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:6830393469cfe
DTSTART;TZID=America/Toronto:20240129T103000
SEQUENCE:0
TRANSP:TRANSPARENT
DTEND;TZID=America/Toronto:20240129T113000
URL:/computer-science/events/seminar-machine-learning-d
 istributionally-robust-machine-learning
SUMMARY:Seminar • Machine Learning • Distributionally Robust Machine\nL
 earning
CLASS:PUBLIC
DESCRIPTION:Summary \n\nPLEASE NOTE: THIS SEMINAR WILL TAKE PLACE IN DC 130
 4.\n\nSHIORI SAGAWA\, PHD CANDIDATE\n_Department of Computer Science\, Sta
 nford University_\n\nMachine learning systems are powerful\, but they can 
 fail due to\ndistribution shifts: mismatches in the data distribution betw
 een\ntraining and deployment. Distribution shifts are ubiquitous and have\
 nreal-world consequences: models can fail on subpopulations (e.g.\,\ndemog
 raphic groups) and on new domains unseen during training (e.g.\,\nnew hosp
 itals).\n 
DTSTAMP:20250523T090036Z
END:VEVENT
END:VCALENDAR