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BEGIN:VEVENT

CATEGORIES:Lectures/Conferences
CATEGORIES:Utilities
CATEGORIES:Lecture/Talk
CATEGORIES:Main
CONTACT;X-BEDEWORK-UID=00f1fcdb-0f068baf-010f-068baf83-00000004:None
CREATED:20240110T184203Z
DESCRIPTION:Recent interest has centered on uncertainty quantification for
  machine learning models. For the most part\, this work has assumed indep
 endence of the observations. However\, many of the most important problem
 s arising across scientific fields\, from genomics to climate science\, i
 nvolve systems where dependence cannot be ignored. In this talk\, I will 
 investigate inference on machine learning models in the presence of depen
 dence. \n\nIn the first part of my talk\, I will consider a common practi
 ce in the field of genomics in which researchers compute a correlation ma
 trix between genes and threshold its elements in order to extract groups 
 of independent genes. I will describe how to construct valid p-values ass
 ociated with these discovered groups that properly account for the group 
 selection process.  While this is related to the literature on selective 
 inference developed in the past decade\, this work involves inference abo
 ut the covariance matrix rather than the mean\, and therefore requires an
  entirely new technical toolset. This same toolset can be applied to quan
 tify the uncertainty associated with canonical correlation analysis after
  feature screening. \n\nIn the second part of my talk\, I will turn to an
  important problem in the field of oceanography as it relates to climate 
 science. Oceanographers have recently applied random forests to estimate 
 carbon export production\, a key quantity of interest\, at a given locati
 on in the ocean\; they then wish to sum the estimates across the world's 
 oceans to obtain an estimate of global export production. While quantifyi
 ng uncertainty associated with a single estimate is relatively straightfo
 rward\, quantifying uncertainty of the summed estimates is not\, due to t
 heir complex dependence structure. I will adapt the theory of V-statistic
 s to this dependent data setting in order to establish a central limit th
 eorem for the summed estimates\, which can be used to quantify the uncert
 ainty associated with global export production across the world's oceans.
 \n\nThis is joint work with my postdoctoral supervisors\, Daniela Witten 
 (University of Washington) and Jacob Bien (University of Southern Califor
 nia).
DURATION:PT1H
DTSTAMP:20240126T191301Z
DTSTART;TZID=America/New_York:20240205T114500
LAST-MODIFIED:20240126T191301Z
LOCATION;X-BEDEWORK-UID=18832edc-1b27e154-011b-28365daf-0000006c:Old Chemi
 stry 116
STATUS:CONFIRMED
SUMMARY:Inference for machine learning under dependence
UID:CAL-8a018ccf-8b87f80e-018c-f4ae7643-00007e00demobedework@mysite.edu
X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Main:/user/public-user/Utili
 ties/Main
X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Lecture_Talk:/user/public-us
 er/Lectures_Conferences/Lecture_Talk
X-BEDEWORK-STUDENT-CONTACT;X-BEDEWORK-PARAM-EMAIL=karen.whitesell@duke.edu
 :Karen Whitesell
X-BEDEWORK-SPEAKER:Arkajyoti Saha\, University of Washington
X-BEDEWORK-DUKE-SERIES:Statistical Science
X-BEDEWORK-SUBMITTEDBY:kherndon for Statistical Science (agrp_StatisticalS
 cience)
END:VEVENT
END:VCALENDAR