@inproceedings{34df8e1ba3d248faa8fb247ac6c42c44,
title = "Finito: A faster, permutable incremental gradient method for big data problems",
abstract = "Recent advances in optimization theory have shown that smooth strongly convex finite sums can be minimized faster than by treating them as a black box {"}batch{"} problem. In this work we introduce a new method in this class with a theoretical convergence rate four times faster than ex-isting methods, for sums with sufficiently many terms. This method is also amendable to a sampling without replacement scheme that in practice gives further speed-ups. We give empirical results showing state of the art performance.",
author = "Defazio, {Aaron J.} and Caetano, {Tib{\'e}rio S.} and Justin Domke",
note = "Publisher Copyright: Copyright {\textcopyright} (2014) by the International Machine Learning Society (IMLS) All rights reserved.; 31st International Conference on Machine Learning, ICML 2014 ; Conference date: 21-06-2014 Through 26-06-2014",
year = "2014",
language = "English",
series = "31st International Conference on Machine Learning, ICML 2014",
publisher = "International Machine Learning Society (IMLS)",
pages = "2839--2855",
booktitle = "31st International Conference on Machine Learning, ICML 2014",
}