Hyperparameters are pivotal for machine learning models. The success of efficient calibration, often surpasses the results obtained by devising new approaches. Traditionally, human intervention is req
Enhancing AI assurance in tuning configurations and hyper-parameters of AI algorithms is an important problem in many applications. This work provides an experimental design method to address this cha
