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SUMMARY:Gaussian processes\, spectral analysis kernels and optimal transpo
 rt - Felipe Tobar\, Universidad de Chile
DTSTART:20221122T110000Z
DTEND:20221122T120000Z
UID:TALK193046@talks.cam.ac.uk
CONTACT:Dr R.E. Turner
DESCRIPTION:Gaussian processes (GPs) are Bayesian nonparametric generative
  models for time series and are particularly well suited for continuous-ti
 me nonlinear regression tasks. The talk will start with a brief introducti
 on to GPs so as to illustrate their advantages and challenges as well as t
 o motivate their use in a variety of tasks involving missing or irregularl
 y-sampled data. We will then interpret the GP model from a (Fourier) spect
 ral analysis perspective and motivate the construction of covariance funct
 ions based on the GPs frequency representation\; we will also show how GPs
  can be used for Spectral Estimation. Then\, we will present recent advanc
 es using optimal transport (a distance between probability distributions) 
 to define a distance between GPs and explore alternative\, cost-efficient\
 , training strategies for GP. Throughout the talk\, we will show illustrat
 ive and real world examples. \n\nBio: Felipe is an Associate Professor at 
 the Initiative for Data and Artificial Intelligence\, Universidad de Chile
 \, and the Director of the Initiative for Data and Artificial Intelligence
  at the same institution. He holds Researcher positions at the Center for 
 Mathematical Modeling and the Advanced Center for Electrical and Electroni
 c Engineering. Prior to joining Universidad de Chile\, Felipe was a postdo
 c at the Machine Learning Group\, University of Cambridge\, during 2015 an
 d he received a PhD in Signal Processing from Imperial College London in 2
 014. Felipe's research interests lie in the interface between Machine Lear
 ning and Statistical Signal Processing\, including approximate inference\,
  Bayesian nonparametrics\, spectral estimation\, optimal transport and Gau
 ssian processes. 
LOCATION:CBL Seminar Room
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