Think Global and Act Local: Bayesian Optimisation over High-Dimensional Categorical and Mixed Search Spaces
Abstract
High-dimensional black-box optimisation remains an important yet notoriously challenging problem. Despite the success of Bayesian optimisation methods on continuous domains, domains that are categorical, or that mix continuous and categorical variables, remain challenging. We propose a novel solution—we combine local optimisation with a tailored kernel design, effectively handling high-dimensional categorical and mixed search spaces, whilst retaining sample efficiency. We further derive convergence guarantee for the proposed approach. Finally, we demonstrate empirically that our method outperforms the current baselines on a variety of synthetic and real-world tasks in terms of performance, computational costs, or both.
Cite
Text
Wan et al. "Think Global and Act Local: Bayesian Optimisation over High-Dimensional Categorical and Mixed Search Spaces." International Conference on Machine Learning, 2021.Markdown
[Wan et al. "Think Global and Act Local: Bayesian Optimisation over High-Dimensional Categorical and Mixed Search Spaces." International Conference on Machine Learning, 2021.](https://mlanthology.org/icml/2021/wan2021icml-think/)BibTeX
@inproceedings{wan2021icml-think,
title = {{Think Global and Act Local: Bayesian Optimisation over High-Dimensional Categorical and Mixed Search Spaces}},
author = {Wan, Xingchen and Nguyen, Vu and Ha, Huong and Ru, Binxin and Lu, Cong and Osborne, Michael A.},
booktitle = {International Conference on Machine Learning},
year = {2021},
pages = {10663-10674},
volume = {139},
url = {https://mlanthology.org/icml/2021/wan2021icml-think/}
}