ICML 2019oral3 citations
Self-similar Epochs: Value in arrangement
Eliav Buchnik, Edith Cohen, Avinatan Hasidim, Yossi Matias
Abstract
Optimization of machine learning models is commonly performed through stochastic gradient updates on randomly ordered training examples. This practice means that each fraction of an epoch comprises an independent random sample of the training data that may not preserve informative structure present in the full data. We hypothesize that the training can be more effective with
BibTeX
@InProceedings{pmlr-v97-buchnik19a,
title = {Self-similar Epochs: Value in arrangement},
author = {Buchnik, Eliav and Cohen, Edith and Hasidim, Avinatan and Matias, Yossi},
booktitle = {Proceedings of the 36th International Conference on Machine Learning},
pages = {841--850},
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/buchnik19a/buchnik19a.pdf},
url = {https://proceedings.mlr.press/v97/buchnik19a.html},
abstract = {Optimization of machine learning models is commonly performed through stochastic gradient updates on randomly ordered training examples. This practice means that each fraction of an epoch comprises an independent random sample of the training data that may not preserve informative structure present in the full data. We hypothesize that the training can be more effective with