Date: 2025-01-31

Time: 15:30-16:30 (Montreal time)

Location: In person, Burnside 1104

https://mcgill.zoom.us/j/88929152266

Meeting ID: 889 2915 2266

Passcode: None

Abstract:

Generating realistic extremes from an observational dataset is crucial when trying to estimate the risks associated with the occurrence of future extremes, possibly of greater magnitude than those already observed. Generative approaches from the machine learning community are not applicable to extreme samples without careful adaptation. On the other hand, asymptotic results from extreme value theory provide a theoretical framework for modeling multivariate extreme events, through the notion of multivariate regular variation. Bridging these two fields, this presentation details a variational autoencoder approach for sampling multivariate distributions with heavy tails, i.e., distributions likely to exhibit extremes of particularly large intensities.

Speaker

Nicolas Lafon is currently a postdoctoral researcher collaborating with Christian Genest and Johanna Nešlehová. He obtained his PhD from the Université Paris-Saclay at the Laboratory for Climate and Environmental Sciences in 2024 under the supervision of Philippe Naveau and Ronan Fablet. His primary research focuses on environmental extremes, statistical learning, and data assimilation.