多任务贝叶斯优化中如何保证安全约束的满足
An Analysis of Safety Guarantees in Multi-Task Bayesian Optimization
- 用未知相关矩阵建模信息源间依赖，动态调整误差界
- 实验显示样本效率显著提升，适用于昂贵函数优化
- 兼顾贝叶斯与频率学派视角，适合工业级安全敏感场景
本文研究在贝叶斯优化框架中引入额外信息源的同时保障安全约束的方法。通过未知相关矩阵建模这些信息源之间的依赖关系，探讨如何调整统一误差界以在整个优化过程中维持约束满足，从贝叶斯和频率学派两个统计视角出发。通过基于数据估计的置信区间，对误差界进行合理缩放。实验在两个基准函数和一个控制器参数优化问题上验证了该方法的有效性，结果表明在保持安全约束的前提下，样本效率显著提升，证明其适用于高成本评估函数的优化。
原文摘要 · Abstract (English)
This paper addresses the integration of additional information sources into a Bayesian optimization framework while ensuring that safety constraints are satisfied. The interdependencies between these information sources are modeled using an unknown correlation matrix. We explore how uniform error bounds must be adjusted to maintain constraint satisfaction throughout the optimization process, considering both Bayesian and frequentist statistical perspectives. This is achieved by appropriately scaling the error bounds based on a confidence interval that can be estimated from the data. Furthermore, the efficacy of the proposed approach is demonstrated through experiments on two benchmark functions and a controller parameter optimization problem. Our results highlight a significant improvement in sample efficiency, demonstrating the methods suitability for optimizing expensive-to-evaluate functions.
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