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SUMMARY:Gaussian Processes on Graphs via Spectral Kernel Learning - Yin-Co
 ng Zhi\, Oxford Man Institute
DTSTART:20211109T131500Z
DTEND:20211109T141500Z
UID:TALK165778@talks.cam.ac.uk
CONTACT:Mateja Jamnik
DESCRIPTION:"Join us on Zoom":https://zoom.us/j/99166955895?pwd=SzI0M3pMVE
 kvNmw3Q0dqNDVRalZvdz09\n\nI will go over topics on regularization\, RKHS\,
  and filtering on graphs. I will then show how I applied these methods to 
 graph signal prediction through a graph spectrum-based Gaussian process. T
 he model is designed to capture various graph signal structures through a 
 highly adaptive kernel that incorporates a flexible polynomial function in
  the graph spectral domain. I will also present a bespoke maximum likeliho
 od learning algorithm that enforces the positivity of the polynomial to ac
 hieve interpretability of filtering on graphs.\n\nBIO: Yin-Cong Zhi is a f
 ourth year DPhil student with Xiaowen Dong at the Oxford Man Institute. Hi
 s focus is in graph signal processing and kernel methods\, and utilising t
 hese tools for predictive tasks on graphs using Gaussian processes.
LOCATION:Zoom
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