Adaptive multi-resolution Gaussian processes: Scalable exact inference with naturally data-sparse covariance matrices
Theoretical analysis and numerical experiments demonstrate that the model achieves exact inference with $\mathcal{O}(n \log^2 n)$ training cost and $\mathcal{O}(\log^d n)$ prediction cost, establishing a principled framework for scalable and high-fidelity Gaussian process regression.
Yan-Chuang Cao, Jun Liu, Teng-Chao Yu et al.
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