1.9k citations · 6.5k across the 132 of their papers we have counts for
4 papers · 2 filters
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…
Matrix reconstruction with the local max norm
Rina Foygel, Nathan Srebro, Ruslan Salakhutdinov
We introduce a new family of matrix norms, the "local max" norms, generalizing existing methods such as the max norm, the trace norm (nuclear norm), and the weighted or smoothed we…
Sparse Prediction with the -Support Norm
Andreas Argyriou, Rina Foygel, Nathan Srebro
We derive a novel norm that corresponds to the tightest convex relaxation of sparsity combined with an penalty. We show that this new {\em -support norm} provides a tig…
Distribution-Dependent Sample Complexity of Large Margin Learning
Sivan Sabato, Nathan Srebro, Naftali Tishby
We obtain a tight distribution-specific characterization of the sample complexity of large-margin classification with L2 regularization: We introduce the margin-adapted dimension,…