Asynchronous Batch Bayesian Optimisation with Improved Local Penalisation
Abstract
Batch Bayesian optimisation (BO) has been successfully applied to hyperparameter tuning using parallel computing, but it is wasteful of resources: workers that complete jobs ahead of others are left idle. We address this problem by developing an approach, Penalising Locally for Asynchronous Bayesian Optimisation on K Workers (PLAyBOOK), for asynchronous parallel BO. We demonstrate empirically the efficacy of PLAyBOOK and its variants on synthetic tasks and a real-world problem. We undertake a comparison between synchronous and asynchronous BO, and show that asynchronous BO often outperforms synchronous batch BO in both wall-clock time and sample efficiency.
Cite
Text
Alvi et al. "Asynchronous Batch Bayesian Optimisation with Improved Local Penalisation." International Conference on Machine Learning, 2019.Markdown
[Alvi et al. "Asynchronous Batch Bayesian Optimisation with Improved Local Penalisation." International Conference on Machine Learning, 2019.](https://mlanthology.org/icml/2019/alvi2019icml-asynchronous/)BibTeX
@inproceedings{alvi2019icml-asynchronous,
title = {{Asynchronous Batch Bayesian Optimisation with Improved Local Penalisation}},
author = {Alvi, Ahsan and Ru, Binxin and Calliess, Jan-Peter and Roberts, Stephen and Osborne, Michael A.},
booktitle = {International Conference on Machine Learning},
year = {2019},
pages = {253-262},
volume = {97},
url = {https://mlanthology.org/icml/2019/alvi2019icml-asynchronous/}
}