419 citations · 3.1k across the 71 of their papers we have counts for
5 papers · 1 filter
More data speeds up training time in learning halfspaces over sparse vectors
Amit Daniely, Nati Linial, Shai Shalev Shwartz
The increased availability of data in recent years has led several authors to ask whether it is possible to use data as a {\em computational} resource. That is, if more data is ava…
From average case complexity to improper learning complexity
Amit Daniely, Nati Linial, Shai Shalev-Shwartz
The basic problem in the PAC model of computational learning theory is to determine which hypothesis classes are efficiently learnable. There is presently a dearth of results showi…
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…
An Algorithm for Training Polynomial Networks
Roi Livni, Shai Shalev-Shwartz, Ohad Shamir
We consider deep neural networks, in which the output of each node is a quadratic function of its inputs. Similar to other deep architectures, these networks can compactly represen…