BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Drupal iCal API//EN
X-WR-CALNAME:Events items teaser
X-WR-TIMEZONE:America/Toronto
BEGIN:VTIMEZONE
TZID:America/Toronto
X-LIC-LOCATION:America/Toronto
BEGIN:DAYLIGHT
TZNAME:EDT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
DTSTART:20200308T070000
END:DAYLIGHT
BEGIN:STANDARD
TZNAME:EST
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
DTSTART:20191103T060000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
UID:69ce9f6651936
DTSTART;TZID=America/Toronto:20200731T153000
SEQUENCE:0
TRANSP:TRANSPARENT
DTEND;TZID=America/Toronto:20200731T153000
URL:https://uwaterloo.ca/combinatorics-and-optimization/events/tutte-colloq
 uium-jim-luedtke
SUMMARY:Tutte Colloquium - Jim Luedtke
CLASS:PUBLIC
DESCRIPTION:TITLE: Data-Driven Sample-Average Approximation for Stochastic
 \nOptimization with Covariate Information\n\nSpeaker:\n Jim Luedtke\n\nAff
 iliation:\n University of Wisconsin-Madison\n\nZoom:\n Please email Emma 
 Watson\n\nABSTRACT:\n\nWe consider optimization models for decision-making
  in which\nparameters within the optimization model are uncertain\, but\np
 redictions of these parameters can be made using available covariate\ninfo
 rmation.  We consider a data-driven setting in which we have\nobservation
 s of the uncertain parameters together with\nconcurrently-observed covaria
 tes.  Given a new covariate observation\,\nthe goal is to choose a decisi
 on that minimizes the expected cost\nconditioned on this observation.  We
  investigate a data-driven\nframework in which the outputs from a machine 
 learning prediction\nmodel are directly used to define a stochastic progra
 mming sample\naverage approximation (SAA). 
DTSTAMP:20260402T165502Z
END:VEVENT
END:VCALENDAR