Computationally Efficient High-Dimensional Bayesian Optimization via Variable Selection
Abstract
Bayesian Optimization (BO) is a widely-used method for the global optimization of black-box functions. While BO has been successfully applied to many scenarios, scaling BO algorithms to high-dimensional domains remains a challenge. Optimizing such functions by vanilla BO is extremely time-consuming. Alternative strategies for high-dimensional BO that are based on the idea of embedding the high-dimensional space to one with low dimensions are sensitive to the choice of the embedding dimension, which needs to be pre-specified. We develop a new computationally efficient high-dimensional BO method that leverages variable selection. We analyze the computational complexity of our algorithm and demonstrate its efficacy on several synthetic and real problems through empirical evaluations.
Cite
Text
Shen and Kingsford. "Computationally Efficient High-Dimensional Bayesian Optimization via Variable Selection." Proceedings of the Second International Conference on Automated Machine Learning, 2023.Markdown
[Shen and Kingsford. "Computationally Efficient High-Dimensional Bayesian Optimization via Variable Selection." Proceedings of the Second International Conference on Automated Machine Learning, 2023.](https://mlanthology.org/automl/2023/shen2023automl-computationally/)BibTeX
@inproceedings{shen2023automl-computationally,
title = {{Computationally Efficient High-Dimensional Bayesian Optimization via Variable Selection}},
author = {Shen, Yihang and Kingsford, Carl},
booktitle = {Proceedings of the Second International Conference on Automated Machine Learning},
year = {2023},
pages = {15/1-27},
volume = {224},
url = {https://mlanthology.org/automl/2023/shen2023automl-computationally/}
}