论文标题
Hebo推动样品高效参数优化的限制
HEBO Pushing The Limits of Sample-Efficient Hyperparameter Optimisation
论文作者
论文摘要
在这项工作中，我们严格地分析了黑框优化超参数调整任务固有的假设。我们在贝叶标准基准上的结果表明，异质性和非平稳性对黑盒优化剂构成了重大挑战。基于这些发现，我们提出了异质和进化的贝叶斯优化求解器（HEBO）。 Hebo执行非线性输入和输出翘曲，接受确切的边缘对数 - 样式优化，并且对学习参数的值很强。我们在2020年黑盒优化挑战中展示了Hebo在Hebo排名第一的Neurips Black-Box优化挑战上的经验功效。经过进一步的分析，我们观察到HEBO在108机器学习超参数调谐任务上的现有黑盒优化器的表现显着胜过包括贝内斯标准的基准。我们的发现表明，大多数高参数调谐任务表现出异质性和非平稳性，具有帕累托前溶液的多目标采集集合可以改善查询配置，并具有强大的获取最大值最大化器，可提供相对于非强制性对手的经验优势。我们希望这些发现可以作为贝叶斯优化从业者的指导原则。所有代码均可在https://github.com/huawei-noah/hebo上提供。
In this work we rigorously analyse assumptions inherent to black-box optimisation hyper-parameter tuning tasks. Our results on the Bayesmark benchmark indicate that heteroscedasticity and non-stationarity pose significant challenges for black-box optimisers. Based on these findings, we propose a Heteroscedastic and Evolutionary Bayesian Optimisation solver (HEBO). HEBO performs non-linear input and output warping, admits exact marginal log-likelihood optimisation and is robust to the values of learned parameters. We demonstrate HEBO's empirical efficacy on the NeurIPS 2020 Black-Box Optimisation challenge, where HEBO placed first. Upon further analysis, we observe that HEBO significantly outperforms existing black-box optimisers on 108 machine learning hyperparameter tuning tasks comprising the Bayesmark benchmark. Our findings indicate that the majority of hyper-parameter tuning tasks exhibit heteroscedasticity and non-stationarity, multi-objective acquisition ensembles with Pareto front solutions improve queried configurations, and robust acquisition maximisers afford empirical advantages relative to their non-robust counterparts. We hope these findings may serve as guiding principles for practitioners of Bayesian optimisation. All code is made available at https://github.com/huawei-noah/HEBO.
