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UID:www.tcs.tifr.res.in/event/1611
DTSTAMP:20250923T100142Z
SUMMARY:Instance-optimality in universal prediction
DESCRIPTION:Speaker: Alankrita Bhatt (Caltech)\n\nAbstract: \nData-driven d
 ecision-making systems seamlessly integrate into every facet of our daily 
 lives. Despite this ubiquity\, the current era has also brought with it a 
 host of emerging challenges such as the need to make good decisions in the
  presence of uncertainty (about the future and the environment) as well as
  the storage and processing of high-volume data to improve decision-making
 . In this talk\, I will discuss my research program\, which takes an infor
 mation-theoretic approach to modern problems arising in sequential decisio
 n-making. This perspective will be illustrated through the problem of un
 iversal prediction\, where the goal is to make accurate\, sequential forec
 asts without knowing in advance whether the data are stochastic\, adversar
 ial\, or somewhere in between. I will present new algorithms and fundament
 al limits that characterize what is and isn't possible in this setting\, h
 ighlighting broader principles for reliable decision-making under uncertai
 nty.\nShort Bio:Alankrita Bhatt is a Research Scientist at Granica Computi
 ng Inc\, in Mountain View\, CA. Prior to this she was a Center for the Mat
 hematics of Information postdoctoral fellow at Caltech and a research fell
 ow at the Simons Institute for the Theory of Computing\, UC Berkeley. She 
 received a Ph.D. in Electrical and Computer Engineering from UC San Diego\
 , and a B.Tech. in Electrical Engineering from the Indian Institute of Tec
 hnology Kanpur. Her research interests lie broadly at the intersection of 
 information theory\, statistics\, and data science\, with a recent focus o
 n sequential decision-making.\n
URL:https://www.tcs.tifr.res.in/web/events/1611
DTSTART;TZID=Asia/Kolkata:20251009T113000
DTEND;TZID=Asia/Kolkata:20251009T123000
LOCATION:A-201 (STCS Seminar Room)
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