Discovering Many Diverse Solutions with Bayesian Optimization
Natalie Maus, Kaiwen Wu, David Eriksson, Jacob Gardner
Abstract
Bayesian optimization (BO) is a popular approach for 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, for example, a designed molecule may turn out to later violate constraints that can only be evaluated after the optimization process has concluded. To address this issue, we propose rank-ordered Bayesian Optimization with trustregions (ROBOT) which aims to find a portfolio of high-performing solutions that are diverse according to a user-specified diversity measure. 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.
BibTeX
@InProceedings{pmlr-v206-maus23a,
title = {Discovering Many Diverse Solutions with Bayesian Optimization},
author = {Maus, Natalie and Wu, Kaiwen and Eriksson, David and Gardner, Jacob},
booktitle = {Proceedings of The 26th International Conference on Artificial Intelligence and Statistics},
pages = {1779--1798},
year = {2023},
editor = {Ruiz, Francisco and Dy, Jennifer and van de Meent, Jan-Willem},
volume = {206},
series = {Proceedings of Machine Learning Research},
month = {25--27 Apr},
publisher = {PMLR},
pdf = {https://proceedings.mlr.press/v206/maus23a/maus23a.pdf},
url = {https://proceedings.mlr.press/v206/maus23a.html},
abstract = {Bayesian optimization (BO) is a popular approach for 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, for example, a designed molecule may turn out to later violate constraints that can only be evaluated after the optimization process has concluded. To address this issue, we propose rank-ordered Bayesian Optimization with trustregions (ROBOT) which aims to find a portfolio of high-performing solutions that are diverse according to a user-specified diversity measure. 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.}
}