3 papers
math.NA2025
A gradient-based and determinant-free framework for fully Bayesian Gaussian process regression
P. Michael Kielstra, Michael Lindsey
Gaussian Process Regression (GPR) is widely used for inferring functions from noisy data. GPR crucially relies on the choice of a kernel, which might be specified in terms of a col…
math.NA2025
Gaussian process regression with log-linear scaling for common non-stationary kernels
P. Michael Kielstra, Michael Lindsey
We introduce a fast algorithm for Gaussian process regression in low dimensions, applicable to a widely-used family of non-stationary kernels. The non-stationarity of these kernels…
math.NA2025
A Linear-complexity Tensor Butterfly Algorithm for Compressing High-dimensional Oscillatory Integral Operators
P. Michael Kielstra, Tianyi Shi, Hengrui Luo +2
This paper presents a multilevel tensor compression algorithm called tensor butterfly algorithm for efficiently representing large-scale and high-dimensional oscillatory integral o…