BEGIN:VCALENDAR
CALSCALE:GREGORIAN
VERSION:2.0
METHOD:PUBLISH
PRODID:-//Drupal iCal API//EN
X-WR-TIMEZONE:America/New_York
BEGIN:VTIMEZONE
TZID:America/New_York
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
RRULE:FREQ=YEARLY;BYMONTH=3;BYDAY=2SU
DTSTART:20070311T020000
TZNAME:EDT
TZOFFSETTO:-0400
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
RRULE:FREQ=YEARLY;BYMONTH=11;BYDAY=1SU
DTSTART:20071104T020000
TZNAME:EST
TZOFFSETTO:-0500
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
SEQUENCE:1
X-APPLE-TRAVEL-ADVISORY-BEHAVIOR:AUTOMATIC
UID:243386
DTSTAMP:20260924T142331Z
DTSTART;TZID=America/New_York:20261001T130000
DTEND;TZID=America/New_York:20261001T140000
URL;TYPE=URI:https://www.wpi.edu/news/calendar/events/mathematical-sciences
 -department-applied-math-seminar-tamara-broderick-mit
SUMMARY:Mathematical Sciences Department Applied Math Seminar - Tamara Brod
 erick\, MIT
DESCRIPTION:Title: An Automatic Finite-Sample Robustness Check: Can Droppin
 g a Little Data Change Conclusions?\nAbstract:Practitioners will often ana
 lyze a data sample with the goal of applying any conclusions to a new popu
 lation. For instance\, if economists conclude microcredit is effective at 
 alleviating poverty based on observed data\, policymakers might decide to 
 distribute microcredit in other locations or future years. Typically\, the
  original data is not a perfect random sample from the population where po
 licy is applied --- but researchers might feel comfortable generalizing an
 yway so long as deviations from random sampling are small\, and the corres
 ponding impact on conclusions is small as well. Conversely\, researchers m
 ight worry if a very small proportion of the data sample was instrumental 
 to the original conclusion. So we propose a method to assess the sensitivi
 ty of conclusions to the removal of a very small fraction of the data set.
  Manually checking all small data subsets is computationally infeasible\, 
 so we propose an approximation based on the classical influence function. 
 Our method is automatically computable for common estimators. We provide e
 rror bounds on approximation performance and a low-cost exact lower bound 
 on sensitivity. We find that sensitivity is driven by a signal-to-noise ra
 tio in the inference problem\, does not disappear as data accrues\, and is
  not decided by misspecification. Empirically we find that many data analy
 ses are robust\, but the conclusions of several influential economics pape
 rs can be changed by removing (much) less than 1% of the data\, and droppi
 ng just 0.003% of human preferences can change the top-ranked large langua
 ge model on Chatbot Arena.
END:VEVENT
END:VCALENDAR