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The Ethereal
Distributed Mini-Batch SDCA
July 29, 2015 ยท Declared Dead ยท ๐ arXiv.org
"No code URL or promise found in abstract"
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Authors
Martin Takรกฤ, Peter Richtรกrik, Nathan Srebro
arXiv ID
1507.08322
Category
cs.LG: Machine Learning
Cross-listed
math.OC
Citations
50
Venue
arXiv.org
Last Checked
5 months ago
Abstract
We present an improved analysis of mini-batched stochastic dual coordinate ascent for regularized empirical loss minimization (i.e. SVM and SVM-type objectives). Our analysis allows for flexible sampling schemes, including where data is distribute across machines, and combines a dependence on the smoothness of the loss and/or the data spread (measured through the spectral norm).
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