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SUMMARY:Evaluating a black-box algorithm: stability\, risk\, and model com
 parisons - Rina Foygel Barber (University of Chicago)
DTSTART:20250314T140000Z
DTEND:20250314T150000Z
UID:TALK226150@talks.cam.ac.uk
CONTACT:Qingyuan Zhao
DESCRIPTION:When we run a complex algorithm on real data\, it is standard 
 to use a holdout set\, or a cross-validation strategy\, to evaluate its be
 havior and performance. When we do so\, are we learning information about 
 the algorithm itself\, or only about the particular fitted model(s) that t
 his particular data set produced? In this talk\, we will establish fundame
 ntal hardness results on the problem of empirically evaluating properties 
 of a black-box algorithm\, such as its stability and its average risk\, in
  the distribution-free setting.\nThis work is joint with Yuetian Luo and B
 yol Kim.\n
LOCATION:Centre for Mathematical Sciences MR12\, CMS
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