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 both scalable and exact. Our key innovation is constructing a naturally data-sparse covariance matrix with adaptive multi-resolution basis functions. These basis functions are directly anchored to samples, eliminating the need for auxiliary points. By shrinking the support domains of multi-resolution basis, the matrix block sizes are limited, guaranteeing sparsity. The inverse of the data-spar...
| Authors | Yanchuang Cao, Jun Liu, Tengchao Yu et al. |
|---|---|
| Primary category | stat.ML |
| Published | 24 September 2026 |
| Last updated at source | 24 September 2026 |
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