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UID:404@fds.yale.edu
DTSTART;TZID=America/New_York:20230331T120000
DTEND;TZID=America/New_York:20230331T130000
DTSTAMP:20240226T190404Z
URL:https://fds.yale.edu/events/fds-colloquium-tara-javidi-ucsd-a-conseque
ntial-view-of-information-for-statistical-learning-and-optimization/
SUMMARY:FDS Colloquium: Tara Javidi (UCSD) "A (Con)Sequential View of Infor
mation for Statistical Learning and Optimization"
DESCRIPTION:A (Con)Sequential View of Information for Statistical Learning
and Optimization\n\n\n\nSpeaker: Tara JavidiJacobs Family Scholar and Prof
essorElectrical and Computer EngineeringUCSD\n\n\n\nAbstract: In most comm
unication systems\, adapting transmission strategies to the (unpredictable
) realization of channel output at the receiver requires an (unrealistic)
assumption about the availability of a reliable “feedback” channel. Th
is unfortunate fact\, combined by the historical linkage between teaching
information theory and digital communication curriculum has kept “feedba
ck information theory” less taught\, discussed\, appreciated and underst
ood compared to other topics in our field.\n\n\n\nThis talk\, in contrast\
, highlights important and challenging problems in machine learning\, opti
mization\, statistics\, and control theory\, where the problem of acquirin
g information in an adaptive manner arises very naturally. Thus\, I will a
rgue that an increased emphasis on (teaching) feedback information theory
can provide vast and exciting research opportunities at the intersection o
f information theory and these fields. In particular\, I will revisit simp
le-to-teach results in feedback information theory including sequential hy
pothesis testing\, arithmetic coding\, successive refinement\, noisy binar
y search\, and posterior matching. Drawing on my own research\, I will als
o highlight the successful application of these sequential techniques in a
variety of problem instances such as black-box optimization\, distributio
n estimation\, and active machine learning with imperfect labels.\n\n\n\nS
peaker bio: Tara Javidi received her BS in electrical engineering at Shar
if University of Technology\, Tehran\, Iran. She received her MS degrees
in electrical engineering (systems) and in applied mathematics (stochasti
c analysis) from the University of Michigan\, Ann Arbor as well as her Ph
.D. in electrical engineering and computer science in 2002. She is curren
tly a Jacobs Family Scholar and Professor of Electrical and Computer Engi
neering and a founding co-director of the Center for Machine-Intelligence
\, Computing and Security (MICS) at UCSD.\n\n\n\nTara Javidi’s research
interests are in theory of active learning\, information acquisition and
statistical inference\, information theory with feedback\, stochastic
control theory\, and wireless networks. \n\n\n\nLocation: In-person at YI
NS\, 17 Hillhouse Ave\, 3rd floor. Yale-only livestream: https://yale.host
ed.panopto.com/Panopto/Pages/Viewer.aspx?id=accec6b8-cece-4306-869b-afce01
58dceb \n\n\n\nLunch will be served.\n
CATEGORIES:Colloquium,Seminar Series
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