1 citations · 1 across the 5 of their papers we have counts for
5 papers
Gradient-Free Method for Heavily Constrained Nonconvex Optimization
Wanli Shi, Hongchang Gao, Bin Gu
Zeroth-order (ZO) method has been shown to be a powerful method for solving the optimization problem where explicit expression of the gradients is difficult or infeasible to obtain…
Double Momentum Method for Lower-Level Constrained Bilevel Optimization
Wanli Shi, Yi Chang, Bin Gu
Bilevel optimization (BO) has recently gained prominence in many machine learning applications due to its ability to capture the nested structure inherent in these problems. Recent…
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…