mlsanityData to 7 Oct 2026

When is Importance Weighting Correction Needed for Covariate Shift Adaptation?

arXiv 2303.04020 ↗First posted 7 Mar 2023stat.ML cs.LGcited by 11

Not on the map. Why?

Abstract (arXiv)

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.

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). Builds on 16 of its 59 references that are in the map. First posted on arXiv 7 Mar 2023.

Builds on

16 of 59 references.

References · 16

  1. 2019Benchmarking Neural Network Robustness to Common Corruptions and Perturbationscited by 2,857
  2. 2018Neural Tangent Kernel: Convergence and Generalization in Neural Networkscited by 2,597
  3. 2020The Many Faces of Robustness: A Critical Analysis of Out-of-Distribution Generalizationcited by 1,842
  4. 2019On Exact Computation with an Infinitely Wide Neural Netcited by 572
  5. 2018What is the Effect of Importance Weighting in Deep Learning?cited by 250
  6. 2016Generalization Properties of Learning with Random Featurescited by 163
  7. 2020Rethinking Importance Weighting for Deep Learning under Distribution Shiftcited by 92
  8. 2018Marginal Singularity, and the Benefits of Labels in Covariate-Shiftcited by 65
  9. 2022Optimally tackling covariate shift in RKHS-based nonparametric regressioncited by 53
  10. 2021Near-Optimal Linear Regression under Distribution Shiftcited by 40
  11. 2021Understanding the role of importance weighting for deep learningcited by 38
  12. 2022A new similarity measure for covariate shift with applications to nonparametric regressioncited by 33
4 more
  1. 2022Understanding Why Generalized Reweighting Does Not Improve Over ERMcited by 29
  2. 2013Generalization Bounds for Domain Adaptationcited by 27
  3. 2021Is Importance Weighting Incompatible with Interpolating Classifiers?cited by 19
  4. 2023Pseudo-Labeling for Kernel Ridge Regression under Covariate Shiftcited by 17

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 3; 2025: 3; 2026: 4.

11 papers.

  1. 2024A Short Survey on Importance Weighting for Machine Learningcited by 15
  2. 2023Towards a Unified Analysis of Kernel-based Methods Under Covariate Shiftcited by 12
  3. 2024Conditional Bayesian Quadraturecited by 8
  4. 2024High-Dimensional Kernel Methods under Covariate Shift: Data-Dependent Implicit Regularizationcited by 4
  5. 2025Computational Efficiency under Covariate Shift in Kernel Ridge Regressioncited by 4
  6. 2025Spectral Algorithms under Covariate Shiftcited by 3
  7. 2025Spectral Algorithms in Misspecified Regression: Convergence under Covariate Shiftcited by 3
  8. 2026Towards regularized learning from functional data with covariate shiftcited by 1
  9. 2026Entropic Projection Alignment: Estimating, Explaining, and Improving Model Performance Under Distribution Shiftcited by 0
  10. 2026Convergence Analysis of Nystr"om Subsampling in Covariate Shift Adaptation for Misspecified casecited by 0
  11. 2026Learning under Target Shift: Optimal Density Ratio Estimation and Importance-Weighted Regressioncited by 0