# A One-step Approach to Covariate Shift Adaptation

arXiv: https://arxiv.org/abs/2007.04043 · HTML: https://mlsanity.com/paper/2007.04043/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jul 2020.

## Built on by (most cited first, 21 of 21)

- [TOHAN: A One-step Approach towards Few-shot Hypothesis Adaptation](https://mlsanity.com/paper/2106.06326/) (2021)
- [Adapting to Continuous Covariate Shift via Online Density Ratio Estimation](https://mlsanity.com/paper/2302.02552/) (2023)
- [Binary Classification from Multiple Unlabeled Datasets via Surrogate Set Classification](https://mlsanity.com/paper/2102.00678/) (2021)
- [Being Properly Improper](https://mlsanity.com/paper/2106.09920/) (2021)
- [Rethinking Importance Weighting for Transfer Learning](https://mlsanity.com/paper/2112.10157/) (2021)
- [Best-Effort Adaptation](https://mlsanity.com/paper/2305.05816/) (2023)
- [Information Geometrically Generalized Covariate Shift Adaptation](https://mlsanity.com/paper/2304.09387/) (2023)
- [Federated Learning under Covariate Shifts with Generalization Guarantees](https://mlsanity.com/paper/2306.05325/) (2023)
- [Nearest Neighbor Sampling for Covariate Shift Adaptation](https://mlsanity.com/paper/2312.09969/) (2023)
- [Making Binary Classification from Multiple Unlabeled Datasets Almost Free of Supervision](https://mlsanity.com/paper/2306.07036/) (2023)
- [AdaTrans: Feature-wise and Sample-wise Adaptive Transfer Learning for High-dimensional Regression](https://mlsanity.com/paper/2403.13565/) (2024)
- [Adversarial Attack and Defense for Non-Parametric Two-Sample Tests](https://mlsanity.com/paper/2202.03077/) (2022)
- [Mitigating covariate shift in non-colocated data with learned parameter priors](https://mlsanity.com/paper/2411.06499/) (2024)
- [Learning from Biased and Costly Data Sources: Minimax-optimal Data Collection under a Budget](https://mlsanity.com/paper/2602.17894/) (2026)
- [TILT: Target-induced loss tilting under covariate shift](https://mlsanity.com/paper/2605.14280/) (2026)
- [Walk a Mile in Their Shoes: a New Fairness Criterion for Machine Learning](https://mlsanity.com/paper/2210.06680/) (2022)
- [Differentially Private Domain Adaptation with Theoretical Guarantees](https://mlsanity.com/paper/2306.08838/) (2023)
- [Adapting to Covariate Shift in Real-time by Encoding Trees with Motion Equations](https://mlsanity.com/paper/2404.05168/) (2024)
- [Short-Long Policy Evaluation with Novel Actions](https://mlsanity.com/paper/2407.03674/) (2024)
- [Technical note on Fisher Information for Robust Federated Cross-Validation](https://mlsanity.com/paper/2510.03838/) (2025)
- [Latent-Space Importance Weighting under Covariate Shift](https://mlsanity.com/paper/2610.15571/) (2026)

## Abstract (arXiv)

A default assumption in many machine learning scenarios is that the training and test samples are drawn from the same probability distribution. However, such an assumption is often violated in the real world due to non-stationarity of the environment or bias in sample selection. In this work, we consider a prevalent setting called covariate shift, where the input distribution differs between the training and test stages while the conditional distribution of the output given the input remains unchanged. Most of the existing methods for covariate shift adaptation are two-step approaches, which first calculate the importance weights and then conduct importance-weighted empirical risk minimization. In this paper, we propose a novel one-step approach that jointly learns the predictive model and the associated weights in one optimization by minimizing an upper bound of the test risk. We theoretically analyze the proposed method and provide a generalization error bound. We also empirically demonstrate the effectiveness of the proposed method.