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DTSTART;TZID=America/New_York:20220907T120000
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SUMMARY:ASSET Seminar: Robust and Equitable Uncertainty Estimation (Aaron Roth\, University of Pennsylvania)
DESCRIPTION:Presentation Abstract:  \nMachine learning provides us with an amazing set of tools to make predictions\, but how much should we trust particular predictions? To answer this\, we need a way of estimating the confidence we should have in particular predictions of black-box models. Standard tools for doing this give guarantees that are averages over predictions. For instance\, in a medical application\, such tools might paper over poor performance on one medically relevant demographic group if it is made up for by higher performance on another group. Standard methods also depend on the data distribution being static — in other words\, the future should be like the past. \nIn this talk\, we will describe a new technique to address both these problems: a way to produce prediction sets for arbitrary black-box prediction methods that have correct empirical coverage even when the data distribution might change in arbitrary\, unanticipated ways and such that we have correct coverage even when we zoom in to focus on demographic groups that can be arbitrary and intersecting. \nThis is a joint work with Osbert Bastani\, Varun Gupta\, Christopher Jung\, Georgy Noarov\, and Ramya Ramalingam.
URL:https://seasevents.nmsdev7.com/event/asset-seminar-robust-and-equitable-uncertainty-estimation-aaron-roth-university-of-pennsylvania/
LOCATION:Levine 307\, 3330 Walnut Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Seminar
ORGANIZER;CN="Computer and Information Science":MAILTO:cherylh@cis.upenn.edu
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