English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Gaussian process regression is a classical kernel method for functionestimation and data interpolation. In large data applications, computationalcosts can be reduced using low-rank or sparse approximations of the kernel.This paper investigates the effect of such kernel approximations on theinterpolation error. We introduce a unified framework to analyze Gaussianprocess regression under important classes of computational misspecification:Karhunen-Lo\`eve expansions that result in low-rank kernel approximations,multiscale wavelet expansions that induce sparsity in the covariance matrix,and finite element representations that induce sparsity in the precisionmatrix. Our theory also accounts for epistemic misspecification in the choiceof kernel parameters.

Gaussian Process Regression under Computational and Epistemic Misspecification