5 papers
Multi-Objective Generative Optimization via Expected Hypervolume Fine-Tuning
Juan Guevara, Riccardo De Santi, Andreas Krause +1
Scientific discovery requires plausible designs spanning useful trade-offs among competing properties: a molecule with strong binding affinity, for example, may be difficult to syn…
LLM-to-Flow Distillation: Teaching Natural Language Goals to Scientific Generators
Riccardo De Santi, Federico Di Gennaro, Angelo Gnazzo +6
Generative discovery in science requires coupling high-level scientific reasoning with the ability to generate samples respecting the structure of a design space. Yet these capabil…
Constrained Flow Optimization via Sequential Fine Tuning for Molecular Design
Sven Gutjahr, Riccardo De Santi, Luca Schaufelberger +2
Adapting generative foundation models, in particular diffusion and flow models, to optimize given reward functions (e.g., binding affinity) while satisfying constraints (e.g., mole…
Geometric Active Exploration in Markov Decision Processes: the Benefit of Abstraction
Riccardo De Santi, Federico Arangath Joseph, Noah Liniger +2
How can a scientist use a Reinforcement Learning (RL) algorithm to design experiments over a dynamical system's state space? In the case of finite and Markovian systems, an area ca…
Global Reinforcement Learning: Beyond Linear and Convex Rewards via Submodular Semi-gradient Methods
Riccardo De Santi, Manish Prajapat, Andreas Krause
In classic Reinforcement Learning (RL), the agent maximizes an additive objective of the visited states, e.g., a value function. Unfortunately, objectives of this type cannot model…