60 citations · 307 across the 33 of their papers we have counts for
33 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…
Zeroth-Order Negative Curvature Finding: Escaping Saddle Points without Gradients
Hualin Zhang, Huan Xiong, Bin Gu
We consider escaping saddle points of nonconvex problems where only the function evaluations can be accessed. Although a variety of works have been proposed, the majority of them r…
Zeroth-Order Hard-Thresholding: Gradient Error vs. Expansivity
William de Vazelhes, Hualin Zhang, Huimin Wu +2
constrained optimization is prevalent in machine learning, particularly for high-dimensional problems, because it is a fundamental approach to achieve sparse learning. Har…
An Accelerated Doubly Stochastic Gradient Method with Faster Explicit Model Identification
Runxue Bao, Bin Gu, Heng Huang
Sparsity regularized loss minimization problems play an important role in various fields including machine learning, data mining, and modern statistics. Proximal gradient descent m…
Balanced Self-Paced Learning for AUC Maximization
Bin Gu, Chenkang Zhang, Huan Xiong +1
Learning to improve AUC performance is an important topic in machine learning. However, AUC maximization algorithms may decrease generalization performance due to the noisy data. S…
On the Convergence of Distributed Stochastic Bilevel Optimization Algorithms over a Network
Hongchang Gao, Bin Gu, My T. Thai
Bilevel optimization has been applied to a wide variety of machine learning models, and numerous stochastic bilevel optimization algorithms have been developed in recent years. How…