BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Biomedical Mathematics Group - ECPv6.16.2//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-ORIGINAL-URL:https://www.ibs.re.kr/bimag
X-WR-CALDESC:Events for Biomedical Mathematics Group
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:Asia/Seoul
BEGIN:STANDARD
TZOFFSETFROM:+0900
TZOFFSETTO:+0900
TZNAME:KST
DTSTART:20220101T000000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20230324T160000
DTEND;TZID=Asia/Seoul:20230324T170000
DTSTAMP:20260529T080059
CREATED:20230213T105312Z
LAST-MODIFIED:20230320T010451Z
UID:7318-1679673600-1679677200@www.ibs.re.kr
SUMMARY:(Rescheduled: 3/22 -> 3/24) Stefan Bauer\, Neural Causal Models for Experimental Design
DESCRIPTION:Abstract: Many questions in everyday life as well as in research are causal in nature: How would the climate change if we lower train prices or will my headache go away if I take an aspirin? Inherently\, such questions need to specify the causal variables relevant to the question and their interactions. However\, existing algorithms for learning causal graphs from data are often not scaling well both with the number of variables or the number of observations. This talk will provide a brief introduction to causal structure learning\, recent efforts in using continuous optimization to learn causal graphs at scale and systematic approaches for causal experimental design at scale.
URL:https://www.ibs.re.kr/bimag/event/neural-causal-models-for-experimental-design/
LOCATION:ZOOM ID: 997 8258 4700 (Biomedical Mathematics Online Colloquium)\, (pw: 1234)
CATEGORIES:Biomedical Mathematics Online Colloquium
ATTACH;FMTTYPE=image/jpeg:https://www.ibs.re.kr/bimag/cms/wp-content/uploads/2023/02/jItlmUQr_400x400.jpg
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
END:VCALENDAR