{
  "id": "2103.09434",
  "version": "v1",
  "published": "2021-03-17T04:35:09.000Z",
  "updated": "2021-03-17T04:35:09.000Z",
  "title": "Efficient Bayesian Optimization using Multiscale Graph Correlation",
  "authors": [
    "Takuya Kanazawa"
  ],
  "comment": "12 pages, 2 figures",
  "categories": [
    "cs.LG"
  ],
  "abstract": "Bayesian optimization is a powerful tool to optimize a black-box function, the evaluation of which is time-consuming or costly. In this paper, we propose a new approach to Bayesian optimization called GP-MGC, which maximizes multiscale graph correlation with respect to the global maximum to determine the next query point. We present our evaluation of GP-MGC in applications involving both synthetic benchmark functions and real-world datasets and demonstrate that GP-MGC performs as well as or even better than state-of-the-art methods such as max-value entropy search and GP-UCB.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2021-03-17T04:35:09.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "efficient bayesian optimization",
      "maximizes multiscale graph correlation",
      "synthetic benchmark functions",
      "max-value entropy search",
      "evaluation"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 12,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}