# Importance Weight Estimation and Generalization in Domain Adaptation under Label Shift

arXiv: https://arxiv.org/abs/2011.14251 · HTML: https://mlsanity.com/paper/2011.14251/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Nov 2020.

## Built on by (most cited first, 10 of 10)

- [Federated Learning under Covariate Shifts with Generalization Guarantees](https://mlsanity.com/paper/2306.05325/) (2023)
- [UDA-Bench: Revisiting Common Assumptions in Unsupervised Domain Adaptation Using a Standardized Framework](https://mlsanity.com/paper/2409.15264/) (2024)
- [Sparse joint shift in multinomial classification](https://mlsanity.com/paper/2303.16971/) (2023)
- [UniTrans: A Unified Vertical Federated Knowledge Transfer Framework for Enhancing Cross-Hospital Collaboration](https://mlsanity.com/paper/2501.11388/) (2025)
- [Contrastive Conditional Alignment based on Label Shift Calibration for Imbalanced Domain Adaptation](https://mlsanity.com/paper/2412.20337/) (2024)
- [Functional Linear Regression of Cumulative Distribution Functions](https://mlsanity.com/paper/2205.14545/) (2022)
- [centroIDA: Cross-Domain Class Discrepancy Minimization Based on Accumulative Class-Centroids for Imbalanced Domain Adaptation](https://mlsanity.com/paper/2308.10619/) (2023)
- [Theory-inspired Label Shift Adaptation via Aligned Distribution Mixture](https://mlsanity.com/paper/2411.02047/) (2024)
- [An Efficient Plugin Method for Metric Optimization of Black-Box Models](https://mlsanity.com/paper/2503.02119/) (2025)
- [Factorizable joint shift revisited](https://mlsanity.com/paper/2601.15036/) (2026)

## Abstract (arXiv)

We study generalization under labeled shift for categorical and general normed label spaces. We propose a series of methods to estimate the importance weights from labeled source to unlabeled target domain and provide confidence bounds for these estimators. We deploy these estimators and provide generalization bounds in the unlabeled target domain.