Importance Weight Estimation and Generalization in Domain Adaptation under Label Shift
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.
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Domain AdaptationGeneralization BoundsOperator learningSimilar Papers 제목 키워드 기반
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