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SUMMARY:Aretha Teckentrup (University of Edinburgh)
DTSTART:20210608T140000Z
DTEND:20210608T150000Z
DTSTAMP:20260423T034449Z
UID:DSCSS/21
DESCRIPTION:Title: <a href="https://researchseminars.org/talk/DSCSS/21/">C
 onvergence\, Robustness and Flexibility of Gaussian Process Regression</a>
 \nby Aretha Teckentrup (University of Edinburgh) as part of Data Science a
 nd Computational Statistics Seminar\n\n\nAbstract\nWe are interested in th
 e task of estimating an unknown function from a set of point evaluations. 
 In this context\, Gaussian process regression is often used as a Bayesian 
 inference procedure. However\, hyper-parameters appearing in the mean and 
 covariance structure of the Gaussian process prior\, such as smoothness of
  the function and typical length scales\, are often unknown and learnt fro
 m the data\, along with the posterior mean and covariance.\n\nIn the first
  part of the talk\, we will study the robustness of Gaussian process regre
 ssion with respect to mis-specification of the hyper-parameters\, and prov
 ide a convergence analysis of the method applied to a fixed\, unknown func
 tion of interest [1].\n\nIn the second part of the talk\, we discuss deep 
 Gaussian processes as a class of flexible non-stationary prior distributio
 ns [2].\n\n[1] A.L. Teckentrup. Convergence of Gaussian process regression
  with estimated hyper-parameters and applications in Bayesian inverse prob
 lems. SIAM/ASA Journal on Uncertainty Quantification\, 8(4)\, p. 1310-1337
 \, 2020.\n\n[2] M.M. Dunlop\, M.A. Girolami\, A.M. Stuart\, A.L. Teckentru
 p. How deep are deep Gaussian processes? Journal of Machine Learning Resea
 rch\, 19(54)\, 1-46\, 2018.\n
LOCATION:https://researchseminars.org/talk/DSCSS/21/
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