Put CASH on Bandits: A Max K-Armed Problem for Automated Machine Learning
Abstract
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
Cite
@article{arxiv.2505.05226,
title = {Put CASH on Bandits: A Max K-Armed Problem for Automated Machine Learning},
author = {Amir Rezaei Balef and Claire Vernade and Katharina Eggensperger},
journal= {arXiv preprint arXiv:2505.05226},
year = {2025}
}
Comments
Accepted at the 39th Conference on Neural Information Processing Systems (NeurIPS 2025)