@inproceedings{wu2021chacha,
author = {Wu, Qingyun and Wang, Chi and Langford, John and Mineiro, Paul and Rossi, Marco},
title = {ChaCha for Online AutoML},
booktitle = {2021 International Conference on Machine Learning (ICML 2021)},
year = {2021},
month = {July},
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.},
url = {http://approjects.co.za/?big=en-us/research/publication/chacha-for-online-automl/},
}