Essec\Faculty\Model\Contribution {#2216
  #_index: "academ_contributions"
  #_id: "13865"
  #_source: array:26 [
    "id" => "13865"
    "slug" => "estimation-bounds-and-sharp-oracle-inequalities-of-regularized-procedures-with-lipschitz-loss-functions"
    "yearMonth" => "2019-08"
    "year" => "2019"
    "title" => "Estimation bounds and sharp oracle inequalities of regularized procedures with Lipschitz loss functions"
    "description" => "ALQUIER, P., COTTET, V. et LECUE, G. (2019). Estimation bounds and sharp oracle inequalities of regularized procedures with Lipschitz loss functions. <i>Annals of Statistics</i>, 47(4), pp. 2117-2144."
    "authors" => array:3 [
      0 => array:3 [
        "name" => "ALQUIER Pierre"
        "bid" => "B00809923"
        "slug" => "alquier-pierre"
      ]
      1 => array:3 [
        "name" => "LECUE Guillaume"
        "bid" => "B00806953"
        "slug" => "lecue-guillaume"
      ]
      2 => array:1 [
        "name" => "COTTET Vincent"
      ]
    ]
    "ouvrage" => ""
    "keywords" => []
    "updatedAt" => "2024-10-31 13:51:19"
    "publicationUrl" => "https://doi.org/10.1214/18-AOS1742"
    "publicationInfo" => array:3 [
      "pages" => "2117-2144"
      "volume" => "47"
      "number" => "4"
    ]
    "type" => array:2 [
      "fr" => "Articles"
      "en" => "Journal articles"
    ]
    "support_type" => array:2 [
      "fr" => "Revue scientifique"
      "en" => "Scientific journal"
    ]
    "countries" => array:2 [
      "fr" => null
      "en" => null
    ]
    "abstract" => array:2 [
      "fr" => """
        Many classification and regression problems are solved in practice by regularized empirical risk minimizers (RERM). The risk is measured\n
        via a loss function. The quadratic loss function is the most popular function for\n
        regression. It has been extensively studied (cf. [23, 31] among others). Still many other loss functions are popular among practitioners and are indeed extremely useful in specific situations
        """
      "en" => """
        Many classification and regression problems are solved in practice by regularized empirical risk minimizers (RERM). The risk is measured\n
        via a loss function. The quadratic loss function is the most popular function for\n
        regression. It has been extensively studied (cf. [23, 31] among others). Still many other loss functions are popular among practitioners and are indeed extremely useful in specific situations
        """
    ]
    "authors_fields" => array:2 [
      "fr" => "Systèmes d'Information, Data Analytics et Opérations"
      "en" => "Information Systems, Data Analytics and Operations"
    ]
    "indexedAt" => "2024-11-21T09:21:53.000Z"
    "docTitle" => "Estimation bounds and sharp oracle inequalities of regularized procedures with Lipschitz loss functions"
    "docSurtitle" => "Journal articles"
    "authorNames" => "<a href="/cv/alquier-pierre">ALQUIER Pierre</a>, <a href="/cv/lecue-guillaume">LECUE Guillaume</a>, COTTET Vincent"
    "docDescription" => "<span class="document-property-authors">ALQUIER Pierre, LECUE Guillaume, COTTET Vincent</span><br><span class="document-property-authors_fields">Information Systems, Data Analytics and Operations</span> | <span class="document-property-year">2019</span>"
    "keywordList" => ""
    "docPreview" => "<b>Estimation bounds and sharp oracle inequalities of regularized procedures with Lipschitz loss functions</b><br><span>2019-08 | Journal articles </span>"
    "docType" => "research"
    "publicationLink" => "<a href="https://doi.org/10.1214/18-AOS1742" target="_blank">Estimation bounds and sharp oracle inequalities of regularized procedures with Lipschitz loss functions</a>"
  ]
  +lang: "en"
  +"_type": "_doc"
  +"_score": 8.554104
  +"parent": null
}