Search Machine Learning Repository: @inproceedings{icml2014c2_defazio14,
    Publisher = {JMLR Workshop and Conference Proceedings},
    Title = {Finito: A faster, permutable incremental gradient method for big data problems},
    Url = {},
    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 existing 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 = {Aaron Defazio and Justin Domke and Tiberio Caetano},
    Editor = {Tony Jebara and Eric P. Xing},
    Year = {2014},
    Booktitle = {Proceedings of the 31st International Conference on Machine Learning (ICML-14)},
    Pages = {1125-1133}