Essec\Faculty\Model\Contribution {#2233
  #_index: "academ_contributions"
  #_id: "13930"
  #_source: array:26 [
    "id" => "13930"
    "slug" => "a-mom-based-ensemble-method-for-robustness-subsampling-and-hyperparameter-tuning"
    "yearMonth" => "2021-03"
    "year" => "2021"
    "title" => "A MOM-based ensemble method for robustness, subsampling and hyperparameter tuning"
    "description" => "KWON, J., LECUE, G. et LERASLE, M. (2021). A MOM-based ensemble method for robustness, subsampling and hyperparameter tuning. <i>The Electronic Journal of Statistics</i>, 15(1), pp. 1202-1207."
    "authors" => array:3 [
      0 => array:3 [
        "name" => "LECUE Guillaume"
        "bid" => "B00806953"
        "slug" => "lecue-guillaume"
      ]
      1 => array:1 [
        "name" => "KWON Joon"
      ]
      2 => array:1 [
        "name" => "LERASLE Matthieu"
      ]
    ]
    "ouvrage" => ""
    "keywords" => array:2 [
      0 => "heavy-tailed"
      1 => "robustness"
    ]
    "updatedAt" => "2024-10-31 13:51:19"
    "publicationUrl" => "https://doi.org/10.1214/21-EJS1814"
    "publicationInfo" => array:3 [
      "pages" => "1202-1207"
      "volume" => "15"
      "number" => "1"
    ]
    "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" => "Hyperparameter tuning and model selection are important steps in machine learning. Unfortunately, classical hyperparameter calibration and model selection procedures are sensitive to outliers and heavy-tailed data. In this work, we construct a selection procedure which can be seen as a robust alternative to cross-validation and is based on a median-of-means principle. Using this procedure, we also build an ensemble method which, trained with algorithms and corrupted heavy-tailed data, selects an algorithm, trains it with a large uncorrupted subsample and automatically tunes its hyperparameters. In particular, the approach can transform any procedure into a robust to outliers and to heavy-tailed data procedure while tuning automatically its hyperparameters."
      "en" => "Hyperparameter tuning and model selection are important steps in machine learning. Unfortunately, classical hyperparameter calibration and model selection procedures are sensitive to outliers and heavy-tailed data. In this work, we construct a selection procedure which can be seen as a robust alternative to cross-validation and is based on a median-of-means principle. Using this procedure, we also build an ensemble method which, trained with algorithms and corrupted heavy-tailed data, selects an algorithm, trains it with a large uncorrupted subsample and automatically tunes its hyperparameters. In particular, the approach can transform any procedure into a robust to outliers and to heavy-tailed data procedure while tuning automatically its hyperparameters."
    ]
    "authors_fields" => array:2 [
      "fr" => "Systèmes d'Information, Data Analytics et Opérations"
      "en" => "Information Systems, Data Analytics and Operations"
    ]
    "indexedAt" => "2024-12-22T08:21:46.000Z"
    "docTitle" => "A MOM-based ensemble method for robustness, subsampling and hyperparameter tuning"
    "docSurtitle" => "Articles"
    "authorNames" => "<a href="/cv/lecue-guillaume">LECUE Guillaume</a>, KWON Joon, LERASLE Matthieu"
    "docDescription" => "<span class="document-property-authors">LECUE Guillaume, KWON Joon, LERASLE Matthieu</span><br><span class="document-property-authors_fields">Systèmes d'Information, Data Analytics et Opérations</span> | <span class="document-property-year">2021</span>"
    "keywordList" => "<a href="#">heavy-tailed</a>, <a href="#">robustness</a>"
    "docPreview" => "<b>A MOM-based ensemble method for robustness, subsampling and hyperparameter tuning</b><br><span>2021-03 | Articles </span>"
    "docType" => "research"
    "publicationLink" => "<a href="https://doi.org/10.1214/21-EJS1814" target="_blank">A MOM-based ensemble method for robustness, subsampling and hyperparameter tuning</a>"
  ]
  +lang: "fr"
  +"_type": "_doc"
  +"_score": 8.831362
  +"parent": null
}