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BEGIN:VEVENT
DTSTAMP:20260427T084119Z
DTSTART;VALUE=DATE-TIME:20180119T140000
DTEND;VALUE=DATE-TIME:20180119T150000
SUMMARY:CRiSM Seminar
TZID:Europe/London
UID:20180119-8a17841a5e5cecfb015e762e5030626f@warwick.ac.uk
CREATED:20170912T130140Z
DESCRIPTION:Jonas Peters\, Department of Mathematical Sciences\, Universi
 ty of Copenhagen Invariant Causal Prediction Abstract: Why are we intere
 sted in the causal structure of a process? In classical prediction tasks
  as regression\, for example\, it seems that no causal knowledge is requ
 ired. In many situations\, however\, we want to understand how a system 
 reacts under interventions\, e.g.\, in gene knock-out experiments. Here\
 , causal models become important because they are usually considered inv
 ariant under those changes. A causal prediction uses only direct causes 
 of the target variable as predictors\; it remains valid even if we inter
 vene on predictor variables or change the whole experimental setting. In
  this talk\, we show how we can exploit this invariance principle to est
 imate causal structure from data. We apply the methodology to data sets 
 from biology\, epidemiology\, and finance. The talk does not require any
  knowledge about causal concepts. David Ginsbourger\, Idiap Research Ins
 titute and University of Bern\, http://www.ginsbourger.ch Quantifying an
 d reducing uncertainties on sets under Gaussian Process priors Abstract:
  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 Gaussian
  Process models have been investigated for several decades\, and have be
 come quite popular notably in design of computer experiments. Also\, fur
 ther classes of problems involving the estimation of sets implicitly def
 ined by f\, e.g. sets of excursion above a given threshold\, have inspir
 ed multiple research developments. In this talk\, we will give an overvi
 ew of recent results and challenges pertaining to the estimation of sets
  under Gaussian Process priors\, with a particular interest for to the q
 uantification and the sequential reduction of associated uncertainties. 
 Based on a series of joint works primarily with Dario Azzimonti\, Franço
 is Bachoc\, Julien Bect\, Mickaël Binois\, Clément Chevalier\, Ilya Molc
 hanov\, Victor Picheny\, Yann Richet and Emmanuel Vazquez.
LOCATION:MA_B1.01
URL:http://www2.warwick.ac.uk/fac/sci/statistics/crism/seminars
ATTACH:http://www2.warwick.ac.uk/fac/sci/statistics/crism/seminars
CATEGORIES:CRiSM Seminars,Seminars
LAST-MODIFIED:20180119T095943Z
ORGANIZER;CN=Paula Matthews:
END:VEVENT
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