NeurIPS 2022accept25 citations
Reproducibility in Optimization: Theoretical Framework and Limits
Kwangjun Ahn, Prateek Jain, Ziwei Ji, Satyen Kale, Praneeth Netrapalli, Gil I. Shamir
Abstract
We initiate a formal study of reproducibility in optimization. We define a quantitative measure of reproducibility of optimization procedures in the face of noisy or error-prone operations such as inexact or stochastic gradient computations or inexact initialization. We then analyze several convex optimization settings of interest such as smooth, non-smooth, and strongly-convex objective functions and establish tight bounds on the limits of reproducibility in each setting. Our analysis reveals a fundamental trade-off between computation and reproducibility: more computation is necessary (and sufficient) for better reproducibility.
reproducibilityfirst-order optimizationconvex optimizationinexact gradient oracles
BibTeX
@inproceedings{
ahn2022reproducibility,
title={Reproducibility in Optimization: Theoretical Framework and Limits},
author={Kwangjun Ahn and Prateek Jain and Ziwei Ji and Satyen Kale and Praneeth Netrapalli and Gil I. Shamir},
booktitle={Advances in Neural Information Processing Systems},
editor={Alice H. Oh and Alekh Agarwal and Danielle Belgrave and Kyunghyun Cho},
year={2022},
url={https://openreview.net/forum?id=3LMI8CHDb0g}
}