Importance Weight Estimation and Generalization in Domain Adaptation Under Label Shift
Kamyar Azizzadenesheli · IEEE Transactions on Pattern Analysis and Machine Intelligence · 2021
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.