BOIDS通过方向线与子空间嵌入，高效解决高维黑箱优化难题
BOIDS: High-dimensional Bayesian Optimization via Incumbent-guided Direction Lines and Subspace Embeddings
- 用一维方向线引导搜索，逐轮选择最优方向提升效率
- 在多个合成与真实世界基准上优于现有最先进方法
- 适合高维优化场景，尤其适用于昂贵函数评估任务
针对昂贵的黑箱优化问题，贝叶斯优化（BO）是一种成熟且强大的解决方案。现实应用常涉及大量维度，因此提升BO在高维场景下的表现至关重要。然而，当前最先进的高维BO方法仍受维度灾难影响，亟需改进。本文提出BOIDS，一种新颖的高维贝叶斯优化算法，通过一系列一维方向线进行优化，并采用定制化的基于线的优化流程。为提升效率，引入自适应选择技术，每轮筛选出最优方向线。此外，结合子空间嵌入技术以增强高维可扩展性。我们还提供了理论分析，研究所提方法的收敛性质。大量实验结果表明，BOIDS在多种合成与真实世界基准测试中均显著优于现有基线方法。
原文摘要 · Abstract (English)
When it comes to expensive black-box optimization problems, Bayesian Optimization (BO) is a well-known and powerful solution. Many real-world applications involve a large number of dimensions, hence scaling BO to high dimension is of much interest. However, state-of-the-art high-dimensional BO methods still suffer from the curse of dimensionality, highlighting the need for further improvements. In this work, we introduce BOIDS, a novel high-dimensional BO algorithm that guides optimization by a sequence of one-dimensional direction lines using a novel tailored line-based optimization procedure. To improve the efficiency, we also propose an adaptive selection technique to identify most optimal lines for each round of line-based optimization. Additionally, we incorporate a subspace embedding technique for better scaling to high-dimensional spaces. We further provide theoretical analysis of our proposed method to analyze its convergence property. Our extensive experimental results show that BOIDS outperforms state-of-the-art baselines on various synthetic and real-world benchmark problems.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。