{
  "id": "1404.5903",
  "version": "v1",
  "published": "2014-04-23T17:25:02.000Z",
  "updated": "2014-04-23T17:25:02.000Z",
  "title": "Most Correlated Arms Identification",
  "authors": [
    "Che-Yu Liu",
    "Sébastien Bubeck"
  ],
  "categories": [
    "stat.ML",
    "cs.LG"
  ],
  "abstract": "We study the problem of finding the most mutually correlated arms among many arms. We show that adaptive arms sampling strategies can have significant advantages over the non-adaptive uniform sampling strategy. Our proposed algorithms rely on a novel correlation estimator. The use of this accurate estimator allows us to get improved results for a wide range of problem instances.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2014-04-23T17:25:02.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "correlated arms identification",
      "novel correlation estimator",
      "wide range",
      "accurate estimator",
      "adaptive arms sampling strategies"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable",
      "adsabs": "2014arXiv1404.5903L"
    }
  }
}