When is Importance Weighting Correction Needed for Covariate Shift Adaptation? - Archive ouverte HAL Accéder directement au contenu
Pré-Publication, Document De Travail Année : 2023

When is Importance Weighting Correction Needed for Covariate Shift Adaptation?

Motonobu Kanagawa
  • Fonction : Auteur
  • PersonId : 1143828
Marios Kountouris
Maurizio Filippone
  • Fonction : Auteur
  • PersonId : 1021042

Résumé

This paper investigates when the importance weighting (IW) correction is needed to address covariate shift, a common situation in supervised learning where the input distributions of training and test data differ. Classic results show that the IW correction is needed when the model is parametric and misspecified. In contrast, recent results indicate that the IW correction may not be necessary when the model is nonparametric and well-specified. We examine the missing case in the literature where the model is nonparametric and misspecified, and show that the IW correction is needed for obtaining the best approximation of the true unknown function for the test distribution. We do this by analyzing IW-corrected kernel ridge regression, covering a variety of settings, including parametric and nonparametric models, well-specified and misspecified settings, and arbitrary weighting functions.

Dates et versions

hal-04069537 , version 1 (14-04-2023)

Identifiants

Citer

Davit Gogolashvili, Matteo Zecchin, Motonobu Kanagawa, Marios Kountouris, Maurizio Filippone. When is Importance Weighting Correction Needed for Covariate Shift Adaptation?. 2023. ⟨hal-04069537⟩
20 Consultations
0 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More