536 citations · 723 across the 7 of their papers we have counts for
7 papers
A Stochastic Composite Gradient Method with Incremental Variance Reduction
Junyu Zhang, Lin Xiao
We consider the problem of minimizing the composition of a smooth (nonconvex) function and a smooth vector mapping, where the inner mapping is in the form of an expectation over so…
Adaptive Stochastic Variance Reduction for Subsampled Newton Method with Cubic Regularization
Junyu Zhang, Lin Xiao, Shuzhong Zhang
The cubic regularized Newton method of Nesterov and Polyak has become increasingly popular for non-convex optimization because of its capability of finding an approximate local sol…
Accelerated Bregman Proximal Gradient Methods for Relatively Smooth Convex Optimization
Filip Hanzely, Peter Richtarik, Lin Xiao
We consider the problem of minimizing the sum of two convex functions: one is differentiable and relatively smooth with respect to a reference convex function, and the other can be…
Stochastic Primal-Dual Coordinate Method for Regularized Empirical Risk Minimization
Yuchen Zhang, Lin Xiao
We consider a generic convex optimization problem associated with regularized empirical risk minimization of linear predictors. The problem structure allows us to reformulate it as…
An Accelerated Proximal Coordinate Gradient Method and its Application to Regularized Empirical Risk Minimization
Qihang Lin, Zhaosong Lu, Lin Xiao
We consider the problem of minimizing the sum of two convex functions: one is smooth and given by a gradient oracle, and the other is separable over blocks of coordinates and has a…
Robust Distributed Online Prediction
Ofer Dekel, Ran Gilad-Bachrach, Ohad Shamir +1
The standard model of online prediction deals with serial processing of inputs by a single processor. However, in large-scale online prediction problems, where inputs arrive at a h…