75 citations · 217 across the 7 of their papers we have counts for
7 papers · 1 filter
On the Computational Efficiency of Training Neural Networks
Roi Livni, Shai Shalev-Shwartz, Ohad Shamir
It is well-known that neural networks are computationally hard to train. On the other hand, in practice, modern day neural networks are trained efficiently using SGD and a variety…
Optimal Learners for Multiclass Problems
Amit Daniely, Shai Shalev-Shwartz
The fundamental theorem of statistical learning states that for binary classification problems, any Empirical Risk Minimization (ERM) learning rule has close to optimal sample comp…
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…
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…
Near-Optimal Algorithms for Online Matrix Prediction
Elad Hazan, Satyen Kale, Shai Shalev-Shwartz
In several online prediction problems of recent interest the comparison class is composed of matrices with bounded entries. For example, in the online max-cut problem, the comparis…
Learning Kernel-Based Halfspaces with the Zero-One Loss
Shai Shalev-Shwartz, Ohad Shamir, Karthik Sridharan
We describe and analyze a new algorithm for agnostically learning kernel-based halfspaces with respect to the \emph{zero-one} loss function. Unlike most previous formulations which…