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The Ethereal
Conditional Accelerated Lazy Stochastic Gradient Descent
March 16, 2017 ยท Declared Dead ยท ๐ International Conference on Machine Learning
"No code URL or promise found in abstract"
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Authors
Guanghui Lan, Sebastian Pokutta, Yi Zhou, Daniel Zink
arXiv ID
1703.05840
Category
cs.LG: Machine Learning
Cross-listed
stat.ML
Citations
29
Venue
International Conference on Machine Learning
Last Checked
4 months ago
Abstract
In this work we introduce a conditional accelerated lazy stochastic gradient descent algorithm with optimal number of calls to a stochastic first-order oracle and convergence rate $O\left(\frac{1}{\varepsilon^2}\right)$ improving over the projection-free, Online Frank-Wolfe based stochastic gradient descent of Hazan and Kale [2012] with convergence rate $O\left(\frac{1}{\varepsilon^4}\right)$.
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