在假设-Lean框架内对高数字无标签数据的可依赖利用

Dependable Exploitation of High-Dimensional Unlabeled Data in an Assumption-Lean Framework

Semi-supervised learning has attracted significant attention due to the proliferation of applications featuring limited labeled data but abundant unlabeled data. In this paper, we examine the statistical inference problem in an assumption-lean framework which involves a high-dimensional regression parameter, defined by minimizing the least squares, within the context of semi-supervised learning. We investigate when and how unlabeled data can enhance the estimation efficiency of a regression