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The Ethereal
SDCA without Duality
February 22, 2015 ยท Declared Dead ยท ๐ arXiv.org
"No code URL or promise found in abstract"
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Authors
Shai Shalev-Shwartz
arXiv ID
1502.06177
Category
cs.LG: Machine Learning
Citations
48
Venue
arXiv.org
Last Checked
6 months ago
Abstract
Stochastic Dual Coordinate Ascent is a popular method for solving regularized loss minimization for the case of convex losses. In this paper we show how a variant of SDCA can be applied for non-convex losses. We prove linear convergence rate even if individual loss functions are non-convex as long as the expected loss is convex.
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