Uncertainty-Aware Selection of Online Algorithms with Simulator Ensembles
Uncertainty-Aware selection is investigated, which forms an ensemble of simulators and selects the online algorithm with the best average performance across the ensemble and leads to more reliable selection and improved online performance.