ICML 2019oral20 citations
Multiplicative Weights Updates as a distributed constrained optimization algorithm: Convergence to second-order stationary points almost always
Ioannis Panageas, Georgios Piliouras, Xiao Wang
Abstract
Non-concave maximization has been the subject of much recent study in the optimization and machine learning communities, specifically in deep learning. Recent papers ([Ge et al. 2015, Lee et al 2017] and references therein) indicate that first order methods work well and avoid saddles points. Results as in [Lee \etal 2017], however, are limited to the
BibTeX
@InProceedings{pmlr-v97-panageas19a,
title = {Multiplicative Weights Updates as a distributed constrained optimization algorithm: Convergence to second-order stationary points almost always},
author = {Panageas, Ioannis and Piliouras, Georgios and Wang, Xiao},
booktitle = {Proceedings of the 36th International Conference on Machine Learning},
pages = {4961--4969},
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/panageas19a/panageas19a.pdf},
url = {https://proceedings.mlr.press/v97/panageas19a.html},
abstract = {Non-concave maximization has been the subject of much recent study in the optimization and machine learning communities, specifically in deep learning. Recent papers ([Ge et al. 2015, Lee et al 2017] and references therein) indicate that first order methods work well and avoid saddles points. Results as in [Lee \etal 2017], however, are limited to the