{
  "id": "1704.02718",
  "version": "v1",
  "published": "2017-04-10T06:04:34.000Z",
  "updated": "2017-04-10T06:04:34.000Z",
  "title": "Distributed Learning for Cooperative Inference",
  "authors": [
    "Angelia Nedić",
    "Alex Olshevsky",
    "César A. Uribe"
  ],
  "categories": [
    "math.OC",
    "cs.LG",
    "cs.MA",
    "math.PR",
    "stat.ML"
  ],
  "abstract": "We study the problem of cooperative inference where a group of agents interact over a network and seek to estimate a joint parameter that best explains a set of observations. Agents do not know the network topology or the observations of other agents. We explore a variational interpretation of the Bayesian posterior density, and its relation to the stochastic mirror descent algorithm, to propose a new distributed learning algorithm. We show that, under appropriate assumptions, the beliefs generated by the proposed algorithm concentrate around the true parameter exponentially fast. We provide explicit non-asymptotic bounds for the convergence rate. Moreover, we develop explicit and computationally efficient algorithms for observation models belonging to exponential families.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2017-04-10T06:04:34.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "cooperative inference",
      "distributed learning",
      "stochastic mirror descent algorithm",
      "explicit non-asymptotic bounds",
      "bayesian posterior density"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}