Adaptive multi-resolution Gaussian processes: Scalable exact inference with naturally data-sparse covariance matrices

Gaussian processes constitute a cornerstone of probabilistic machine learning, yet scaling them to large datasets typically forces a trade-off between computational efficiency and model fidelity. This work bridges this gap by presenting an adaptive multi-resolution Gaussian process framework that is