{
  "id": "1601.04011",
  "version": "v1",
  "published": "2016-01-15T17:32:44.000Z",
  "updated": "2016-01-15T17:32:44.000Z",
  "title": "Tightening the Sample Complexity of Empirical Risk Minimization via Preconditioned Stability",
  "authors": [
    "Alon Gonen",
    "Shai Shalev-Shwartz"
  ],
  "categories": [
    "cs.LG"
  ],
  "abstract": "We tighten the sample complexity of empirical risk minimization (ERM) associated with a class of generalized linear models that include linear and logistic regression. In particular, we conclude that ERM attains the optimal sample complexity for linear regression. Our analysis relies on a new notion of stability, called preconditioned stability, which may be of independent interest.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2016-01-15T17:32:44.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "empirical risk minimization",
      "preconditioned stability",
      "optimal sample complexity",
      "independent interest",
      "logistic regression"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable",
      "adsabs": "2016arXiv160104011G"
    }
  }
}