NeurIPS 2018poster120 citations
Online Adaptive Methods, Universality and Acceleration
Kfir Y. Levy, Alp Yurtsever, Volkan Cevher
Abstract
We present a novel method for convex unconstrained optimization that, without any modifications ensures: (1) accelerated convergence rate for smooth objectives, (2) standard convergence rate in the general (non-smooth) setting, and (3) standard convergence rate in the stochastic optimization setting.
BibTeX
@inproceedings{NEURIPS2018_b0169350,
author = {Levy, Kfir Y. and Yurtsever, Alp and Cevher, Volkan},
booktitle = {Advances in Neural Information Processing Systems},
editor = {S. Bengio and H. Wallach and H. Larochelle and K. Grauman and N. Cesa-Bianchi and R. Garnett},
pages = {},
publisher = {Curran Associates, Inc.},
title = {Online Adaptive Methods, Universality and Acceleration},
url = {https://proceedings.neurips.cc/paper_files/paper/2018/file/b0169350cd35566c47ba83c6ec1d6f82-Paper.pdf},
volume = {31},
year = {2018}
}