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Discovering Many Diverse Solutions with Bayesian Optimization

Natalie Maus · Kaiwen Wu · David Eriksson · Jacob Gardner

Auditorium 1 Foyer 136


Bayesian optimization (BO) is a popular approach to sample-efficient optimization of black-box objective functions. While BO has been successfully applied to a wide range of scientific applications, traditional approaches to single-objective BO only seek to find a single best solution. This can be a significant limitation in situations where solutions may later turn out to be intractable, e.g., a designed molecule may turn out to later violate constraints that can only be evaluated after the optimization process has concluded. To combat this issue, we propose Rank-Ordered Bayesian Optimization with Trust-regions (ROBOT) which aims to find a portfolio of high-performing solutions that are diverse according to a user-specified diversity metric. We evaluate ROBOT on several real-world applications and show that it can discover large sets of high-performing diverse solutions while requiring few additional function evaluations compared to finding a single best solution.

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