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arXiv:2210.10953 (cs)
[Submitted on 20 Oct 2022 (v1), last revised 2 May 2023 (this version, v4)]

Title:Discovering Many Diverse Solutions with Bayesian Optimization

Authors:Natalie Maus, Kaiwen Wu, David Eriksson, Jacob Gardner
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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 violate constraints that can only be reasonably evaluated after the optimization process has concluded. To address 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.
Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
Cite as: arXiv:2210.10953 [cs.LG]
  (or arXiv:2210.10953v4 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.2210.10953

Submission history

From: Natalie Maus [view email]
[v1] Thu, 20 Oct 2022 01:56:38 UTC (13,414 KB)
[v2] Fri, 21 Oct 2022 13:15:41 UTC (13,414 KB)
[v3] Fri, 10 Mar 2023 19:45:49 UTC (13,469 KB)
[v4] Tue, 2 May 2023 21:49:15 UTC (13,470 KB)
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