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SUMMARY:Universal sampling discretization of integral norms and sparse sam
 pling recovery - Feng Dai (University of Alberta)
DTSTART:20240715T083000Z
DTEND:20240715T091000Z
UID:TALK218116@talks.cam.ac.uk
DESCRIPTION:In this talk\, I will report some advancements in sampling dis
 cretization and recovery. &nbsp\; My primary focus will be on my joint wor
 k with &nbsp\;E.&nbsp\; Kosov\, &nbsp\; A. Prymak\, A. Shadrin\, &nbsp\;V.
  Temlyakov\, &nbsp\;S. Tikhonov in this area.&nbsp\; The central topic of 
 discussion will be the challenge of discretizing &nbsp\;$L_p$ norm in a hi
 gh-dimensional space. The goal is to establish two-sided estimates of the 
 $L_p$ norm defined with respect to a &nbsp\;general probability measure\, 
 using a finite sum of function values. &nbsp\; &nbsp\;The uniform discreti
 zation approach applies universally to all functions in the space\, &nbsp\
 ;ensuring that the points are independent of any specific functions within
  the space.&nbsp\;&nbsp\;
LOCATION:Seminar Room 1\, Newton Institute
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