A Light-Weight Multi-Objective Asynchronous Hyper-Parameter Optimizer
Machine Learning
2022-09-09 v2
Abstract
We describe a light-weight yet performant system for hyper-parameter optimization that approximately minimizes an overall scalar cost function that is obtained by combining multiple performance objectives using a target-priority-limit scalarizer. It also supports a trade-off mode, where the goal is to find an appropriate trade-off among objectives by interacting with the user. We focus on the common scenario where there are on the order of tens of hyper-parameters, each with various attributes such as a range of continuous values, or a finite list of values, and whether it should be treated on a linear or logarithmic scale. The system supports multiple asynchronous simulations and is robust to simulation stragglers and failures.
Cite
@article{arxiv.2202.07735,
title = {A Light-Weight Multi-Objective Asynchronous Hyper-Parameter Optimizer},
author = {Gabriel Maher and Stephen Boyd and Mykel Kochenderfer and Cristian Matache and Dylan Reuter and Alex Ulitsky and Slava Yukhymuk and Leonid Kopman},
journal= {arXiv preprint arXiv:2202.07735},
year = {2022}
}