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SUMMARY:Kirk Public Lecture: On feasible set estimation with Bayesian acti
 ve learning - Clémentine Prieur (Université Grenoble Alpes)
DTSTART:20250722T150000Z
DTEND:20250722T160000Z
UID:TALK230752@talks.cam.ac.uk
DESCRIPTION:The general topic of this talk is Bayesian adaptive learning o
 f excursion sets defined from a costly black-box model. This research fiel
 d has received many attention in the last decades. During this talk\, we w
 ill first review Gaussian Process Regression for feasible set estimation i
 n the framework where the set to recover is defined from a numerical model
  with scalar values. We will exhibit that usual adaptive sampling criteria
  may lack of robustness\, e.g.\, when the set to recover has several conne
 x components. Then we will address more complex frameworks\, such as the p
 resence of uncertainties or the case of numerical models with vector outpu
 ts.
LOCATION:Seminar Room 1\, Newton Institute
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