Hyperparameter optimization (HPO) is a crucial component of AutoML. The recent development of multi-fidelity optimization approaches are shown to be more efficient and powerful than existing black-box optimization methods. However, the performance of an HPO algorithm heavily depends on the HPO problem that it is applied to, i.e. the machine learning model to be evaluated, … Continue reading HPOBench: Reproducible Benchmarks for HPO