# Robust Importance Weighting for Covariate Shift

arXiv: https://arxiv.org/abs/1910.06324 · HTML: https://mlsanity.com/paper/1910.06324/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Oct 2019.

## Built on by (most cited first, 15 of 15)

- [Mandoline: Model Evaluation under Distribution Shift](https://mlsanity.com/paper/2107.00643/) (2021)
- [A Short Survey on Importance Weighting for Machine Learning](https://mlsanity.com/paper/2403.10175/) (2024)
- [JAWS: Auditing Predictive Uncertainty Under Covariate Shift](https://mlsanity.com/paper/2207.10716/) (2022)
- [Estimating Model Performance Under Covariate Shift Without Labels](https://mlsanity.com/paper/2401.08348/) (2024)
- [Scalable Causal Domain Adaptation](https://mlsanity.com/paper/2103.00139/) (2021)
- [Adaptive Sampling for Probabilistic Forecasting under Distribution Shift](https://mlsanity.com/paper/2302.11870/) (2023)
- [Fairness under Covariate Shift: Improving Fairness-Accuracy tradeoff with few Unlabeled Test Samples](https://mlsanity.com/paper/2310.07535/) (2023)
- [Prediction-Enhanced Monte Carlo: A Machine Learning View on Control Variate](https://mlsanity.com/paper/2412.11257/) (2024)
- [Minimum Wasserstein distance estimator under covariate shift: closed-form, super-efficiency and irregularity](https://mlsanity.com/paper/2601.07282/) (2026)
- [Structured Credal Learning](https://mlsanity.com/paper/2603.14070/) (2026)
- [A Domain-Region Based Evaluation of ML Performance Robustness to Covariate Shift](https://mlsanity.com/paper/2304.08855/) (2023)
- [Minimax Optimal Two-Stage Algorithm For Moment Estimation Under Covariate Shift](https://mlsanity.com/paper/2506.23453/) (2025)
- [Fix Representation (Optimally) Before Fairness: Finite-Sample Shrinkage Population Correction and the True Price of Fairness Under Subpopulation Shift](https://mlsanity.com/paper/2602.05707/) (2026)
- [BadDLM: Backdooring Diffusion Language Models with Diverse Targets](https://mlsanity.com/paper/2605.09397/) (2026)
- [Latent-Space Importance Weighting under Covariate Shift](https://mlsanity.com/paper/2610.15571/) (2026)

## Abstract (arXiv)

In many learning problems, the training and testing data follow different distributions and a particularly common situation is the textitcovariate shift. To correct for sampling biases, most approaches, including the popular kernel mean matching (KMM), focus on estimating the importance weights between the two distributions. Reweighting-based methods, however, are exposed to high variance when the distributional discrepancy is large and the weights are poorly estimated. On the other hand, the alternate approach of using nonparametric regression (NR) incurs high bias when the training size is limited. In this paper, we propose and analyze a new estimator that systematically integrates the residuals of NR with KMM reweighting, based on a control-variate perspective. The proposed estimator can be shown to either strictly outperform or match the best-known existing rates for both KMM and NR, and thus is a robust combination of both estimators. The experiments shows the estimator works well in practice.