Essec\Faculty\Model\Contribution {#2233 ▼
#_index: "academ_contributions"
#_id: "14980"
#_source: array:26 [
"id" => "14980"
"slug" => "14980-pac-bayes-bounds-understanding-the-generalization-of-bayesian-learning-algorithms"
"yearMonth" => "2024-10"
"year" => "2024"
"title" => "PAC-Bayes bounds: understanding the generalization of Bayesian learning algorithms."
"description" => "ALQUIER, P. (2024). PAC-Bayes bounds: understanding the generalization of Bayesian learning algorithms. Dans: Stochastics Seminar, Department of Mathematics, NUS. Singapore. 
ALQUIER, P. (2024). PAC-Bayes bounds: understanding the generalization of Bayesian learning algorith "
"authors" => array:1 [
0 => array:3 [
"name" => "ALQUIER Pierre"
"bid" => "B00809923"
"slug" => "alquier-pierre"
]
]
"ouvrage" => "Stochastics Seminar, Department of Mathematics, NUS"
"keywords" => array:1 [
0 => "Machine learning, neural networks, information theory."
]
"updatedAt" => "2024-10-14 10:15:49"
"publicationUrl" => null
"publicationInfo" => array:3 [
"pages" => ""
"volume" => ""
"number" => ""
]
"type" => array:2 [
"fr" => "Présentations dans un séminaire de recherche"
"en" => "Presentations at a Faculty research seminar"
]
"support_type" => array:2 [
"fr" => null
"en" => null
]
"countries" => array:2 [
"fr" => null
"en" => null
]
"abstract" => array:2 [
"fr" => ""
"en" => """
The PAC-Bayesian theory provides tools to understand the accuracy of Bayes-inspired algorithms that learn probability distributions on parameters. This theory was initially developed by McAllester about 20 years ago, and applied successfully\n The PAC-Bayesian theory provides tools to understand the accuracy of Bayes-inspired algorithms that
to various machine learning algorithms in various problems. Recently, it led to tight generalization bounds for deep neural networks, a task that could not be achieved by standard "worst-case" generalization bounds such as Vapnik-Chervonenkis bounds. In this talk, I will provide a brief introduction to PAC-Bayes bounds, and explain the core ideas of the theory. I will also provide an overview of the recent research directions. In particular, I will highlight the application of PAC-Bayes bounds to derive minimax-optimal rates of convergence in classification and in regression, and the connection to mutual-information bounds. to various machine learning algorithms in various problems. Recently, it led to tight generalization
"""
]
"authors_fields" => array:2 [
"fr" => "Systèmes d'Information, Data Analytics et Opérations"
"en" => "Information Systems, Data Analytics and Operations"
]
"indexedAt" => "2025-04-02T09:21:48.000Z"
"docTitle" => "PAC-Bayes bounds: understanding the generalization of Bayesian learning algorithms."
"docSurtitle" => "Présentations dans un séminaire de recherche"
"authorNames" => "<a href="/cv/alquier-pierre">ALQUIER Pierre</a>"
"docDescription" => "<span class="document-property-authors">ALQUIER Pierre</span><br><span class="document-property-authors_fields">Systèmes d'Information, Data Analytics et Opérations</span> | <span class="document-property-year">2024</span> <span class="document-property-authors">ALQUIER Pierre</span><br><span class="document-property-auth "
"keywordList" => "<a href="#">Machine learning, neural networks, information theory.</a>"
"docPreview" => "<b>PAC-Bayes bounds: understanding the generalization of Bayesian learning algorithms.</b><br><span>2024-10 | Présentations dans un séminaire de recherche </span> <b>PAC-Bayes bounds: understanding the generalization of Bayesian learning algorithms.</b><br><span> "
"docType" => "research"
"publicationLink" => "<a href="#" target="_blank">PAC-Bayes bounds: understanding the generalization of Bayesian learning algorithms.</a> <a href="#" target="_blank">PAC-Bayes bounds: understanding the generalization of Bayesian learning "
]
+lang: "fr"
+"_type": "_doc"
+"_score": 8.969117
+"parent": null
}
