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:20230312T070000
END:DAYLIGHT
BEGIN:STANDARD
TZNAME:EST
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
DTSTART:20231105T060000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
UID:69dbbb9be400c
DTSTART;TZID=America/Toronto:20240129T103000
SEQUENCE:0
TRANSP:TRANSPARENT
DTEND;TZID=America/Toronto:20240129T113000
URL:https://uwaterloo.ca/computer-science/events/seminar-machine-learning-d
 istributionally-robust-machine-learning
LOCATION:DC - William G. Davis Computer Research Centre 200 University Aven
 ue West DC 1304 Waterloo ON N2L 3G1 Canada
SUMMARY:Seminar • Machine Learning • Distributionally Robust Machine\nL
 earning
CLASS:PUBLIC
DESCRIPTION:PLEASE NOTE: THIS SEMINAR WILL TAKE PLACE IN DC 1304.\n\nSHIORI
  SAGAWA\, PHD CANDIDATE\n_Department of Computer Science\, Stanford Univer
 sity_\n\nMachine learning systems are powerful\, but they can fail due to\
 ndistribution shifts: mismatches in the data distribution between\ntrainin
 g and deployment. Distribution shifts are ubiquitous and have\nreal-world 
 consequences: models can fail on subpopulations (e.g.\,\ndemographic group
 s) and on new domains unseen during training (e.g.\,\nnew hospitals).
DTSTAMP:20260412T153451Z
END:VEVENT
END:VCALENDAR