提出一种可扩展的批量贝叶斯优化方法，提升大规模并行计算效率。
Scalable Batch Bayesian Optimization Via Subspace Acquisition Functions
- 通过轴对齐子空间抽样，分块优化提升并行效率
- 相比串行贝叶斯优化，收敛速度显著提升
- 适用于大规模批量优化场景，适合并行计算需求
将贝叶斯优化扩展至批量评估，可充分利用并行计算资源。然而，现有大多数批量方法在批量规模增大时效率下降。本文提出一种简单高效的扩展方法，通过在原问题中抽取一系列轴对齐子空间，并在每个子空间内使用已有采集函数选择一个点。数值实验表明，该方法相比串行贝叶斯优化显著加速收敛，且在与十种现有批量贝叶斯优化算法对比中表现优异。代码已开源：https://github.com/zhandawei/SubSpace_Acquisition_Functions。
原文摘要 · Abstract (English)
Extending Bayesian optimization to batch evaluation can enable the designer to make the most use of parallel computing technology. However, most of current batch approaches do not scale well with the batch size. That is, their optimization efficiencies often deteriorate as the batch size increases. To address this issue, we propose a simple and efficient approach to extend Bayesian optimization to large-scale batch evaluation in this work. Different from existing batch approaches, the idea of the new approach is to draw a batch of axis-aligned subspaces of the original problem and select one point from each subspace using existing acquisition functions. Numerical experiments show that our proposed approach speedups the convergence significantly when compared with the sequential Bayesian optimization algorithm, and performs very competitively when compared with ten batch Bayesian optimization algorithms. The implementation of our proposed approach is available at https://github.com/zhandawei/SubSpace_Acquisition_Functions.
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