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UID:www.tcs.tifr.res.in/event/842
DTSTAMP:20230914T125940Z
SUMMARY:Beyond Parametric Models: Robust Machine Learning with Permutation-
 based Models
DESCRIPTION:Speaker: Ashwin Pananjady (University of California\, Berkeley 
 \nDepartment of Electrical Engineering \nand Computer Science \nBerkeley\,
  CA \nUnited States of America)\n\nAbstract: \nParametric models offer sim
 plicity and interpretability\, and have been instrumental in driving progr
 ess in machine learning over the last few decades. In this talk\, I will d
 escribe progress on supplementing these models by explicitly incorporating
  underlying permutations in two canonical machine learning settings -- ran
 king and regression -- and show that the resulting "permutation-based" mod
 els are significantly richer but still preserve the advantages of parametr
 ic models. Focussing first on the ranking aspect\, I will show the utility
  of permutation-based models in estimating the results of pairwise compari
 sons and aggregating survey responses\, both of which are standard crowdso
 urcing tasks. In particular\, I will present algorithms that are significa
 ntly more robust than their parametric counterparts for ranking from parti
 al pairwise comparisons\, while also being rate-optimal in some settings. 
 In addition\, I will describe our recent progress on characterizing the st
 atistical and computational limits of this problem -- which are conjecture
 d to differ -- by presenting the first algorithm that makes progress on cl
 osing a conjectured statistical-computational gap. I will also briefly tou
 ch upon the regression aspect\, showing that such a permutation-based appr
 oach is suitable for modelling correspondence tasks in computer vision\, a
 nd allows us to design rate-optimal estimators for this classical problem.
 \n\nBio: Ashwin Pananjady is a fourth year Ph.D. student in the Department
  of Electrical Engineering and Computer Sciences at the University of Cali
 fornia\, Berkeley\, advised by Martin Wainwright and Thomas Courtade. His 
 interests are in machine learning\, optimization\, information theory\, an
 d statistics. He obtained his B.Tech. in Electrical Engineering from IIT M
 adras in 2014\, and graduated with the Governor's Gold Medal.\n
URL:https://www.tcs.tifr.res.in/web/events/842
DTSTART;TZID=Asia/Kolkata:20180108T140000
DTEND;TZID=Asia/Kolkata:20180108T150000
LOCATION:A-201 (STCS Seminar Room)
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