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SUMMARY:Alexey Lindo (University of Glasgow)
DTSTART:20250115T121500Z
DTEND:20250115T130000Z
DTSTAMP:20260422T155319Z
UID:gbgstats/78
DESCRIPTION:Title: <a href="https://researchseminars.org/talk/gbgstats/78/
 ">Probability-Generating Function Kernels for Spherical Data</a>\nby Alexe
 y Lindo (University of Glasgow) as part of Gothenburg statistics seminar\n
 \nLecture held in MVL14.\n\nAbstract\nIn 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 pro
 perties\, demonstrate a semi-parametric learning algorithm for fitting the
 se 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.\n
LOCATION:https://researchseminars.org/talk/gbgstats/78/
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