BEGIN:VCALENDAR VERSION:2.0 PRODID:researchseminars.org CALSCALE:GREGORIAN X-WR-CALNAME:researchseminars.org BEGIN:VEVENT SUMMARY:Aleksander Madry (MIT) DTSTART:20200609T162000Z DTEND:20200609T175000Z DTSTAMP:20260423T003247Z UID:IASML/4 DESCRIPTION:Title: Wh at do our models learn?\nby Aleksander Madry (MIT) as part of IAS Semi nar Series on Theoretical Machine Learning\n\n\nAbstract\nLarge-scale visi on benchmarks have driven---and often even defined---progress in machine l earning. However\, these benchmarks are merely proxies for the real-world tasks we actually care about. How well do our benchmarks capture such task s?\n\nIn this talk\, I will discuss the alignment between our benchmark-dr iven ML paradigm and the real-world uses cases that motivate it. First\, w e will explore examples of biases in the ImageNet dataset\, and how state- of-the-art models exploit them. We will then demonstrate how these biases arise as a result of design choices in the data collection and curation pr ocesses.\n\nBased on joint works with Logan Engstrom\, Andrew Ilyas\, Shib ani Santurkar\, Jacob Steinhardt\, Dimitris Tsipras and Kai Xiao.\n LOCATION:https://researchseminars.org/talk/IASML/4/ END:VEVENT END:VCALENDAR