Abstract
K-DAREK is a novel extension of DAREK that introduces distance-aware error bounds for more robust function approximation. By measuring uncertainty based on the distance to training data, it produces tighter and more meaningful confidence estimates. Evaluations on safe control tasks show K-DAREK is faster, more scalable, and safer than methods like Gaussian Processes, Deep Ensembles, and earlier versions such as DAREK.
