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SUMMARY:Jayadev Acharya (Cornell)
DTSTART:20201014T170000Z
DTEND:20201014T180000Z
DTSTAMP:20260423T021012Z
UID:TCSPlus/13
DESCRIPTION:Title: <a href="https://researchseminars.org/talk/TCSPlus/13/"
 >Distributed Statistical Inference under Local Information Constraints</a>
 \nby Jayadev Acharya (Cornell) as part of TCS+\n\n\nAbstract\nWe consider 
 statistical inference tasks in a distributed setting where access to data 
 samples is subjected to strict "local constraints\," through a unified fra
 mework that captures communication limitations and (local) privacy constra
 ints as special cases. We study estimation (learning) and goodness-of-fit 
 (testing) for both discrete and high-dimensional distributions. Our goal i
 s to understand how the sample complexity increases under the information 
 constraints.\n\nIn this talk we will provide an overview of this field and
  a sample of some of our results. We will discuss the role of (public) ran
 domness  and interactivity in information-constrained inference\, and make
  a case for thinking about randomness and interactivity as resources.\n\nT
 he work is part of a long-term ongoing collaboration with Clément Canonne
  (IBM Research) and Himanshu Tyagi (IISc)\, and includes works done with C
 ody Freitag (Cornell)\, Yanjun Han (Stanford)\, Yuhan Liu (Cornell)\, and 
 Ziteng Sun (Cornell).\n
LOCATION:https://researchseminars.org/talk/TCSPlus/13/
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