CCM Colloquium: Philip Greengard (Columbia University)

Wednesday Nov 3, 2021, 10:00 AM → 11:15 AM America/New_York

Title: Efficient Gaussian process regression via function-space representations

Abstract: 
Over the last couple of decades a large number of numerical methods have been introduced for efficiently performing Gaussian process regression. Most of these methods focus on fast inversion of the covariance matrix that appears in the Gaussian density. In this talk I describe a slightly different approach to Gaussian process regression that relies on efficient function-space representations of Gaussian processes. These representations — fixed basis functions with Gaussian coefficients — have several substantial advantages in Gaussian process regression tasks including computational and model-interpretability benefits.

If you would like to attend, please email crampersad@flatironinstitute.org for the Zoom link.