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Learning from Biased Data: A Semi-Parametric Approach

Author

Listed:
  • Patrice Bertail

    (FP2M - Fédération Parisienne de Modélisation Mathématique - CNRS - Centre National de la Recherche Scientifique, MODAL'X - Modélisation aléatoire de Paris X - UPN - Université Paris Nanterre - CNRS - Centre National de la Recherche Scientifique)

  • Stéphan Clémençon

    (S2A - Signal, Statistique et Apprentissage - LTCI - Laboratoire Traitement et Communication de l'Information - IMT - Institut Mines-Télécom [Paris] - Télécom Paris, IDS - Département Images, Données, Signal - Télécom ParisTech)

  • Yannick Guyonvarch

    (UMR PSAE - Paris-Saclay Applied Economics - AgroParisTech - Université Paris-Saclay - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement)

  • Nathan Noiry

    (S2A - Signal, Statistique et Apprentissage - LTCI - Laboratoire Traitement et Communication de l'Information - IMT - Institut Mines-Télécom [Paris] - Télécom Paris, IDS - Département Images, Données, Signal - Télécom ParisTech)

Abstract

We consider risk minimization problems where the (source) distribution P-S of the training obser- vations Z(1),..., Z(n) differs from the (target) distribution P-T involved in the risk that one seeks to minimize Under the natural assumption that P-S dominates P-T , i.e. PT

Suggested Citation

  • Patrice Bertail & Stéphan Clémençon & Yannick Guyonvarch & Nathan Noiry, 2021. "Learning from Biased Data: A Semi-Parametric Approach," Post-Print hal-04431531, HAL.
    • Handle: RePEc:hal:journl:hal-04431531
    as

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