论文标题
小样本试验中完全贝叶斯优化的情况
The case for fully Bayesian optimisation in small-sample trials
论文作者
论文摘要
尽管当黑盒功能昂贵时，样品效率是使用贝叶斯优化的主要动机，但基于II型最大可能性（ML-II）的标准方法可能会失败，并在小样本试验中导致令人失望的性能。该论文提供了三个令人信服的理由，将完全贝叶斯优化（FBO）作为替代方案。首先，ML-II的失败比使用人为设置的现有研究所隐含的更为普遍。其次，FBO比ML-II更健壮，而且健壮性的价格几乎是微不足道的。第三，FBO变得易于实施，并且足够快，可以实用。本文使用相关实验支持该论点，这些实验反映了有关模型，算法和软件平台的当前实践。由于收益似乎超过了成本，研究人员应考虑为其应用采用FBO，以便他们可以防止可能浪费宝贵的研究资源的潜在失败。
While sample efficiency is the main motive for use of Bayesian optimisation when black-box functions are expensive to evaluate, the standard approach based on type II maximum likelihood (ML-II) may fail and result in disappointing performance in small-sample trials. The paper provides three compelling reasons to adopt fully Bayesian optimisation (FBO) as an alternative. First, failures of ML-II are more commonplace than implied by the existing studies using the contrived settings. Second, FBO is more robust than ML-II, and the price of robustness is almost trivial. Third, FBO has become simple to implement and fast enough to be practical. The paper supports the argument using relevant experiments, which reflect the current practice regarding models, algorithms, and software platforms. Since the benefits seem to outweigh the costs, researchers should consider adopting FBO for their applications so that they can guard against potential failures that end up wasting precious research resources.
