Riccardo De Santi

21 papers A* 7Journal 14

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	A Unified Density Operator View of Flow Control and Merging. CoRR Riccardo De Santi, Malte Franke, Ya-Ping Hsieh, Andreas Krause
2026	J	`jnl`	Efficient Tail-Aware Generative Optimization via Flow Model Fine-Tuning. CoRR Zifan Wang, Riccardo De Santi, Xiaoyu Mo, Michael M. Zavlanos, Andreas Krause, Karl Henrik Johansson
2026	J	`jnl`	Verifier-Constrained Flow Expansion for Discovery Beyond the Data. CoRR Riccardo De Santi, Kimon Protopapas, Ya-Ping Hsieh, Andreas Krause
2025	J	`jnl`	Efficient Personalization of Generative Models via Optimal Experimental Design. CoRR Guy Schacht, Ziyad Sheebaelhamd, Riccardo De Santi, Mojmír Mutný, Andreas Krause
2025	J	`jnl`	Flow Density Control: Generative Optimization Beyond Entropy-Regularized Fine-Tuning. CoRR Riccardo De Santi, Marin Vlastelica, Ya-Ping Hsieh, Zebang Shen, Niao He, Andreas Krause
2025	J	`jnl`	Landing with the Score: Riemannian Optimization through Denoising. CoRR Andrey Kharitenko, Zebang Shen, Riccardo De Santi, Niao He, Florian Dörfler
2025	A*	`conf`	Provable Maximum Entropy Manifold Exploration via Diffusion Models. ICML Riccardo De Santi, Marin Vlastelica, Ya-Ping Hsieh, Zebang Shen, Niao He, Andreas Krause
2025	J	`jnl`	Provable Maximum Entropy Manifold Exploration via Diffusion Models. CoRR Riccardo De Santi, Marin Vlastelica, Ya-Ping Hsieh, Zebang Shen, Niao He, Andreas Krause
2024	A*	`conf`	Exploiting Causal Graph Priors with Posterior Sampling for Reinforcement Learning. ICLR Mirco Mutti, Riccardo De Santi, Marcello Restelli, Alexander Marx, Giorgia Ramponi
2024	A*	`conf`	Geometric Active Exploration in Markov Decision Processes: the Benefit of Abstraction. ICML Riccardo De Santi, Federico Arangath Joseph, Noah Liniger, Mirco Mutti, Andreas Krause
2024	J	`jnl`	Geometric Active Exploration in Markov Decision Processes: the Benefit of Abstraction. CoRR Riccardo De Santi, Federico Arangath Joseph, Noah Liniger, Mirco Mutti, Andreas Krause
2024	A*	`conf`	Global Reinforcement Learning : Beyond Linear and Convex Rewards via Submodular Semi-gradient Methods. ICML Riccardo De Santi, Manish Prajapat, Andreas Krause
2024	J	`jnl`	Global Reinforcement Learning: Beyond Linear and Convex Rewards via Submodular Semi-gradient Methods. CoRR Riccardo De Santi, Manish Prajapat, Andreas Krause
2023	J	`jnl`	Convex Reinforcement Learning in Finite Trials. J. Mach. Learn. Res. Mirco Mutti, Riccardo De Santi, Piersilvio De Bartolomeis, Marcello Restelli
2023	J	`jnl`	Exploiting Causal Graph Priors with Posterior Sampling for Reinforcement Learning. CoRR Mirco Mutti, Riccardo De Santi, Marcello Restelli, Alexander Marx, Giorgia Ramponi
2023	A*	`conf`	Provably Efficient Causal Model-Based Reinforcement Learning for Systematic Generalization. AAAI Mirco Mutti, Riccardo De Santi, Emanuele Rossi, Juan Felipe Calderón, Michael M. Bronstein, Marcello Restelli
2022	A*	`conf`	Challenging Common Assumptions in Convex Reinforcement Learning. NeurIPS Mirco Mutti, Riccardo De Santi, Piersilvio De Bartolomeis, Marcello Restelli
2022	J	`jnl`	Challenging Common Assumptions in Convex Reinforcement Learning. CoRR Mirco Mutti, Riccardo De Santi, Piersilvio De Bartolomeis, Marcello Restelli
2022	J	`jnl`	Provably Efficient Causal Model-Based Reinforcement Learning for Systematic Generalization. CoRR Mirco Mutti, Riccardo De Santi, Emanuele Rossi, Juan Felipe Calderón, Michael M. Bronstein, Marcello Restelli
2022	A*	`conf`	The Importance of Non-Markovianity in Maximum State Entropy Exploration. ICML Mirco Mutti, Riccardo De Santi, Marcello Restelli
2022	J	`jnl`	The Importance of Non-Markovianity in Maximum State Entropy Exploration. CoRR Mirco Mutti, Riccardo De Santi, Marcello Restelli