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."
    "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
        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.
        """
    ]
    "authors_fields" => array:2 [
      "fr" => "Systèmes d'Information, Data Analytics et Opérations"
      "en" => "Information Systems, Data Analytics and Operations"
    ]
    "indexedAt" => "2025-07-10T19:21:47.000Z"
    "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>"
    "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>"
    "docType" => "research"
    "publicationLink" => "<a href="#" target="_blank">PAC-Bayes bounds: understanding the generalization of Bayesian learning algorithms.</a>"
  ]
  +lang: "fr"
  +"_type": "_doc"
  +"_score": 8.838929
  +"parent": null
}