2 papers
cs.LG2026
Leveraging Discrete Function Decomposability for Scientific Design
James C. Bowden, Sergey Levine, Jennifer Listgarten
In the era of AI-driven science and engineering, we often want to design discrete objects in silico according to user-specified properties. For example, we may wish to design a pro…
cs.LG2025
Conformal Prediction Under Feedback Covariate Shift for Biomolecular Design
Clara Fannjiang, Stephen Bates, Anastasios N. Angelopoulos +2
Many applications of machine learning methods involve an iterative protocol in which data are collected, a model is trained, and then outputs of that model are used to choose what…