论文标题
使用内点方法的安全贝叶斯优化 - 应用于个性化的胰岛素剂量指导
Safe Bayesian Optimization using Interior-Point Methods -- Applied to Personalized Insulin Dose Guidance
论文作者
论文摘要
本文考虑了针对具有安全性关键约束的系统的贝叶斯优化的问题，在该系统中，目标函数和约束都未知，但是可以通过查询系统来观察。在关键安全应用中，在不可行的点查询系统可能会带来灾难性的后果。这样的系统需要一个安全的学习框架，以便可以优化性能目标，同时满足概率很高的安全性限制。在本文中，我们提出了一个安全的贝叶斯优化框架，以确保查询点始终位于部分揭示的安全区域的内部，从而确保对高概率的限制满意度。提出的内点贝叶斯优化框架可以与任何采集功能一起使用，从而广泛适用。使用个性化胰岛素剂量施用1型糖尿病患者的个性化胰岛素剂量应用，证明了该方法的性能。
This paper considers the problem of Bayesian optimization for systems with safety-critical constraints, where both the objective function and the constraints are unknown, but can be observed by querying the system. In safety-critical applications, querying the system at an infeasible point can have catastrophic consequences. Such systems require a safe learning framework, such that the performance objective can be optimized while satisfying the safety-critical constraints with high probability. In this paper we propose a safe Bayesian optimization framework that ensures that the points queried are always in the interior of the partially revealed safe region, thereby guaranteeing constraint satisfaction with high probability. The proposed interior-point Bayesian optimization framework can be used with any acquisition function, making it broadly applicable. The performance of the proposed method is demonstrated using a personalized insulin dosing application for patients with type 1 diabetes.
