解决高维组合与混合变量优化难题，提升复杂搜索空间的效率。
MOCA-HESP: Meta High-dimensional Bayesian Optimization for Combinatorial and Mixed Spaces via Hyper-ellipsoid Partitioning
- 用超椭球分区结合多编码器，适应高维组合与混合变量空间。
- 在多个真实与合成基准上超越现有方法，最优解搜索更快更准。
- 可作为元算法集成其他优化器，适合自动化机器学习等场景。
高维贝叶斯优化近年来备受关注，但现有方法主要针对连续域优化，而组合（序数与类别）和混合域仍具挑战。本文提出MOCA-HESP，一种面向组合与混合变量的新型高维贝叶斯优化方法。核心思想是利用超椭球空间划分（HESP）技术，结合不同类别编码器处理高维、组合与混合空间，并通过多臂赌博机自适应选择最优编码器。该方法设计为元算法，可集成其他组合与混合贝叶斯优化器以进一步提升性能。我们基于此构建了三种实用优化方法：标准贝叶斯优化、CASMOPOLITAN和Bounce。在多种合成与真实世界基准上的实验表明，所提方法优于现有基线。代码实现见https://github.com/LamNgo1/moca-hesp。
原文摘要 · Abstract (English)
High-dimensional Bayesian Optimization (BO) has attracted significant attention in recent research. However, existing methods have mainly focused on optimizing in continuous domains, while combinatorial (ordinal and categorical) and mixed domains still remain challenging. In this paper, we first propose MOCA-HESP, a novel high-dimensional BO method for combinatorial and mixed variables. The key idea is to leverage the hyper-ellipsoid space partitioning (HESP) technique with different categorical encoders to work with high-dimensional, combinatorial and mixed spaces, while adaptively selecting the optimal encoders for HESP using a multi-armed bandit technique. Our method, MOCA-HESP, is designed as a \textit{meta-algorithm} such that it can incorporate other combinatorial and mixed BO optimizers to further enhance the optimizers' performance. Finally, we develop three practical BO methods by integrating MOCA-HESP with state-of-the-art BO optimizers for combinatorial and mixed variables: standard BO, CASMOPOLITAN, and Bounce. Our experimental results on various synthetic and real-world benchmarks show that our methods outperform existing baselines. Our code implementation can be found at https://github.com/LamNgo1/moca-hesp
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