Stagewise Safe Bayesian Optimization with Gaussian Processes

Yanan Sui, Vincent Zhuang, Joel Burdick, Yisong Yue
Proceedings of the 35th International Conference on Machine Learning, PMLR 80:4781-4789, 2018.

Abstract

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v80-sui18a, title = {Stagewise Safe {B}ayesian Optimization with {G}aussian Processes}, author = {Sui, Yanan and Zhuang, Vincent and Burdick, Joel and Yue, Yisong}, booktitle = {Proceedings of the 35th International Conference on Machine Learning}, pages = {4781--4789}, year = {2018}, editor = {Dy, Jennifer and Krause, Andreas}, volume = {80}, series = {Proceedings of Machine Learning Research}, month = {10--15 Jul}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v80/sui18a/sui18a.pdf}, url = {https://proceedings.mlr.press/v80/sui18a.html}, abstract = {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.} }
Endnote
%0 Conference Paper %T Stagewise Safe Bayesian Optimization with Gaussian Processes %A Yanan Sui %A Vincent Zhuang %A Joel Burdick %A Yisong Yue %B Proceedings of the 35th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2018 %E Jennifer Dy %E Andreas Krause %F pmlr-v80-sui18a %I PMLR %P 4781--4789 %U https://proceedings.mlr.press/v80/sui18a.html %V 80 %X 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.
APA
Sui, Y., Zhuang, V., Burdick, J. & Yue, Y.. (2018). Stagewise Safe Bayesian Optimization with Gaussian Processes. Proceedings of the 35th International Conference on Machine Learning, in Proceedings of Machine Learning Research 80:4781-4789 Available from https://proceedings.mlr.press/v80/sui18a.html.

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