Essec\Faculty\Model\Contribution {#2188
  #_index: "academ_contributions"
  #_id: "14316"
  #_source: array:26 [
    "id" => "14316"
    "slug" => "infinite-dimensional-gradient-based-descent-for-alpha-divergence-minimisation"
    "yearMonth" => "2021-08"
    "year" => "2021"
    "title" => "Infinite-dimensional gradient-based descent for alpha-divergence minimisation"
    "description" => "DAUDEL, K., DOUC, R. et PORTIER, F. (2021). Infinite-dimensional gradient-based descent for alpha-divergence minimisation. <i>Annals of Statistics</i>, 49(4), pp. 2250 - 2270."
    "authors" => array:3 [
      0 => array:3 [
        "name" => "DAUDEL Kamélia"
        "bid" => "B00812202"
        "slug" => "daudel-kamelia"
      ]
      1 => array:1 [
        "name" => "DOUC Randal"
      ]
      2 => array:1 [
        "name" => "PORTIER François"
      ]
    ]
    "ouvrage" => ""
    "keywords" => []
    "updatedAt" => "2023-09-12 15:34:27"
    "publicationUrl" => "https://projecteuclid.org/journals/annals-of-statistics/volume-49/issue-4/Infinite-dimensional-gradient-based-descent-for-alpha-divergence-minimisation/10.1214/20-AOS2035.short"
    "publicationInfo" => array:3 [
      "pages" => "2250 - 2270"
      "volume" => "49"
      "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" => "We demonstrate empirically on both toy and real-world examples the benefit of using the Power Descent and going beyond the Entropic Mirror Descent framework, which fails as the dimension grows."
      "en" => "We demonstrate empirically on both toy and real-world examples the benefit of using the Power Descent and going beyond the Entropic Mirror Descent framework, which fails as the dimension grows."
    ]
    "authors_fields" => array:2 [
      "fr" => "Systèmes d’Information, Sciences de la Décision et Statistiques"
      "en" => "Information Systems, Decision Sciences and Statistics"
    ]
    "indexedAt" => "2024-05-19T03:21:48.000Z"
    "docTitle" => "Infinite-dimensional gradient-based descent for alpha-divergence minimisation"
    "docSurtitle" => "Journal articles"
    "authorNames" => "<a href="/cv/daudel-kamelia">DAUDEL Kamélia</a>, DOUC Randal, PORTIER François"
    "docDescription" => "<span class="document-property-authors">DAUDEL Kamélia, DOUC Randal, PORTIER François</span><br><span class="document-property-authors_fields">Information Systems, Decision Sciences and Statistics</span> | <span class="document-property-year">2021</span>"
    "keywordList" => ""
    "docPreview" => "<b>Infinite-dimensional gradient-based descent for alpha-divergence minimisation</b><br><span>2021-08 | Journal articles </span>"
    "docType" => "research"
    "publicationLink" => "<a href="https://projecteuclid.org/journals/annals-of-statistics/volume-49/issue-4/Infinite-dimensional-gradient-based-descent-for-alpha-divergence-minimisation/10.1214/20-AOS2035.short" target="_blank">Infinite-dimensional gradient-based descent for alpha-divergence minimisation</a>"
  ]
  +lang: "en"
  +"_type": "_doc"
  +"_score": 8.789355
  +"parent": null
}