189 citations · 763 across the 33 of their papers we have counts for
4 papers · 1 filter
Bayesian nonparametric generative modeling of large multivariate non-Gaussian spatial fields
Paul F. V. Wiemann, Matthias Katzfuss
Multivariate spatial fields are of interest in many applications, including climate model emulation. Not only can the marginal spatial fields be subject to nonstationarity, but the…
Sparse inverse Cholesky factorization of dense kernel matrices by greedy conditional selection
Stephen Huan, Joseph Guinness, Matthias Katzfuss +2
Dense kernel matrices resulting from pairwise evaluations of a kernel function arise naturally in machine learning and statistics. Previous work in constructing sparse approximate…
Vecchia Gaussian Process Ensembles on Internal Representations of Deep Neural Networks
Felix Jimenez, Matthias Katzfuss
For regression tasks, standard Gaussian processes (GPs) provide natural uncertainty quantification (UQ), while deep neural networks (DNNs) excel at representation learning. Determi…
Variational sparse inverse Cholesky approximation for latent Gaussian processes via double Kullback-Leibler minimization
Jian Cao, Myeongjong Kang, Felix Jimenez +3
To achieve scalable and accurate inference for latent Gaussian processes, we propose a variational approximation based on a family of Gaussian distributions whose covariance matric…