Put CASH on Bandits: A Max K-Armed Problem for Automated Machine Learning
This addresses a resource allocation challenge in automated machine learning, offering an efficient solution for practitioners, though it appears incremental as it builds on existing bandit methods.
The paper tackles the Combined Algorithm Selection and Hyperparameter optimization (CASH) problem in AutoML by proposing MaxUCB, a max k-armed bandit method designed for light-tailed and bounded reward distributions, demonstrating superior performance on four standard benchmarks.
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