{
  "id": "2006.10940",
  "version": "v1",
  "published": "2020-06-19T03:00:01.000Z",
  "updated": "2020-06-19T03:00:01.000Z",
  "title": "Open Problem: Model Selection for Contextual Bandits",
  "authors": [
    "Dylan J. Foster",
    "Akshay Krishnamurthy",
    "Haipeng Luo"
  ],
  "comment": "COLT 2020 open problem",
  "categories": [
    "cs.LG",
    "stat.ML"
  ],
  "abstract": "In statistical learning, algorithms for model selection allow the learner to adapt to the complexity of the best hypothesis class in a sequence. We ask whether similar guarantees are possible for contextual bandit learning.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2020-06-19T03:00:01.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "model selection",
      "open problem",
      "best hypothesis class",
      "similar guarantees",
      "complexity"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}