{
  "id": "2011.01217",
  "version": "v1",
  "published": "2020-10-31T00:15:39.000Z",
  "updated": "2020-10-31T00:15:39.000Z",
  "title": "Prediction against limited adversary",
  "authors": [
    "Erhan Bayraktar",
    "Ibrahim Ekren",
    "Xin Zhang"
  ],
  "categories": [
    "cs.LG",
    "math.PR"
  ],
  "abstract": "We study the problem of prediction with expert advice with adversarial corruption where the adversary can at most corrupt one expert. Using tools from viscosity theory, we characterize the long-time behavior of the value function of the game between the forecaster and the adversary. We provide lower and upper bounds for the growth rate of regret without relying on a comparison result. We show that depending on the description of regret, the limiting behavior of the game can significantly differ.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2020-10-31T00:15:39.000Z"
    }
  ],
  "analyses": {
    "subjects": [
      "68T05",
      "35K55",
      "35K65",
      "35Q91"
    ],
    "keywords": [
      "limited adversary",
      "prediction",
      "expert advice",
      "adversarial corruption",
      "viscosity theory"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}