Pointwise uncertainty quantification for sparse variational Gaussian process regression with a Brownian motion prior
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Abstract (arXiv)
We study pointwise estimation and uncertainty quantification for a sparse variational Gaussian process method with eigenvector inducing variables. For a rescaled Brownian motion prior, we derive theoretical guarantees and limitations for the frequentist size and coverage of pointwise credible sets. For sufficiently many inducing variables, we precisely characterize the asymptotic frequentist coverage, deducing when credible sets from this variational method are conservative and when overconfident/misleading. We numerically illustrate the applicability of our results and discuss connections with other common Gaussian process priors.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 18 of its 42 references that are in the map. First posted on arXiv 29 Sep 2023.
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18 of 42 references.
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6 more
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Citing papers per year: 2023: 1.
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