICML | Enhancing Diversity In Parallel Agents: A Maximum State Entropy Exploration Story. | Vincenzo De Paola, Riccardo Zamboni, Mirco Mutti, Marcello Restelli |
| 2024 | ICLR | Exploiting Causal Graph Priors with Posterior Sampling for Reinforcement Learning. | Mirco Mutti, Riccardo De Santi, Marcello Restelli, Alexander Marx, Giorgia Ramponi |
| 2024 | ICML | Offline Inverse RL: New Solution Concepts and Provably Efficient Algorithms. | Filippo Lazzati, Mirco Mutti, Alberto Maria Metelli |
| 2024 | ICML | Test-Time Regret Minimization in Meta Reinforcement Learning. | Mirco Mutti, Aviv Tamar |
| 2024 | ICML | Geometric Active Exploration in Markov Decision Processes: the Benefit of Abstraction. | Riccardo De Santi, Federico Arangath Joseph, Noah Liniger, Mirco Mutti, Andreas Krause |
| 2024 | ICML | How to Explore with Belief: State Entropy Maximization in POMDPs. | Riccardo Zamboni, Duilio Cirino, Marcello Restelli, Mirco Mutti |
| 2023 | AAAI | Provably Efficient Causal Model-Based Reinforcement Learning for Systematic Generalization. | Mirco Mutti, Riccardo De Santi, Emanuele Rossi, Juan Felipe Caldern, Michael M. Bronstein, Marcello Restelli |
| 2023 | AISTATS | A Tale of Sampling and Estimation in Discounted Reinforcement Learning. | Alberto Maria Metelli, Mirco Mutti, Marcello Restelli |
| 2022 | AAAI | Unsupervised Reinforcement Learning in Multiple Environments. | Mirco Mutti, Mattia Mancassola, Marcello Restelli |
| 2022 | AISTATS | Reward-Free Policy Space Compression for Reinforcement Learning. | Mirco Mutti, Stefano Del Col, Marcello Restelli |
| 2022 | ICML | The Importance of Non-Markovianity in Maximum State Entropy Exploration. | Mirco Mutti, Riccardo De Santi, Marcello Restelli |
| 2021 | AAAI | Task-Agnostic Exploration via Policy Gradient of a Non-Parametric State Entropy Estimate. | Mirco Mutti, Lorenzo Pratissoli, Marcello Restelli |
| 2020 | AAAI | An Intrinsically-Motivated Approach for Learning Highly Exploring and Fast Mixing Policies. | Mirco Mutti, Marcello Restelli |
| 2018 | ICML | Configurable Markov Decision Processes. | Alberto Maria Metelli, Mirco Mutti, Marcello Restelli |