# Double-Weighting for Covariate Shift Adaptation

arXiv: https://arxiv.org/abs/2305.08637 · HTML: https://mlsanity.com/paper/2305.08637/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 35 references that are in the map.
- First posted on arXiv 15 May 2023.

## Builds on (most cited first, 6 of 6)

- [A Minimax Approach to Supervised Learning](https://mlsanity.com/paper/1606.02206/) (2016)
- [Generalized Maximum Entropy for Supervised Classification](https://mlsanity.com/paper/2007.05447/) (2020)
- [Minimax risk classifiers with 0-1 loss](https://mlsanity.com/paper/2201.06487/) (2022)
- [Robust Correction of Sampling Bias Using Cumulative Distribution Functions](https://mlsanity.com/paper/2010.12687/) (2020)
- [MRCpy: A Library for Minimax Risk Classifiers](https://mlsanity.com/paper/2108.01952/) (2021)
- [Robust Covariate Shift Prediction with General Losses and Feature Views](https://mlsanity.com/paper/1712.10043/) (2017)

## Built on by (most cited first, 8 of 8)

- [A Short Survey on Importance Weighting for Machine Learning](https://mlsanity.com/paper/2403.10175/) (2024)
- [Density Ratio Estimation via Sampling along Generalized Geodesics on Statistical Manifolds](https://mlsanity.com/paper/2406.18806/) (2024)
- [SKADA-Bench: Benchmarking Unsupervised Domain Adaptation Methods with Realistic Validation On Diverse Modalities](https://mlsanity.com/paper/2407.11676/) (2024)
- [Weighted Risk Invariance: Domain Generalization under Invariant Feature Shift](https://mlsanity.com/paper/2407.18428/) (2024)
- [KMM-CP: Practical Conformal Prediction under Covariate Shift via Selective Kernel Mean Matching](https://mlsanity.com/paper/2603.26415/) (2026)
- [Minimum Wasserstein distance estimator under covariate shift: closed-form, super-efficiency and irregularity](https://mlsanity.com/paper/2601.07282/) (2026)
- [IIns-VAE+: A Robust Transfer Learning Framework for Environmental Identification in Wireless Sensing](https://mlsanity.com/paper/2609.06131/) (2026)
- [Latent-Space Importance Weighting under Covariate Shift](https://mlsanity.com/paper/2610.15571/) (2026)

## Abstract (arXiv)

Supervised learning is often affected by a covariate shift in which the marginal distributions of instances (covariates x) of training and testing samples ptr(x) and pte(x) are different but the label conditionals coincide. Existing approaches address such covariate shift by either using the ratio pte(x)/ptr(x) to weight training samples (reweighted methods) or using the ratio ptr(x)/pte(x) to weight testing samples (robust methods). However, the performance of such approaches can be poor under support mismatch or when the above ratios take large values. We propose a minimax risk classification (MRC) approach for covariate shift adaptation that avoids such limitations by weighting both training and testing samples. In addition, we develop effective techniques that obtain both sets of weights and generalize the conventional kernel mean matching method. We provide novel generalization bounds for our method that show a significant increase in the effective sample size compared with reweighted methods. The proposed method also achieves enhanced classification performance in both synthetic and empirical experiments.