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CATEGORIES:Isaac Newton Institute Seminar Series
SUMMARY:Information-theoretic perspectives on learning alg
 orithms - Varun Jog (University of Wisconsin-Madis
 on)
DTSTART;TZID=Europe/London:20180222T110000
DTEND;TZID=Europe/London:20180222T120000
UID:TALK101302AThttp://talks.cam.ac.uk
URL:http://talks.cam.ac.uk/talk/index/101302
DESCRIPTION:In statistical learning theory\, generalization er
 ror is used to quantify the degree to which a supe
 rvised machine learning algorithm may overfit to t
 raining data. We overview some recent work [Xu and
  Raginsky (2017)] that bounds generalization error
  of empirical risk minimization based on the mutua
 l information I(S\;W) between the algorithm input 
 S and the algorithm output W. We leverage these re
 sults to derive generalization error bounds for a 
 broad class of iterative algorithms that are chara
 cterized by bounded\, noisy updates with Markovian
  structure\, such as stochastic gradient Langevin 
 dynamics (SGLD). We describe certain shortcomings 
 of mutual information-based bounds\, and propose a
 lternate bounds that employ the Wasserstein metric
  from optimal transport theory. We compare the Was
 serstein metric-based bounds with the mutual infor
 mation-based bounds and show that for a class of d
 ata generating distributions\, the former leads to
  stronger bounds on the generalization error.  <br
 ><br><br><br>
LOCATION:Seminar Room 2\, Newton Institute
CONTACT:INI IT
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