Probability-Generating Function Kernels for Spherical Data

Abstract: In this talk, I will introduce the class of probability-generating function (PGF) kernels, a novel approach to spherical data analysis. PGF kernels generalize radial basis function (RBF) kernels and are supported on the unit hypersphere, making them well-suited for tasks involving spherical data. I will discuss their unique properties, demonstrate a semi-parametric learning algorithm for fitting these kernels, and showcase their application in Gaussian processes and deep kernel learning. Through examples and comparisons, I will highlight the advantages of PGF kernels over existing methods.

machine learningprobabilitystatistics theory

Audience: researchers in the discipline

( paper )

Gothenburg statistics seminar

Series comments: Gothenburg statistics seminar is open to the interested public, everybody is welcome. It usually takes place in MVL14 (http://maps.chalmers.se/#05137ad7-4d34-45e2-9d14-7f970517e2b60, see specific talk). Speakers are asked to prepare material for 35 minutes excluding questions from the audience.