Stagewise Safe Bayesian Optimization with Gaussian Processes
Stagewise Safe Bayesian Optimization with Gaussian Processes
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发表时间：
2018-06
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通讯作者：
Yanan Sui;Vincent Zhuang;J. Burdick;Yisong Yue
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作者：
Yanan Sui;Vincent Zhuang;J. Burdick;Yisong Yue
加强安全性是与不确定性下的顺序决策相关的许多问题的一个关键方面，这要求每一步做出的决策既能提供最佳决策的信息，又能保证安全。例如，我们重视医疗治疗的有效性和舒适性，以及机器人控制的效率和安全性。我们考虑在未知安全约束下用绝对反馈或偏好反馈优化未知效用函数的问题。我们开发了一种高效的安全贝叶斯优化算法 StageOpt，它将安全区域扩展和效用函数最大化分为两个不同的阶段。与现有的扩展和优化交错的方法相比，我们表明 StageOpt 更有效，并且自然适用于更广泛的问题。我们为满足安全约束以及收敛到最优效用值提供了理论保证。我们在各种合成实验以及临床实践中评估 StageOpt。我们证明 StageOpt 比现有的安全优化方法更有效，并且能够在临床实验中安全有效地优化脊髓刺激治疗。
Enforcing safety is a key aspect of many problems pertaining to sequential decision making under uncertainty, which require the decisions made at every step to be both informative of the optimal decision and also safe. For example, we value both efficacy and comfort in medical therapy, and efficiency and safety in robotic control. We consider this problem of optimizing an unknown utility function with absolute feedback or preference feedback subject to unknown safety constraints. We develop an efficient safe Bayesian optimization algorithm, StageOpt, that separates safe region expansion and utility function maximization into two distinct stages. Compared to existing approaches which interleave between expansion and optimization, we show that StageOpt is more efficient and naturally applicable to a broader class of problems. We provide theoretical guarantees for both the satisfaction of safety constraints as well as convergence to the optimal utility value. We evaluate StageOpt on both a variety of synthetic experiments, as well as in clinical practice. We demonstrate that StageOpt is more effective than existing safe optimization approaches, and is able to safely and effectively optimize spinal cord stimulation therapy in our clinical experiments.