11 papers
On the Design Space of Discrete Diffusion Online Adaptation for Molecular Optimization
Trevor Chen, Ariel Dai, Jason Yang +8
Molecular optimization often starts from a pretrained generative model that captures a broad prior over valid molecular structures. At test time, however, the goal is not to sample…
Active Flow Expansion for Out-of-Distribution Discovery: from Theory to Molecules
Riccardo De Santi, Bruce Lee, Cristian Perez Jensen +6
Standard flow and diffusion pre-training matches the distribution of available data (e.g., molecules), which often covers only a small fraction of the valid design space. In genera…
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…
Landing with the Score: Riemannian Optimization through Denoising
Andrey Kharitenko, Zebang Shen, Riccardo de Santi +2
Under the data manifold hypothesis, high-dimensional data are concentrated near a low-dimensional manifold. We study the problem of Riemannian optimization over such manifolds when…
Efficient Tail-Aware Generative Optimization via Flow Model Fine-Tuning
Zifan Wang, Riccardo De Santi, Xiaoyu Mo +3
Fine-tuning pre-trained diffusion and flow models to optimize downstream utilities is central to real-world deployment. Existing entropy-regularized methods primarily maximize expe…
Verifier-Constrained Flow Expansion for Discovery Beyond the Data
Riccardo De Santi, Kimon Protopapas, Ya-Ping Hsieh +1
Flow and diffusion models are typically pre-trained on limited available data (e.g., molecular samples), covering only a fraction of the valid design space (e.g., the full molecula…