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When is Importance Weighting Correction Needed for Covariate Shift Adaptation?

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arxiv 2303.04020 v1 pith:PD72YDPD submitted 2023-03-07 stat.ML cs.LG

Davit Gogolashvili , Matteo Zecchin , Motonobu Kanagawa , Marios Kountouris , Maurizio Filippone This is my paper · ORCID
classification stat.MLcs.LG
keywords correctionneededwhenmisspecifiedmodelnonparametricweightingcovariate
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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.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Nonparametric Goodness-of-fit Testing under Covariate Shift

    stat.ME 2026-08 conditional novelty 6.0 of 10

    Truncated importance-weighted kernel ridge regression with multiplier bootstrap yields valid L2(Q) confidence balls for nonparametric goodness-of-fit under covariate shift.

  2. Spectral Algorithms in Misspecified Regression: Convergence under Covariate Shift

    stat.ML 2025-09 accept novelty 6.0 of 10

    Weighted spectral algorithms under covariate shift achieve minimax rates for bounded density ratios and near-optimal rates under truncation, including misspecified targets.

  3. Computational Efficiency under Covariate Shift in Kernel Ridge Regression

    stat.ML 2025-05 conditional novelty 6.0 of 10

    Nyström-approximated importance-weighted kernel ridge regression achieves the same optimal excess-risk rates as the full method under covariate shift, with sublinear time and memory costs.

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