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DTSTART;TZID=America/New_York:20180413T110000
DTEND;TZID=America/New_York:20180413T120000
DTSTAMP:20220528T125007
CREATED:20171215T160922Z
LAST-MODIFIED:20180323T190843Z
UID:2284-1523617200-1523620800@stat.mit.edu
SUMMARY:Testing degree corrections in Stochastic Block Models
DESCRIPTION:Abstract: The community detection problem has attracted significant attention in re-\ncent years\, and it has been studied extensively under the framework of a Stochas-\ntic Block Model (SBM). However\, it is well-known that SBMs t real data very\npoorly\, and various extensions have been suggested to replicate characteristics\nof real data. The recovered community assignments are often sensitive to the\nmodel used\, and this naturally begs the following question: Given a network\nwith community structure\, how to decide whether to t a vanilla SBM\, or a\nmore complicated model ? In this talk\, we will formulate this problem as a\nclassical goodness of fit question\, and try to provide some principled answers in\nthis direction.\nThis is based on joint work with Rajarshi Mukherjee. \n Biography: Subhabrata Sen is Schramm Postdoctoral Fellow at Microsoft Re-\nsearch NE and MIT Mathematics. He graduated from the Stanford Statistics\nDepartment in 2017\, where he was advised by Amir Dembo and Andrea Mon-\ntanari. He was awarded the \Probability Dissertation Award” for his thesis on\n\Random graphs\, optimization\, and spin glasses”. His research interests include\nhypothesis testing and non-parametric inference on one hand\, and combinatorial\noptimization and random graphs on the other.
URL:https://stat.mit.edu/calendar/subhabrata_sen/
LOCATION:E18-304\, United States
CATEGORIES:Stochastics and Statistics Seminar
GEO:42.3620185;-71.0878444
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