3 accepted papers
In the era of AI-driven science and engineering, we often want to design discrete objects (e.g., circuits, proteins, materials) in silico according to user-specified properties (e.g., that a protein binds its target). Given a property predictive model, in silico design typically involves training a…
Integrals with discontinuous integrands are ubiquitous, arising from discrete structure in applications like topology optimization, graphics, and computational geometry. These integrals are often part of a forward model in an inverse problem where it is necessary to reason backwards about the pa…
We study Bayesian optimization (BO) in high-dimensional and non-stationary scenarios. Existing algorithms for such scenarios typically require extensive hyperparameter tuning, which limits their practical effectiveness. We propose a framework, called BALLET, which adaptively filters for a high-confi…