Bayesian Optimization for High-Dimensional and Structured Problems

Oregon Convention Center 

Bayesian optimization is a framework that leverages the ability of Gaussian processes to quantity uncertainty in order to efficiently solve black-box optimization problems. For many years, much work in this area has focused on relatively low dimensional continuous optimization problems where the objective function is highly expensive to evaluate, and is limited to a few hundred evaluations at most. In this talk, I'll discuss the application of Bayesian optimization to radically different optimization problems over challenging inputs like molecules, proteins, and database query plans. In these settings, practitioners may have access to vast libraries of known results, and the objective functions are structured, discrete and high dimensional. By uniting recent work on deep generative modelling, scalable Gaussian processes, and high dimensional black-box optimization, we are able to achieve up to a 20x performance improvement over state of the art on several of the most popular benchmarks for molecule design, and 5x improvements in query execution time over the built in PostgreSQL query optimizer.