NeurIPS 2018poster165 citations
NEON2: Finding Local Minima via First-Order Oracles
Abstract
We propose a reduction for non-convex optimization that can (1) turn an stationary-point finding algorithm into an local-minimum finding one, and (2) replace the Hessian-vector product computations with only gradient computations. It works both in the stochastic and the deterministic settings, without hurting the algorithm's performance.
BibTeX
@inproceedings{NEURIPS2018_d4b2aeb2,
author = {Allen-Zhu, Zeyuan and Li, Yuanzhi},
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 = {NEON2: Finding Local Minima via First-Order Oracles},
url = {https://proceedings.neurips.cc/paper_files/paper/2018/file/d4b2aeb2453bdadaa45cbe9882ffefcf-Paper.pdf},
volume = {31},
year = {2018}
}