{
  "id": "2210.10953",
  "version": "v1",
  "published": "2022-10-20T01:56:38.000Z",
  "updated": "2022-10-20T01:56:38.000Z",
  "title": "Discovering Many Diverse Solutions with Bayesian Optimization",
  "authors": [
    "Natalie Maus",
    "Kaiwen Wu",
    "David Eriksson",
    "Jacob Gardner"
  ],
  "categories": [
    "cs.LG",
    "cs.AI"
  ],
  "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.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2022-10-20T01:56:38.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "bayesian optimization",
      "diverse solutions",
      "single best solution",
      "additional function evaluations",
      "scientific applications"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}