BEGIN:VCALENDAR VERSION:2.0 PRODID:researchseminars.org CALSCALE:GREGORIAN X-WR-CALNAME:researchseminars.org BEGIN:VEVENT SUMMARY:Avrim Blum (Toyota Technological Institute at Chicago) DTSTART:20200616T190000Z DTEND:20200616T203000Z DTSTAMP:20260423T003236Z UID:IASML/6 DESCRIPTION:Title: On learning in the presence of biased data and strategic behavior\nby Av rim Blum (Toyota Technological Institute at Chicago) as part of IAS Semina r Series on Theoretical Machine Learning\n\n\nAbstract\nIn this talk I wil l discuss two lines of work involving learning in the presence of biased d ata and strategic behavior. In the first\, we ask whether fairness constr aints on learning algorithms can actually improve the accuracy of the clas sifier produced\, when training data is unrepresentative or corrupted due to bias. Typically\, fairness constraints are analyzed as a tradeoff with classical objectives such as accuracy. Our results here show there are n atural scenarios where they can be a win-win\, helping to improve overall accuracy. In the second line of work we consider strategic classification : settings where the entities being measured and classified wish to be cla ssified as positive (e.g.\, college admissions) and will try to modify the ir observable features if possible to make that happen. We consider this in the online setting where a particular challenge is that updates made by the learning algorithm will change how the inputs behave as well.\n LOCATION:https://researchseminars.org/talk/IASML/6/ END:VEVENT END:VCALENDAR