13 citations · 43 across the 18 of their papers we have counts for
5 papers · 1 filter
Equispaced Fourier representations for efficient Gaussian process regression from a billion data points
Philip Greengard, Manas Rachh, Alex Barnett
We introduce a Fourier-based fast algorithm for Gaussian process regression in low dimensions. It approximates a translationally-invariant covariance kernel by complex exponentials…
Fast methods for posterior inference of two-group normal-normal models
Philip Greengard, Jeremy Hoskins, Charles C. Margossian +2
We describe a class of algorithms for evaluating posterior moments of certain Bayesian linear regression models with a normal likelihood and a normal prior on the regression coeffi…
Efficient Fourier representations of families of Gaussian processes
Philip Greengard
We introduce a class of algorithms for constructing Fourier representations of Gaussian processes in dimension that are valid over ranges of hyperparameter values. The scaling…
Efficient reduced-rank methods for Gaussian processes with eigenfunction expansions
Philip Greengard, Michael O'Neil
In this work we introduce a reduced-rank algorithm for Gaussian process regression. Our numerical scheme converts a Gaussian process on a user-specified interval to its Karhunen-Lo…
A Fast Linear Regression via SVD and Marginalization
Philip Greengard, Andrew Gelman, Aki Vehtari
We describe a numerical scheme for evaluating the posterior moments of Bayesian linear regression models with partial pooling of the coefficients. The principal analytical tool of…