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Large-Scale Gaussian Processes via Alternating Projection
March 12, 2024, 4:44 a.m. | Kaiwen Wu, Jonathan Wenger, Haydn Jones, Geoff Pleiss, Jacob R. Gardner
cs.LG updates on arXiv.org arxiv.org
Abstract: Training and inference in Gaussian processes (GPs) require solving linear systems with $n\times n$ kernel matrices. To address the prohibitive $\mathcal{O}(n^3)$ time complexity, recent work has employed fast iterative methods, like conjugate gradients (CG). However, as datasets increase in magnitude, the kernel matrices become increasingly ill-conditioned and still require $\mathcal{O}(n^2)$ space without partitioning. Thus, while CG increases the size of datasets GPs can be trained on, modern datasets reach scales beyond its applicability. In this …
abstract arxiv become complexity cs.lg datasets gaussian processes gps however inference iterative kernel linear processes projection scale stat.ml systems training type via work
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