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SUMMARY:Piotr Indyk (Massachusetts Institute of Technology)
DTSTART:20200825T163000Z
DTEND:20200825T174500Z
DTSTAMP:20260423T021052Z
UID:IASML/22
DESCRIPTION:Title: <a href="https://researchseminars.org/talk/IASML/22/">L
 earning-Based Sketching Algorithms</a>\nby Piotr Indyk (Massachusetts Inst
 itute of Technology) as part of IAS Seminar Series on Theoretical Machine 
 Learning\n\n\nAbstract\nClassical algorithms typically provide "one size f
 its all" performance\, and do not leverage properties or patterns in their
  inputs. A recent line of work aims to address this issue by developing al
 gorithms that use machine learning predictions to improve their performanc
 e. In this talk I will present two examples of this type\, in the context 
 of streaming and sketching algorithms. In particular\, I will show how to 
 use machine learning predictions to improve the performance of (a) low-mem
 ory streaming algorithms for frequency estimation\, and (b) generating spa
 ce partitions for nearest neighbor search.\n\nThe talk will cover material
  from papers co-authored with Y Dong\, CY Hsu\, D Katabi\, I Razenshteyn\,
  T Wagner and A Vakilian.\n
LOCATION:https://researchseminars.org/talk/IASML/22/
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