411 citations · 3k across the 39 of their papers we have counts for
7 papers · 1 filter
Stochastic Gradient Descent for Non-smooth Optimization: Convergence Results and Optimal Averaging Schemes
Ohad Shamir, Tong Zhang
Stochastic Gradient Descent (SGD) is one of the simplest and most popular stochastic optimization methods. While it has already been theoretically studied for decades, the classica…
Learning Sparse Low-Threshold Linear Classifiers
Sivan Sabato, Shai Shalev-Shwartz, Nathan Srebro +2
We consider the problem of learning a non-negative linear classifier with a -norm of at most , and a fixed threshold, under the hinge-loss. This problem generalizes the probl…
Analysis of a randomized approximation scheme for matrix multiplication
Daniel Hsu, Sham M. Kakade, Tong Zhang
This note gives a simple analysis of a randomized approximation scheme for matrix multiplication proposed by Sarlos (2006) based on a random rotation followed by uniform column sam…
Proximal Stochastic Dual Coordinate Ascent
Shai Shalev-Shwartz, Tong Zhang
We introduce a proximal version of dual coordinate ascent method. We demonstrate how the derived algorithmic framework can be used for numerous regularized loss minimization proble…
Partial Gaussian Graphical Model Estimation
Xiao-Tong Yuan, Tong Zhang
This paper studies the partial estimation of Gaussian graphical models from high-dimensional empirical observations. We derive a convex formulation for this problem using -…
A Proximal-Gradient Homotopy Method for the Sparse Least-Squares Problem
Lin Xiao, Tong Zhang
We consider solving the -regularized least-squares (-LS) problem in the context of sparse recovery, for applications such as compressed sensing. The standard proxim…