Essec\Faculty\Model\Contribution {#2216
  #_index: "academ_contributions"
  #_id: "13929"
  #_source: array:26 [
    "id" => "13929"
    "slug" => "regularization-and-the-small-ball-method-i-sparse-recovery"
    "yearMonth" => "2018-04"
    "year" => "2018"
    "title" => "Regularization and the small-ball method I: Sparse recovery"
    "description" => "LECUE, G. et MENDELSON, S. (2018). Regularization and the small-ball method I: Sparse recovery. <i>Annals of Statistics</i>, 46(2), pp. 611-641."
    "authors" => array:2 [
      0 => array:3 [
        "name" => "LECUE Guillaume"
        "bid" => "B00806953"
        "slug" => "lecue-guillaume"
      ]
      1 => array:1 [
        "name" => "MENDELSON Shahar"
      ]
    ]
    "ouvrage" => ""
    "keywords" => []
    "updatedAt" => "2023-03-28 10:48:21"
    "publicationUrl" => "https://projecteuclid.org/journals/annals-of-statistics/volume-46/issue-2/Regularization-and-the-small-ball-method-I-Sparse-recovery/10.1214/17-AOS1562.full"
    "publicationInfo" => array:3 [
      "pages" => "611-641"
      "volume" => "46"
      "number" => "2"
    ]
    "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" => """
        Our approach gives a common framework that may be used in the analysis of learning problems and regularization problems alike. In particular, it sheds some light on the role various notions of sparsity have in regularization and on their connection with the size of subdifferentials of \n
        Ψ\n
         in a neighborhood of the true minimizer.\n
        As “proof of concept” we extend the known estimates for the LASSO, SLOPE and trace norm regularization.
        """
      "en" => """
        Our approach gives a common framework that may be used in the analysis of learning problems and regularization problems alike. In particular, it sheds some light on the role various notions of sparsity have in regularization and on their connection with the size of subdifferentials of \n
        Ψ\n
         in a neighborhood of the true minimizer.\n
        As “proof of concept” we extend the known estimates for the LASSO, SLOPE and trace norm regularization.
        """
    ]
    "authors_fields" => array:2 [
      "fr" => "Systèmes d'Information, Data Analytics et Opérations"
      "en" => "Information Systems, Data Analytics and Operations"
    ]
    "indexedAt" => "2024-12-31T01:21:44.000Z"
    "docTitle" => "Regularization and the small-ball method I: Sparse recovery"
    "docSurtitle" => "Journal articles"
    "authorNames" => "<a href="/cv/lecue-guillaume">LECUE Guillaume</a>, MENDELSON Shahar"
    "docDescription" => "<span class="document-property-authors">LECUE Guillaume, MENDELSON Shahar</span><br><span class="document-property-authors_fields">Information Systems, Data Analytics and Operations</span> | <span class="document-property-year">2018</span>"
    "keywordList" => ""
    "docPreview" => "<b>Regularization and the small-ball method I: Sparse recovery</b><br><span>2018-04 | Journal articles </span>"
    "docType" => "research"
    "publicationLink" => "<a href="https://projecteuclid.org/journals/annals-of-statistics/volume-46/issue-2/Regularization-and-the-small-ball-method-I-Sparse-recovery/10.1214/17-AOS1562.full" target="_blank">Regularization and the small-ball method I: Sparse recovery</a>"
  ]
  +lang: "en"
  +"_type": "_doc"
  +"_score": 8.709729
  +"parent": null
}