Graph Kernel Networks learn PDE solution operators that generalize across discretization methods and grid resolutions using graph-based kernel integration.
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Gaussian and Besov-Laplace priors yield minimax-optimal posterior contraction rates for nonparametric Bayesian intensity estimation in covariate-driven point processes under increasing domain asymptotics with ergodic covariates.
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Increasing domain asymptotics for covariate-based nonparametric Bayesian intensity estimation with Gaussian and Besov-Laplace priors
Gaussian and Besov-Laplace priors yield minimax-optimal posterior contraction rates for nonparametric Bayesian intensity estimation in covariate-driven point processes under increasing domain asymptotics with ergodic covariates.