419 citations · 3.1k across the 71 of their papers we have counts for
8 papers · 1 filter
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…
The complexity of learning halfspaces using generalized linear methods
Amit Daniely, Nati Linial, Shai Shalev-Shwartz
Many popular learning algorithms (E.g. Regression, Fourier-Transform based algorithms, Kernel SVM and Kernel ridge regression) operate by reducing the problem to a convex optimizat…
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…
Efficient Active Learning of Halfspaces: an Aggressive Approach
Alon Gonen, Sivan Sabato, Shai Shalev-Shwartz
We study pool-based active learning of half-spaces. We revisit the aggressive approach for active learning in the realizable case, and show that it can be made efficient and practi…
Learning the Experts for Online Sequence Prediction
Elad Eban, Aharon Birnbaum, Shai Shalev-Shwartz +1
Online sequence prediction is the problem of predicting the next element of a sequence given previous elements. This problem has been extensively studied in the context of individu…