1 citations · 1 across the 6 of their papers we have counts for
6 papers
S2MAM: Semi-supervised Meta Additive Model for Robust Estimation and Variable Selection
Xuelin Zhang, Hong Chen, Yingjie Wang +2
Semi-supervised learning with manifold regularization is a classical framework for jointly learning from both labeled and unlabeled data, where the key requirement is that the supp…
Fine-grained Analysis of Stability and Generalization for Stochastic Bilevel Optimization
Xuelin Zhang, Hong Chen, Bin Gu +2
Stochastic bilevel optimization (SBO) has been integrated into many machine learning paradigms recently, including hyperparameter optimization, meta learning, and reinforcement lea…
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…
A Personalized Diagnostic Generation Framework Based on Multi-source Heterogeneous Data
Jialun Wu, Zeyu Gao, Haichuan Zhang +4
Personalized diagnoses have not been possible due to sear amount of data pathologists have to bear during the day-to-day routine. This lead to the current generalized standards tha…
W-Net: A Two-Stage Convolutional Network for Nucleus Detection in Histopathology Image
Anyu Mao, Jialun Wu, Xinrui Bao +3
Pathological diagnosis is the gold standard for cancer diagnosis, but it is labor-intensive, in which tasks such as cell detection, classification, and counting are particularly pr…