3 papers
math.NA2024
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.NA2024
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…
math.CO2019
Revisiting a Cutting Plane Method for Perfect Matchings
Amber Q Chen, Kevin K. H. Cheung, P. Michael Kielstra +1
In 2016, Chandrasekaran, Végh, and Vempala published a method to solve the minimum-cost perfect matching problem on an arbitrary graph by solving a strictly polynomial number of li…