419 citations · 3.1k across the 71 of their papers we have counts for
Showing 2012 · stat.MLShow all
3 papers · 2 filters
stat.ML2012★ 1 cited
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…
stat.ML2012★ 88 cited
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…
stat.ML2012★ 243 cited
Stochastic Dual Coordinate Ascent Methods for Regularized Loss Minimization
Shai Shalev-Shwartz, Tong Zhang
Stochastic Gradient Descent (SGD) has become popular for solving large scale supervised machine learning optimization problems such as SVM, due to their strong theoretical guarante…