Semi-Supervised Triply Robust Inductive Transfer Learning
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半监督三重稳健归纳迁移学习
- 研究问题
- 面对高维协变量漂移，如何结合标签充足的源人群、标签稀缺的目标人群及大量无标签数据，提高目标人群预测精度？
- 方法与思路
- 作者提出 STRIFLE，以密度比和插补两个辅助模型结合迁移学习与替代特征辅助的半监督学习，并检测源数据的可迁移性。
- 材料陈述的贡献
- 摘要称，方法具有三重稳健性质；其相对仅用目标数据方法的误差保证含额外迁移检测项，并有理论、模拟和疾病预测应用。
- 阅读关注点
- 应核对三重稳健的准确分支、条件分布共享要求、允许部分迁移的相似性条件，以及迁移检测附加误差项的大小。
- 阅读边界
- 摘要中的条件分布符号存在乱码，需核对原文；具体误差界与高维条件未列出，不能将其保证理解为无条件优于目标样本方法。
原文摘要
In this work, we propose a Semi-supervised Triply Robust Inductive transFer LEarning (STRIFLE) approach, which integrates heterogeneous data from a label-rich source population and a label-scarce target population and uses a large amount of unlabeled data simultaneously to improve the learning accuracy in the target population. Specifically, we consider a high dimensional covariate shift setting and employ two nuisance models, a density ratio model and an imputation model, to combine transfer learning and surrogate-assisted semi-supervised learning strategies effectively and achieve triple robustness. While the STRIFLE approach assumes the target and source populations to share the same conditional distribution of outcome Y given both the surrogate features S and predictors X, it allows the true underlying model of Y⏧X to differ between the two populations due to the potential covariate shift in S and X. Different from double robustness, even if both nuisance models are misspecified or the distribution of Y⏧S,X is not the same between the two populations when the shifted source population and the target population share enough similarities, the triply robust STRIFLE estimator can still partially use the source population when the shifted source population and the target population share enough similarities. Moreover, it is guaranteed to be no worse than the target-only surrogate-assisted semi-supervised estimator with an additional error term from transferability detection. These desirable properties of our estimator are established theoretically and verified in finite samples via extensive simulation studies. We use the STRIFLE estimator to train a Type II diabetes polygenic risk prediction model for the African American target population by transferring knowledge from electronic health records linked genomic data observed in a larger European source population. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.