ICML 2019oral133 citations
Random Shuffling Beats SGD after Finite Epochs
Abstract
A long-standing problem in stochastic optimization is proving that \rsgd, the without-replacement version of \sgd, converges faster than the usual with-replacement \sgd. Building upon \citep{gurbuzbalaban2015random}, we present the
BibTeX
@InProceedings{pmlr-v97-haochen19a,
title = {Random Shuffling Beats {SGD} after Finite Epochs},
author = {Haochen, Jeff and Sra, Suvrit},
booktitle = {Proceedings of the 36th International Conference on Machine Learning},
pages = {2624--2633},
year = {2019},
editor = {Chaudhuri, Kamalika and Salakhutdinov, Ruslan},
volume = {97},
series = {Proceedings of Machine Learning Research},
month = {09--15 Jun},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v97/haochen19a/haochen19a.pdf},
url = {https://proceedings.mlr.press/v97/haochen19a.html},
abstract = {A long-standing problem in stochastic optimization is proving that \rsgd, the without-replacement version of \sgd, converges faster than the usual with-replacement \sgd. Building upon \citep{gurbuzbalaban2015random}, we present the