Abstract

Scientists and engineers in diverse domains need to perform expensive experiments to optimize combinatorial spaces, where each candidate input is a discrete structure (e.g., sequence, tree, graph) or a hybrid structure (mixture of discrete and continuous design variables). For example, in drug and vaccine design, we need to search a large space of molecules guided by physical lab experiments. These experiments are often performed in a heuristic manner by humans and without any formal reasoning. Bayesian optimization (BO) is an efficient framework for optimizing expensive black-box functions. However, most of the BO literature is largely focused on optimizing continuous spaces. In this talk, I will discuss the main challenges in extending BO framework to combinatorial structures and some algorithms that I have developed in addressing them.

Notes

• Personal website can be found here.