Tianxi Cai,Mengyan Li,Molei Liu
Tianxi Cai
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 utilizes a large amoun...
Sharp-SSL: Selective High-Dimensional Axis-Aligned Random Projections for Semi-Supervised Learning [0.03%]
Sharp-SSL：半监督学习中用于选择性的高维轴对齐随机投影方法
Tengyao Wang,Edgar Dobriban,Milana Gataric et al.
Tengyao Wang et al.
We propose a new method for high-dimensional semi-supervised learning problems based on the careful aggregation of the results of a low-dimensional procedure applied to many axis-aligned random projections of the data. Our primary goal is t...
Optimal and Safe Estimation for High-Dimensional Semi-Supervised Learning [0.03%]
高维半监督学习的最优和安全估计方法研究
Siyi Deng,Yang Ning,Jiwei Zhao et al.
Siyi Deng et al.
We consider the estimation problem in high-dimensional semi-supervised learning. Our goal is to investigate when and how the unlabeled data can be exploited to improve the estimation of the regression parameters of linear model in light of ...