Active Calibration of Reachable Sets Using Approximate Pick-to-Learn

Reachability computations that rely on approximate or learned models require calibration in order to uphold confidence about their guarantees. Calibration generally involves sampling scenarios inside the reachable set. However, producing reasonable probabilistic guarantees may require many samples,