Thinking Inside the Box: A Tutorial on Grey-Box Bayesian Optimization
Thinking Inside the Box: A Tutorial on Grey-Box Bayesian Optimization
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DOI：
10.1109/wsc52266.2021.9715343
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发表时间：
2021-12
期刊：
影响因子：
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通讯作者：
Raul Astudillo;Peter I. Frazier
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文献类型：
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作者：
Raul Astudillo;Peter I. Frazier
贝叶斯优化 (BO) 是用于对评估成本高昂的目标函数进行全局优化的框架。经典的 BO 方法假设目标函数是一个黑匣子。然而，有关目标函数计算的内部信息通常是可用的。例如，在通过仿真优化生产线的吞吐量时，除了总体吞吐量之外，我们还会观察每个工作站等待的零件数量。最近的 BO 方法利用此类内部信息来显着提高性能。我们称这些“灰盒”BO 方法是因为它们将目标计算视为部分可观察甚至可修改，将黑盒方法与目标函数计算的所谓“白盒”第一原理知识相结合。本教程描述了这些方法，重点关注复合目标函数的 BO，其中人们可以观察并选择性地评估融入总体目标的各个组成部分；多保真度 BO，可以通过改变评估预言机的参数来评估目标函数的更便宜的近似值。
Bayesian optimization (BO) is a framework for global optimization of expensive-to-evaluate objective functions. Classical BO methods assume that the objective function is a black box. However, internal information about objective function computation is often available. For example, when optimizing a manufacturing line's throughput with simulation, we observe the number of parts waiting at each workstation, in addition to the overall throughput. Recent BO methods leverage such internal information to dramatically improve performance. We call these “grey-box” BO methods because they treat objective computation as partially observable and even modifiable, blending the black-box approach with so-called “white-box” first-principles knowledge of objective function computation. This tutorial describes these methods, focusing on BO of composite objective functions, where one can observe and selectively evaluate individual constituents that feed into the overall objective; and multi-fidelity BO, where one can evaluate cheaper approximations of the objective function by varying parameters of the evaluation oracle.