Statistics seminar

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.