{
  "id": "2005.07972",
  "version": "v1",
  "published": "2020-05-16T12:38:19.000Z",
  "updated": "2020-05-16T12:38:19.000Z",
  "title": "Conformal Prediction: a Unified Review of Theory and New Challenges",
  "authors": [
    "Gianluca Zeni",
    "Matteo Fontana",
    "Simone Vantini"
  ],
  "categories": [
    "cs.LG",
    "econ.EM",
    "stat.ME",
    "stat.ML"
  ],
  "abstract": "In this work we provide a review of basic ideas and novel developments about Conformal Prediction -- an innovative distribution-free, non-parametric forecasting method, based on minimal assumptions -- that is able to yield in a very straightforward way predictions sets that are valid in a statistical sense also in in the finite sample case. The in-depth discussion provided in the paper covers the theoretical underpinnings of Conformal Prediction, and then proceeds to list the more advanced developments and adaptations of the original idea.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2020-05-16T12:38:19.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "conformal prediction",
      "unified review",
      "challenges",
      "finite sample case",
      "straightforward way predictions sets"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}