60 citations · 541 across the 61 of their papers we have counts for
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Zeroth-order Asynchronous Doubly Stochastic Algorithm with Variance Reduction
Bin Gu, Zhouyuan Huo, Heng Huang
Zeroth-order (derivative-free) optimization attracts a lot of attention in machine learning, because explicit gradient calculations may be computationally expensive or infeasible.…
Inexact Proximal Gradient Methods for Non-convex and Non-smooth Optimization
Bin Gu, De Wang, Zhouyuan Huo +1
In machine learning research, the proximal gradient methods are popular for solving various optimization problems with non-smooth regularization. Inexact proximal gradient methods…
Asynchronous Stochastic Block Coordinate Descent with Variance Reduction
Bin Gu, Zhouyuan Huo, Heng Huang
Asynchronous parallel implementations for stochastic optimization have received huge successes in theory and practice recently. Asynchronous implementations with lock-free are more…
Decoupled Asynchronous Proximal Stochastic Gradient Descent with Variance Reduction
Zhouyuan Huo, Bin Gu, Heng Huang
In the era of big data, optimizing large scale machine learning problems becomes a challenging task and draws significant attention. Asynchronous optimization algorithms come out a…
Distributed Asynchronous Dual Free Stochastic Dual Coordinate Ascent
Zhouyuan Huo, Heng Huang
The primal-dual distributed optimization methods have broad large-scale machine learning applications. Previous primal-dual distributed methods are not applicable when the dual for…
Asynchronous Stochastic Gradient Descent with Variance Reduction for Non-Convex Optimization
Zhouyuan Huo, Heng Huang
We provide the first theoretical analysis on the convergence rate of the asynchronous stochastic variance reduced gradient (SVRG) descent algorithm on non-convex optimization. Rece…