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SUMMARY:Quantifying and reducing uncertainties on sets under Gaussian Proc
 ess priors - David Ginsbourger (Other\; Universität Bern)
DTSTART:20180411T100000Z
DTEND:20180411T103000Z
UID:TALK103645@talks.cam.ac.uk
CONTACT:INI IT
DESCRIPTION:Gaussian Process models have been used in a number of problems
  where an objective function f needs to be studied based on a drastically 
 limited number of evaluations.  Global optimization algorithms based on Ga
 ussian Process models have been investigated for several decades\, and hav
 e become quite popular notably in design of computer experiments. Also\, f
 urther classes of problems involving the estimation of sets implicitly def
 ined by f\, e.g. sets of excursion above a given threshold\, have inspired
  multiple research developments.  In this talk\, we will give an overview 
 of recent results and challenges pertaining to the estimation of sets unde
 r Gaussian Process priors\, with a particular interest for to the quantifi
 cation and the sequential reduction of associated uncertainties.  Based on
  a series of joint works primarily with Dario Azzimonti\, Fran&ccedil\;ois
  Bachoc\, Julien Bect\, Micka&euml\;l Binois\, Cl&eacute\;ment Chevalier\,
  Ilya Molchanov\, Victor Picheny\, Yann Richet and Emmanuel Vazquez.
LOCATION:Seminar Room 1\, Newton Institute
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