19 citations · 40 across the 18 of their papers we have counts for
Showing 2019 · math.OCShow all
3 papers · 2 filters
math.OC2019
Nonconvex Zeroth-Order Stochastic ADMM Methods with Lower Function Query Complexity
Feihu Huang, Shangqian Gao, Jian Pei +1
Zeroth-order (a.k.a, derivative-free) methods are a class of effective optimization methods for solving complex machine learning problems, where gradients of the objective function…
math.OC2019
Zeroth-Order Stochastic Alternating Direction Method of Multipliers for Nonconvex Nonsmooth Optimization
Feihu Huang, Shangqian Gao, Songcan Chen +1
Alternating direction method of multipliers (ADMM) is a popular optimization tool for the composite and constrained problems in machine learning. However, in many machine learning…
math.OC2019★ 1 cited
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…