ChaCha for Online AutoML
Machine Learning
2021-06-14 v2
Abstract
We propose the ChaCha (Champion-Challengers) algorithm for making an online choice of hyperparameters in online learning settings. ChaCha handles the process of determining a champion and scheduling a set of `live' challengers over time based on sample complexity bounds. It is guaranteed to have sublinear regret after the optimal configuration is added into consideration by an application-dependent oracle based on the champions. Empirically, we show that ChaCha provides good performance across a wide array of datasets when optimizing over featurization and hyperparameter decisions.
Keywords
Cite
@article{arxiv.2106.04815,
title = {ChaCha for Online AutoML},
author = {Qingyun Wu and Chi Wang and John Langford and Paul Mineiro and Marco Rossi},
journal= {arXiv preprint arXiv:2106.04815},
year = {2021}
}
Comments
16 pages (including supplementary appendix). Appearing at ICML 2021