36 citations · 141 across the 18 of their papers we have counts for
5 papers · 1 filter
Safe Sample Screening for Robust Support Vector Machine
Zhou Zhai, Bin Gu, Xiang Li +1
Robust support vector machine (RSVM) has been shown to perform remarkably well to improve the generalization performance of support vector machine under the noisy environment. Unfo…
Quadruply Stochastic Gradient Method for Large Scale Nonlinear Semi-Supervised Ordinal Regression AUC Optimization
Wanli Shi, Bin Gu, Xinag Li +1
Semi-supervised ordinal regression (SOR) problems are ubiquitous in real-world applications, where only a few ordered instances are labeled and massive instances remain unlabel…
Quadruply Stochastic Gradients for Large Scale Nonlinear Semi-Supervised AUC Optimization
Wanli Shi, Bin Gu, Xiang Li +2
Semi-supervised learning is pervasive in real-world applications, where only a few labeled data are available and large amounts of instances remain unlabeled. Since AUC is an impor…
Scalable Semi-Supervised SVM via Triply Stochastic Gradients
Xiang Geng, Bin Gu, Xiang Li +3
Semi-supervised learning (SSL) plays an increasingly important role in the big data era because a large number of unlabeled samples can be used effectively to improve the performan…
Faster Gradient-Free Proximal Stochastic Methods for Nonconvex Nonsmooth Optimization
Feihu Huang, Bin Gu, Zhouyuan Huo +2
Proximal gradient method has been playing an important role to solve many machine learning tasks, especially for the nonsmooth problems. However, in some machine learning problems…