NeurIPS 2016oral183 citations
Without-Replacement Sampling for Stochastic Gradient Methods
Abstract
Stochastic gradient methods for machine learning and optimization problems are usually analyzed assuming data points are sampled
BibTeX
@inproceedings{NIPS2016_c74d97b0,
author = {Shamir, Ohad},
booktitle = {Advances in Neural Information Processing Systems},
editor = {D. Lee and M. Sugiyama and U. Luxburg and I. Guyon and R. Garnett},
pages = {},
publisher = {Curran Associates, Inc.},
title = {Without-Replacement Sampling for Stochastic Gradient Methods},
url = {https://proceedings.neurips.cc/paper_files/paper/2016/file/c74d97b01eae257e44aa9d5bade97baf-Paper.pdf},
volume = {29},
year = {2016}
}