De Santi, Riccardo

19 publications

ICLR 2026 Landing with the Score: Riemannian Optimization Through Denoising Andrey Kharitenko, Zebang Shen, Riccardo De Santi, Niao He, Florian Dorfler

ICLR 2026 Value Matching: Scalable and Gradient-Free Reward-Guided Flow Adaptation Cristian Perez Jensen, Luca Schaufelberger, Riccardo De Santi, Kjell Jorner, Andreas Krause

ICLR 2026 Verifier-Constrained Flow Expansion for Discovery Beyond the Data Riccardo De Santi, Kimon Protopapas, Ya-Ping Hsieh, Andreas Krause

NeurIPS 2025 Flow Density Control: Generative Optimization Beyond Entropy-Regularized Fine-Tuning Riccardo De Santi, Marin Vlastelica, Ya-Ping Hsieh, Zebang Shen, Niao He, Andreas Krause

ICML 2025 Provable Maximum Entropy Manifold Exploration via Diffusion Models Riccardo De Santi, Marin Vlastelica, Ya-Ping Hsieh, Zebang Shen, Niao He, Andreas Krause

ICLRW 2025 Provable Maximum Entropy Manifold Exploration via Diffusion Models Riccardo De Santi, Marin Vlastelica, Ya-Ping Hsieh, Zebang Shen, Niao He, Andreas Krause

ICLR 2024 Exploiting Causal Graph Priors with Posterior Sampling for Reinforcement Learning Mirco Mutti, Riccardo De Santi, Marcello Restelli, Alexander Marx, Giorgia Ramponi

ICML 2024 Geometric Active Exploration in Markov Decision Processes: The Benefit of Abstraction Riccardo De Santi, Federico Arangath Joseph, Noah Liniger, Mirco Mutti, Andreas Krause

ICML 2024 Global Reinforcement Learning : Beyond Linear and Convex Rewards via Submodular Semi-Gradient Methods Riccardo De Santi, Manish Prajapat, Andreas Krause

JMLR 2023 Convex Reinforcement Learning in Finite Trials Mirco Mutti, Riccardo De Santi, Piersilvio De Bartolomeis, Marcello Restelli

NeurIPSW 2023 Exploiting Causal Representations in Reinforcement Learning: A Posterior Sampling Approach Mirco Mutti, Riccardo De Santi, Marcello Restelli, Alexander Marx, Giorgia Ramponi

AAAI 2023 Provably Efficient Causal Model-Based Reinforcement Learning for Systematic Generalization Mirco Mutti, Riccardo De Santi, Emanuele Rossi, Juan Felipe Calderón, Michael M. Bronstein, Marcello Restelli

NeurIPS 2022 Challenging Common Assumptions in Convex Reinforcement Learning Mirco Mutti, Riccardo De Santi, Piersilvio De Bartolomeis, Marcello Restelli

ICMLW 2022 Invariance Discovery for Systematic Generalization in Reinforcement Learning Mirco Mutti, Riccardo De Santi, Emanuele Rossi, Juan Felipe Calderon, Michael M. Bronstein, Marcello Restelli

ICMLW 2022 Non-Markovian Policies for Unsupervised Reinforcement Learning in Multiple Environments Pietro Maldini, Mirco Mutti, Riccardo De Santi, Marcello Restelli

ICMLW 2022 Non-Markovian Policies for Unsupervised Reinforcement Learning in Multiple Environments Pietro Maldini, Mirco Mutti, Riccardo De Santi, Marcello Restelli

NeurIPSW 2022 Provably Efficient Causal Model-Based Reinforcement Learning for Environment-Agnostic Generalization Mirco Mutti, Riccardo De Santi, Emanuele Rossi, Juan Felipe Calderon, Michael M. Bronstein, Marcello Restelli

ICML 2022 The Importance of Non-Markovianity in Maximum State Entropy Exploration Mirco Mutti, Riccardo De Santi, Marcello Restelli

ICMLW 2021 The Importance of Non-Markovianity in Maximum State Entropy Exploration Mirco Mutti, Riccardo De Santi, Marcello Restelli