Put CASH on Bandits: A Max K-Armed Problem for Automated Machine Learning

Claire Vernade (University of Tuebingen) · Amir Rezaei Balef (University of Tübingen) · Katharina Eggensperger (TU Dortmund University)

algorithmic efficiency automlbounded reward distributionsclassic bandit methodscombined algorithm selectionempirical evaluation heavy-tailed distributions hyperparameter optimizationlight-tailed distributionsmax k-armed banditmaxucbresource allocation standard benchmarks superior performancetheoretical evaluation

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