BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Talks.cam//talks.cam.ac.uk//
X-WR-CALNAME:Talks.cam
BEGIN:VEVENT
SUMMARY:Scalable Gaussian Processes - David Burt\, Andrew Foong
DTSTART:20191204T140000Z
DTEND:20191204T153000Z
UID:TALK135763@talks.cam.ac.uk
CONTACT:Robert Pinsler
DESCRIPTION:Inference and learning in models with Gaussian process (GP) pr
 iors are well-known to suffer from high computational costs\, both in term
 s of time and memory. Various methods have been proposed to allow GP  mode
 ls to scale to big data. In this reading group\, we discuss two popular te
 chniques for scaling up GP models: variational inference and conjugate gra
 dient methods.
LOCATION:Engineering Department\, CBL Room BE-438
END:VEVENT
END:VCALENDAR