Inspired by the increasing desire to efficiently tune machine learning hyper-parameters, in this work we rigorously analyse conventional and non-conventional assumptions inherent to Bayesian optimisation. Across an extensive set of experiments we conclude that: 1) the majority of hyper-parameter tuning tasks exhibit heteroscedasticity and non-stationarity, 2) multi-objective acquisition ensembles with Pareto-front solutions significantly improve queried configurations, and 3) robust acquisition maximisation affords empirical advantages relative to its non-robust counterparts. We hope these findings may serve as guiding principles, both for practitioners and for further research in the field.

翻译：由于人们越来越希望有效地调和机器学习超参数,因此,在这项工作中,我们严格分析贝叶斯最优化所固有的常规和非常规假设。在一系列广泛的实验中,我们的结论是:(1) 大部分超参数调试任务表现出异质性和不常态性;(2) 与Pareto前沿解决方案组合的多目标收购大大改进了质询配置;(3) 强有力的收购最大化与其非紫外对应方相比具有经验优势。 我们希望这些结论可以作为实践者和实地进一步研究的指导原则。