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DTSTAMP:20260915T091304Z
DESCRIPTION:\n	Title: Multivariate Extremes Generator by Statistical Learnin
 g\n\n	 \n\n	Abstract:\n\n\nGenerating realistic extremes from an observation
 al dataset is crucial when trying to estimate the risks associated with th
 e occurrence of future extremes\, possibly of greater magnitude than those
  already observed. Generative approaches from the machine learning communi
 ty are not applicable to extreme samples without careful adaptation. On th
 e other hand\, asymptotic results from extreme value theory provide a theo
 retical framework for modeling multivariate extreme events\, through the n
 otion of multivariate regular variation. Bridging these two fields\, this 
 presentation details a variational autoencoder approach for sampling multi
 variate distributions with heavy tails\, i.e.\, distributions likely to ex
 hibit extremes of particularly large intensities.\n\nSpeaker\n\nNicolas La
 fon is currently a postdoctoral researcher collaborating with Christian Ge
 nest and Johanna Nešlehová. He obtained his PhD from the Université Paris-
 Saclay at the Laboratory for Climate and Environmental Sciences in 2024 un
 der the supervision of Philippe Naveau and Ronan Fablet. His primary resea
 rch focuses on environmental extremes\, statistical learning\, and data as
 similation.\n\nhttps://mcgill.zoom.us/j/88929152266\n\nMeeting ID: 889 291
 5 2266\n\nPasscode: None\n
DTSTART:20250131T213000Z
DTEND:20250131T223000Z
LOCATION:Room 1104\, Burnside Hall\, CA\, QC\, Montreal\, H3A 0B9\, 805 rue
  Sherbrooke Ouest
SUMMARY:Nicolas Lafon (McGill University)
URL:https://www.mcgill.ca/mathstat/channels/event/nicolas-lafon-mcgill-univ
 ersity-362956
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