{
  "id": "2210.01628",
  "version": "v1",
  "published": "2022-10-04T14:16:41.000Z",
  "updated": "2022-10-04T14:16:41.000Z",
  "title": "Monte Carlo Tree Search based Variable Selection for High Dimensional Bayesian Optimization",
  "authors": [
    "Lei Song",
    "Ke Xue",
    "Xiaobin Huang",
    "Chao Qian"
  ],
  "comment": "NeurIPS 2022 accept",
  "categories": [
    "cs.LG",
    "cs.AI"
  ],
  "abstract": "Bayesian optimization (BO) is a class of popular methods for expensive black-box optimization, and has been widely applied to many scenarios. However, BO suffers from the curse of dimensionality, and scaling it to high-dimensional problems is still a challenge. In this paper, we propose a variable selection method MCTS-VS based on Monte Carlo tree search (MCTS), to iteratively select and optimize a subset of variables. That is, MCTS-VS constructs a low-dimensional subspace via MCTS and optimizes in the subspace with any BO algorithm. We give a theoretical analysis of the general variable selection method to reveal how it can work. Experiments on high-dimensional synthetic functions and real-world problems (i.e., NAS-bench problems and MuJoCo locomotion tasks) show that MCTS-VS equipped with a proper BO optimizer can achieve state-of-the-art performance.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2022-10-04T14:16:41.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "monte carlo tree search",
      "high dimensional bayesian optimization",
      "variable selection method"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}