3 papers
math.ST2024
Vecchia Gaussian Processes: on probabilistic and statistical properties
Botond Szabo, Yichen Zhu
Gaussian Processes (GPs) are widely used to model dependencies in spatial statistics and machine learning. However, exact inference is computationally intractable for GP regression…
stat.ME2024
Skew-symmetric approximations of posterior distributions
Francesco Pozza, Daniele Durante, Botond Szabo
Popular deterministic approximations of posterior distributions from, e.g. the Laplace method, variational Bayes and expectation-propagation, generally rely on symmetric approximat…
stat.ML2024
Contraction rates for conjugate gradient and Lanczos approximate posteriors in Gaussian process regression
Bernhard Stankewitz, Botond Szabo
Due to their flexibility and theoretical tractability Gaussian process (GP) regression models have become a central topic in modern statistics and machine learning. While the true…