BEGIN:VCALENDAR VERSION:2.0 PRODID:researchseminars.org CALSCALE:GREGORIAN X-WR-CALNAME:researchseminars.org BEGIN:VEVENT SUMMARY:Emmanuel Candes (Stanford) DTSTART:20200512T180000Z DTEND:20200512T190000Z DTSTAMP:20260423T035406Z UID:MADPlus/8 DESCRIPTION:Title: Reliable predictions? Equitable treatment? Some recent progress in predict ive inference\nby Emmanuel Candes (Stanford) as part of MAD+\n\n\nAbst ract\nRecent progress in machine learning (ML) provides us with many poten tially effective tools to learn from datasets of ever increasing sizes and make useful predictions. How do we know that these tools can be trusted i n critical and high-sensitivity systems? If a learning algorithm predicts the GPA of a prospective college applicant\, what guarantees do I have con cerning the accuracy of this prediction? How do we know that it is not bia sed against certain groups of applicants? This talk introduces statistical ideas to ensure that the learned models satisfy some crucial properties\, especially reliability and fairness (in the sense that the models need to apply to individuals in an equitable manner). To achieve these important objectives\, we shall not ‘open up the black box’ and try understandin g its underpinnings. Rather we discuss broad methodologies — conformal i nference\, quantile regression\, the Jackknife+ — that can be wrapped ar ound any black box as to produce results that can be trusted and are equit able.\n LOCATION:https://researchseminars.org/talk/MADPlus/8/ END:VEVENT END:VCALENDAR