论文标题
在框内思考：灰色盒贝叶斯优化的教程
Thinking inside the box: A tutorial on grey-box Bayesian optimization
论文作者
论文摘要
贝叶斯优化（BO）是全球优化昂贵评估目标功能的框架。经典的BO方法假设目标函数是黑匣子。但是，有关目标函数计算的内部信息通常可用。例如，当通过模拟优化制造线的吞吐量时，除了整体吞吐量外，我们还会观察到每个工作站等待的零件数量。最近的BO方法利用这种内部信息可以大大提高性能。我们之所以称其为“灰色” BO方法，是因为它们将目标计算视为可观察到的，甚至可以修改，将黑框方法与所谓的“白框”融合了目标函数计算的第一原理知识。本教程描述了这些方法，重点是复合目标函数的BO，可以观察并有选择地评估源入整体目标的个体成分；和多保真bo，可以通过不同的评估参数来评估目标函数的近似值。
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.
