2 citations · 2 across the 5 of their papers we have counts for
5 papers
Stepdown SLOPE for Controlled Feature Selection
Jingxuan Liang, Hong Chen, Xuelin Zhang +2
Sorted L-One Penalized Estimation (SLOPE) has shown the nice theoretical property as well as empirical behavior recently on the false discovery rate (FDR) control of high-dimension…
Stability-based Generalization Analysis for Mixtures of Pointwise and Pairwise Learning
Jiahuan Wang, Jun Chen, Hong Chen +3
Recently, some mixture algorithms of pointwise and pairwise learning (PPL) have been formulated by employing the hybrid error metric of "pointwise loss + pairwise loss" and have sh…
On the Stability and Generalization of Triplet Learning
Jun Chen, Hong Chen, Xue Jiang +4
Triplet learning, i.e. learning from triplet data, has attracted much attention in computer vision tasks with an extremely large number of categories, e.g., face recognition and pe…
Error-based Knockoffs Inference for Controlled Feature Selection
Xuebin Zhao, Hong Chen, Yingjie Wang +4
Recently, the scheme of model-X knockoffs was proposed as a promising solution to address controlled feature selection under high-dimensional finite-sample settings. However, the p…
Principal Model Analysis Based on Partial Least Squares
Qiwei Xie, Liang Tang, Weifu Li +2
Motivated by the Bagging Partial Least Squares (PLS) and Principal Component Analysis (PCA) algorithms, we propose a Principal Model Analysis (PMA) method in this paper. In the pro…