Essec\Faculty\Model\Contribution {#2216
#_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" => "2024-11-24T01:21:47.000Z"
"docTitle" => "A MOM-based ensemble method for robustness, subsampling and hyperparameter tuning"
"docSurtitle" => "Journal 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">Information Systems, Data Analytics and Operations</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 | Journal 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: "en"
+"_type": "_doc"
+"_score": 8.757847
+"parent": null
}
