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

翻译：组合算法选择与超参数优化（CASH）是自动化机器学习领域一个具有挑战性的资源分配问题。我们提出MaxUCB方法，这是一种基于最大K臂老虎机的策略，用于权衡不同模型类别的探索与超参数优化过程。MaxUCB专门针对该场景中出现的轻尾有界奖励分布而设计，相比传统假设重尾奖励分布的最大K臂老虎机方法，提供了更高效的替代方案。我们在四个标准AutoML基准测试中从理论和实证两个维度评估了该方法，结果表明其性能优于现有方法。相关代码和数据已公开于https://github.com/amirbalef/CASH_with_Bandits