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Choosing the Kernel and Learning its Parameters
Felipe Tobar

Mon Dec 06 10:10 AM -- 10:25 AM (PST) @
in The Art of Gaussian Processes: Classical and Contemporary »

Building on the use of a Gaussian likelihood, we present a learning objective and refer to the problem of choosing a kernel and optimising its parameters. We revise different kernel functions and their properties.

Author Information

Felipe Tobar (Universidad de Chile)

Felipe Tobar is an Assistant Professor at the Data & AI Initiative at Universidad de Chile. He holds Researcher positions at the Center for Mathematical Modeling and the Advanced Center for Electrical Engineering. Felipe received the BSc/MSc degrees in Electrical Engineering (U. de Chile, 2010) and a PhD in Signal Processing (Imperial College London, 2014), and he was an Associate Researcher in Machine Learning at the University of Cambridge (2014-2015). Felipe teaches Statistics and Machine Learning courses at undergraduate, graduate and professional levels. His research interests lie in the interface between Machine Learning and Statistical Signal Processing, including Gaussian processes, spectral estimation, approximate inference, Bayesian nonparametrics, and optimal transport.

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