ICML 2017poster45 citations
Conditional Accelerated Lazy Stochastic Gradient Descent
Guanghui Lan, Sebastian Pokutta, Yi Zhou, Daniel Zink
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 $O(1/\epsilon^2)$ improving over the projection-free, Online Frank-Wolfe based stochastic gradient descent of (Hazan and Kale, 2012) with convergence rate $O(1/\epsilon^4)$.
BibTeX
@InProceedings{pmlr-v70-lan17a,
title = {Conditional Accelerated Lazy Stochastic Gradient Descent},
author = {Guanghui Lan and Sebastian Pokutta and Yi Zhou and Daniel Zink},
booktitle = {Proceedings of the 34th International Conference on Machine Learning},
pages = {1965--1974},
year = {2017},
editor = {Precup, Doina and Teh, Yee Whye},
volume = {70},
series = {Proceedings of Machine Learning Research},
month = {06--11 Aug},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v70/lan17a/lan17a.pdf},
url = {https://proceedings.mlr.press/v70/lan17a.html},
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 $O(1/\epsilon^2)$ improving over the projection-free, Online Frank-Wolfe based stochastic gradient descent of (Hazan and Kale, 2012) with convergence rate $O(1/\epsilon^4)$.}
}