BEGIN:VCALENDAR VERSION:2.0 PRODID:researchseminars.org CALSCALE:GREGORIAN X-WR-CALNAME:researchseminars.org BEGIN:VEVENT SUMMARY:Sebastian Engelke (University of Geneva) DTSTART:20230112T170000Z DTEND:20230112T180000Z DTSTAMP:20260423T003243Z UID:MPML/94 DESCRIPTION:Title: Ma chine learning beyond the data range: extreme quantile regression\nby Sebastian Engelke (University of Geneva) as part of Mathematics\, Physics and Machine Learning (IST\, Lisbon)\n\n\nAbstract\nMachine learning method s perform well in prediction tasks within the range of the training data. When interest is in quantiles of the response that go beyond the observed records\, these methods typically break down. Extreme value theory provide s the mathematical foundation for estimation of such extreme quantiles. A common approach is to approximate the exceedances over a high threshold by the generalized Pareto distribution. For conditional extreme quantiles\, one may model the parameters of this distribution as functions of the pred ictors. Up to now\, the existing methods are either not flexible enough or do not generalize well in higher dimensions. We develop new approaches fo r extreme quantile regression that estimate the parameters of the generali zed Pareto distribution with tree-based methods and recurrent neural netwo rks. Our estimators outperform classical machine learning methods and meth ods from extreme value theory in simulations studies. We illustrate how th e recurrent neural network model can be used for effective forecasting of flood risk.\n LOCATION:https://researchseminars.org/talk/MPML/94/ END:VEVENT END:VCALENDAR