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Accelerated Proximal Stochastic Dual Coordinate Ascent for Regularized Loss Minimization

2020-03-04 | |  54 |   40 |   0

Abstract

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 runtime of the framework and obtain rates that improve stateof-the-art results for various key machine learning optimization problems including SVM, logistic regression, ridge regression, Lasso, and multiclass SVM. Experiments validate our theoretical findings.

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