BEGIN:VCALENDAR VERSION:2.0 PRODID:researchseminars.org CALSCALE:GREGORIAN X-WR-CALNAME:researchseminars.org BEGIN:VEVENT SUMMARY:Soheil Feizi (University of Maryland College Park) DTSTART:20200623T163000Z DTEND:20200623T174500Z DTSTAMP:20260423T021055Z UID:IASML/24 DESCRIPTION:Title: G eneralizable Adversarial Robustness to Unforeseen Attacks\nby Soheil F eizi (University of Maryland College Park) as part of IAS Seminar Series o n Theoretical Machine Learning\n\n\nAbstract\nIn the last couple of years\ , a lot of progress has been made to enhance robustness of models against adversarial attacks. However\, two major shortcomings still remain: (i) pr actical defenses are often vulnerable against strong “adaptive” attack algorithms\, and (ii) current defenses have poor generalization to “unf oreseen” attack threat models (the ones not used in training).\n\nIn thi s talk\, I will present our recent results to tackle these issues. I will first discuss generalizability of a class of provable defenses based on ra ndomized smoothing to various Lp and non-Lp attack models. Then\, I will p resent adversarial attacks and defenses for a novel “perceptual” adver sarial threat model. Remarkably\, the defense against perceptual threat mo del generalizes well against many types of unforeseen Lp and non-Lp advers arial attacks.\n\nThis talk is based on joint works with Alex Levine\, Sah il Singla\, Cassidy Laidlaw\, Aounon Kumar and Tom Goldstein.\n LOCATION:https://researchseminars.org/talk/IASML/24/ END:VEVENT END:VCALENDAR