Computationally Efficient High-Dimensional Bayesian Optimization via Variable Selection

Yihang Shen, Carl Kingsford

Keywords: Bayesian Optimization, variable selection, gaussian process

TL;DR: This paper describes a new method for high-dimensional bayesian optimization.

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.

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CPU Hours: 100

GPU Hours: 0

TPU Hours: 0

Evaluation Metrics: Yes

Community Implementations: [ 6 code implementations](https://www.catalyzex.com/paper/computationally-efficient-high-dimensional/code)