{
  "id": "1804.04529",
  "version": "v1",
  "published": "2018-04-12T14:22:35.000Z",
  "updated": "2018-04-12T14:22:35.000Z",
  "title": "Online convex optimization and no-regret learning: Algorithms, guarantees and applications",
  "authors": [
    "E. Veronica Belmega",
    "Panayotis Mertikopoulos",
    "Romain Negrel",
    "Luca Sanguinetti"
  ],
  "comment": "34 pages, 15 figures",
  "categories": [
    "cs.LG",
    "math.OC",
    "stat.ML"
  ],
  "abstract": "Spurred by the enthusiasm surrounding the \"Big Data\" paradigm, the mathematical and algorithmic tools of online optimization have found widespread use in problems where the trade-off between data exploration and exploitation plays a predominant role. This trade-off is of particular importance to several branches and applications of signal processing, such as data mining, statistical inference, multimedia indexing and wireless communications (to name but a few). With this in mind, the aim of this tutorial paper is to provide a gentle introduction to online optimization and learning algorithms that are asymptotically optimal in hindsight - i.e., they approach the performance of a virtual algorithm with unlimited computational power and full knowledge of the future, a property known as no-regret. Particular attention is devoted to identifying the algorithms' theoretical performance guarantees and to establish links with classic optimization paradigms (both static and stochastic). To allow a better understanding of this toolbox, we provide several examples throughout the tutorial ranging from metric learning to wireless resource allocation problems.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2018-04-12T14:22:35.000Z"
    }
  ],
  "analyses": {
    "subjects": [
      "68Q32",
      "90C90",
      "68T05",
      "91A26",
      "94A12"
    ],
    "keywords": [
      "online convex optimization",
      "no-regret learning",
      "applications",
      "guarantees",
      "online optimization"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 34,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}