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SUMMARY:Theoretical Advantages of Deep Neural Networks
DTSTART;VALUE=DATE-TIME:20191107T002000Z
DTEND;VALUE=DATE-TIME:20191107T004000Z
DTSTAMP;VALUE=DATE-TIME:20260722T203251Z
UID:indico-contribution-9-20@sshep.snu.ac.kr
DESCRIPTION:Speakers: Yongdai Kim (Seoul National University)\nFrom statis
 tical points of view\, deep neural networks (DNN) are nothing but a (gener
 alized) regression model\, but DNNs have solved many problems no other met
 hods have not succeeded in the past. In this talk\, I will explain theoret
 ical advantages of DNNs compared to other nonparametric regression models.
 \n\nhttps://sshep.snu.ac.kr/event/107/contributions/20/
LOCATION:Seoul National University\, College of Natural Sciences\, Buildin
 g 25-1 국제회의실 (International Conference Hall)
URL:https://sshep.snu.ac.kr/event/107/contributions/20/
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SUMMARY:Projection onto Minkowski Sums with Application to Constrained Lea
 rning
DTSTART;VALUE=DATE-TIME:20191107T004000Z
DTEND;VALUE=DATE-TIME:20191107T010000Z
DTSTAMP;VALUE=DATE-TIME:20260722T203251Z
UID:indico-contribution-9-25@sshep.snu.ac.kr
DESCRIPTION:Speakers: Joong-Ho Won (Seoul National University)\nWe introdu
 ce block descent algorithms for projecting onto Minkowski sums of sets. Pr
 ojection onto such sets is a crucial step in many statistical learning pro
 blems\, and may regularize complexity of solutions to an optimization prob
 lem or arise in dual formulations of penalty methods. We show that project
 ing onto the Minkowski sum admits simple\, efficient algorithms when compl
 ications such as overlapping constraints pose challenges to existing metho
 ds. We prove that our algorithm converges linearly when sets are strongly 
 convex or satisfy an error bound condition\, and extend the theory and met
 hods to encompass non-convex sets as well. We demonstrate empirical advant
 ages in runtime and accuracy over competitors in applications to ℓ1\,p-r
 egularized learning\, constrained lasso\, and overlapping group lasso.\n\n
 \nReference:\n1. Joong-Ho Won\, Jason Xu\, Kenneth Lange\; [Proceedings of
  the 36th International Conference on Machine Learning\, PMLR 97:3642-3651
 \, 2019][1]. \n\n\n  [1]: http://proceedings.mlr.press/v97/lange19a.html\n
 \nhttps://sshep.snu.ac.kr/event/107/contributions/25/
LOCATION:Seoul National University\, College of Natural Sciences\, Buildin
 g 25-1 국제회의실 (International Conference Hall)
URL:https://sshep.snu.ac.kr/event/107/contributions/25/
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