419 citations · 3.1k across the 71 of their papers we have counts for
6 papers · 1 filter
SelfieBoost: A Boosting Algorithm for Deep Learning
Shai Shalev-Shwartz
We describe and analyze a new boosting algorithm for deep learning called SelfieBoost. Unlike other boosting algorithms, like AdaBoost, which construct ensembles of classifiers, Se…
Accelerated Proximal Stochastic Dual Coordinate Ascent for Regularized Loss Minimization
Shai Shalev-Shwartz, Tong Zhang
We introduce a proximal version of the stochastic dual coordinate ascent method and show how to accelerate the method using an inner-outer iteration procedure. We analyze the runti…
Accelerated Mini-Batch Stochastic Dual Coordinate Ascent
Shai Shalev-Shwartz, Tong Zhang
Stochastic dual coordinate ascent (SDCA) is an effective technique for solving regularized loss minimization problems in machine learning. This paper considers an extension of SDCA…
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…
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…
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…