3 papers
cs.LG2026
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…
cs.LG2026
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…
cs.LG2024
Policy Mirror Descent with Lookahead
Kimon Protopapas, Anas Barakat
Policy Mirror Descent (PMD) stands as a versatile algorithmic framework encompassing several seminal policy gradient algorithms such as natural policy gradient, with connections wi…