| 2025 | ICML | Provable Maximum Entropy Manifold Exploration via Diffusion Models. | Riccardo De Santi, Marin Vlastelica, Ya-Ping Hsieh, Zebang Shen, Niao He, Andreas Krause |
| 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 | 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 | Global Reinforcement Learning : Beyond Linear and Convex Rewards via Submodular Semi-gradient Methods. | Riccardo De Santi, Manish Prajapat, Andreas Krause |
| 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 |
| 2022 | ICML | The Importance of Non-Markovianity in Maximum State Entropy Exploration. | Mirco Mutti, Riccardo De Santi, Marcello Restelli |