用最大化多臂老虎机优化自动机器学习中的模型选择与超参调优
Put CASH on Bandits: A Max K-Armed Problem for Automated Machine Learning
- 提出MaxUCB算法，平衡模型类探索与超参调优
- 在四个基准上表现优于现有方法，提升效率与效果
- 适合需要高效自动调参的机器学习工程人员
组合算法选择与超参数优化（CASH）是自动机器学习中一项具有挑战性的资源分配问题。本文提出MaxUCB，一种针对该场景中轻尾且有界的奖励分布设计的最大化k臂老虎机方法，可在不同模型类别间探索与超参数优化之间实现高效权衡。相较于假设重尾奖励分布的经典方法，MaxUCB更具适应性。我们在四个标准AutoML基准上进行了理论与实证评估，结果表明其性能显著优于先前方法。代码与数据已公开于https://github.com/amirbalef/CASH_with_Bandits。
原文摘要 · Abstract (English)
The Combined Algorithm Selection and Hyperparameter optimization (CASH) is a challenging resource allocation problem in the field of AutoML. We propose MaxUCB, a max k-armed bandit method to trade off exploring different model classes and conducting hyperparameter optimization. MaxUCB is specifically designed for the light-tailed and bounded reward distributions arising in this setting and, thus, provides an efficient alternative compared to classic max k-armed bandit methods assuming heavy-tailed reward distributions. We theoretically and empirically evaluate our method on four standard AutoML benchmarks, demonstrating superior performance over prior approaches. We make our code and data available at https://github.com/amirbalef/CASH_with_Bandits
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