BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Penn Engineering Events - ECPv6.15.18//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-WR-CALNAME:Penn Engineering Events
X-ORIGINAL-URL:https://seasevents.nmsdev7.com
X-WR-CALDESC:Events for Penn Engineering Events
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:America/New_York
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20220313T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20221106T060000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20230312T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20231105T060000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20240310T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20241103T060000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20231206T120000
DTEND;TZID=America/New_York:20231206T131500
DTSTAMP:20260403T223329
CREATED:20230928T142047Z
LAST-MODIFIED:20230928T142047Z
UID:9842-1701864000-1701868500@seasevents.nmsdev7.com
SUMMARY:ASSET Seminar: "Robust Machine Learning with Foundation Models" (Aditi Raghunathan\, Carnegie Mellon University)
DESCRIPTION:ABSTRACT: \nIn recent years\, foundation models—large pretrained models that can be adapted for a wide range of tasks—have achieved state-of-the-art performance on a variety of tasks. While the pretrained models are trained on broad data\, the adaptation (or fine-tuning) process is often performed on limited data. As a result\, the challenges of distribution shift\, where a model is deployed on a different distribution as the fine-tuning data remain\, albeit in a different form. \nFirst\, via experiments on pretrained vision and language models\, we show different kinds of “catastrophic forgetting’’ where pretrained information is forgotten and correspondences between and in-distribution and out-of-distribution features are weakened. As a result\, fine-tuned models are not maximally robust to distribution shifts. We then provide new fine-tuning and prompting methods\, backed by theoretical insights\, that minimize such distortion and vastly improve accuracy and robustness. On the flip side\, our work shows that pretrained knowledge can be hard to get rid of\, thereby underlining the potential perils of overreliance on fine-tuning for safety.
URL:https://seasevents.nmsdev7.com/event/asset-seminar-aditi-raghunathan-carnegie-mellon-university-2/
LOCATION:Levine 307\, 3330 Walnut Street\, Philadelphia\, PA\, 19104\, United States
END:VEVENT
END:VCALENDAR