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SUMMARY:Simon Du (University of Washington)
DTSTART:20210728T160000Z
DTEND:20210728T170000Z
DTSTAMP:20260423T003249Z
UID:MPML/52
DESCRIPTION:Title: <a href="https://researchseminars.org/talk/MPML/52/">Pr
 ovable Representation Learning</a>\nby Simon Du (University of Washington)
  as part of Mathematics\, Physics and Machine Learning (IST\, Lisbon)\n\n\
 nAbstract\nRepresentation learning has been widely used in many applicatio
 ns. In this talk\, I will present our work\, which uncovers when and why r
 epresentation learning provably improves the sample efficiency\, from a st
 atistical learning point of view. I will show 1) the existence of a good r
 epresentation among all tasks\, and 2) the diversity of tasks are key cond
 itions that permit improved statistical efficiency via multi-task represen
 tation learning. These conditions provably improve the sample efficiency f
 or functions with certain complexity measures as the representation. If ti
 me permits\, I will also talk about leveraging the theoretical insights to
  improve practical performance.\n
LOCATION:https://researchseminars.org/talk/MPML/52/
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