67 citations · 140 across the 6 of their papers we have counts for
6 papers
Minimizing The Misclassification Error Rate Using a Surrogate Convex Loss
Shai Ben-David, David Loker, Nathan Srebro +1
We carefully study how well minimizing convex surrogate loss functions, corresponds to minimizing the misclassification error rate for the problem of binary classification with lin…
PRISMA: PRoximal Iterative SMoothing Algorithm
Francesco Orabona, Andreas Argyriou, Nathan Srebro
Motivated by learning problems including max-norm regularized matrix completion and clustering, robust PCA and sparse inverse covariance selection, we propose a novel optimization…
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…
The Kernelized Stochastic Batch Perceptron
Andrew Cotter, Shai Shalev-Shwartz, Nathan Srebro
We present a novel approach for training kernel Support Vector Machines, establish learning runtime guarantees for our method that are better then those of any other known kerneliz…
Semi-supervised Learning with Density Based Distances
Avleen S. Bijral, Nathan Ratliff, Nathan Srebro
We present a simple, yet effective, approach to Semi-Supervised Learning. Our approach is based on estimating density-based distances (DBD) using a shortest path calculation on a g…
Collaborative Filtering in a Non-Uniform World: Learning with the Weighted Trace Norm
Ruslan Salakhutdinov, Nathan Srebro
We show that matrix completion with trace-norm regularization can be significantly hurt when entries of the matrix are sampled non-uniformly. We introduce a weighted version of the…