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UID:www.tcs.tifr.res.in/event/1211
DTSTAMP:20230914T125954Z
SUMMARY:Overcoming the sample complexity barrier in risk analytics with str
 uctured tail models
DESCRIPTION:Speaker: Karthyek Murthy (Singapore University of Technology & 
 Design (SUTD))\n\nAbstract: \nThe ability to learn and control tail risks\
 , besides being an integral part of quantitative risk management\, is impo
 rtant in running operations requiring high service levels and cyberphysica
 l systems requiring high reliability guarantees. Despite this significance
 \, scalable algorithmic approaches have remained elusive due to the rarity
  with which relevant risky samples are observed. In this talk we examine t
 his bottleneck in two avenues: (i) statistical learning for minimization o
 f tail risks and (ii) simulation of tail risks. We show efficient learning
  and simulation is possible by exploiting the similarity with which risk e
 vents unfold at different scales. This self-similarity\, being a nonparame
 tric characteristic\, leads to satisfyingly expressive model classes and s
 calable algorithms which require exponentially fewer samples than their be
 nchmark counterparts. Efficient learning is made possible by a novel targe
 ted approach towards robustness which could be of interest in broader cont
 exts due to its automatic bias correction property. Specifically\, the sel
 f-similar structure provides a fertile ground to exhibit how mildly restri
 ctive structures can be utilized to debias the error introduced by first-s
 tep model estimation.\n
URL:https://www.tcs.tifr.res.in/web/events/1211
DTSTART;TZID=Asia/Kolkata:20220628T160000
DTEND;TZID=Asia/Kolkata:20220628T170000
LOCATION:A-201
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