论文标题
宏观运动优化非米诺的高斯工艺
Nonmyopic Gaussian Process Optimization with Macro-Actions
论文作者
论文摘要
本文提出了一种多阶段的方法，用于非近乎自适应的高斯工艺优化（GPO），以实现未知，高度复杂的目标函数的贝叶斯优化（BO），与现有的非Myopic自适应BO算法相比，这些方法利用了宏观效果的现有非媒体自适应BO算法，以提高宏观的概念，以扩大范围，从而使可观的预算更加可用。为了实现这一目标，我们将限制为新的获取函数定义的W.R.T.的GP上限限制。一种非反应的自适应宏观动作策略，由于一组无数的候选输出，它可以准确地优化。因此，我们在这里的工作的贡献是为了得出一项非近乎自适应的Epsilon-bayes-bayes-最佳宏观动作GPO（Epsilon-Macro-GPO）政策。为了实时执行非主管自适应BO，我们随时提出了我们Epsilon-Macro-GPO策略的渐近最佳选择，并提供性能保证。我们从经验上评估了Epsilon-Macro-GPO政策的性能及其在BO中的任何时间与合成和现实世界数据集的变体。
This paper presents a multi-staged approach to nonmyopic adaptive Gaussian process optimization (GPO) for Bayesian optimization (BO) of unknown, highly complex objective functions that, in contrast to existing nonmyopic adaptive BO algorithms, exploits the notion of macro-actions for scaling up to a further lookahead to match up to a larger available budget. To achieve this, we generalize GP upper confidence bound to a new acquisition function defined w.r.t. a nonmyopic adaptive macro-action policy, which is intractable to be optimized exactly due to an uncountable set of candidate outputs. The contribution of our work here is thus to derive a nonmyopic adaptive epsilon-Bayes-optimal macro-action GPO (epsilon-Macro-GPO) policy. To perform nonmyopic adaptive BO in real time, we then propose an asymptotically optimal anytime variant of our epsilon-Macro-GPO policy with a performance guarantee. We empirically evaluate the performance of our epsilon-Macro-GPO policy and its anytime variant in BO with synthetic and real-world datasets.
