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Transfer Learning Under Group‐Label Shift: A Semiparametric Exponential Tilting Approach

Year: 2026
Type: article
Abstract: ABSTRACT This article studies unsupervised transfer learning for a binary outcome, with labeled source data and unlabeled target covariates. The setting is motivated by cases where standard covariate‐shift and label‐shift assumptions are inadequate: group‐ or environment‐related features may vary jointly with the label across populations, while the remaining covariates remain conditionally stable given these features and the label. We formulate this structure as group‐label shift (GLS) and pair it with a class‐specific exponential tilting model for the joint density ratio between the target and source populations. The GLS framework generalizes existing shift assumptions by allowing heterogeneity in both feature and label distributions while mitigating spurious cross‐domain correlations. Under sufficient support and variation conditions on the source outcome regression, the tilting parameters are identifiable from the source distribution of covariates and outcomes, together with the target covariate distribution. A computationally efficient two‐step likelihood‐based estimation procedure is developed. We derive consistency and asymptotic normality for the resulting estimators and extend the theory to receiver operating characteristic curves, the area under the curve, and related target functionals, addressing the nonstandard challenges posed by plug‐in classifiers. Simulation studies demonstrate that the proposed method outperforms existing alternatives under GLS settings. An application to the Waterbirds data set further illustrates its effectiveness in transfer learning and classification.
DOI: 10.1111/sjos.70098
Authors: Manli Cheng, Subha Maity, Qinglong Tian, Pengfei Li
Language: English
Open Access status: hybrid
Estimated APC paid: $3,500
OpenAlex ID: W7083680947
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