The study of geometric extremes, where extremal dependence properties are inferred from
the deterministic limiting shapes of scaled sample clouds, provides an exciting approach to …

Understanding multivariate dependencies in both the bulk and the tails of a distribution is an
important problem for many applications, such as ensuring algorithms are robust to …

This article aims at investigating the use of generative methods based on neural networks to
simulate extreme events. Although very popular, these methods are mainly invoked in …

Dealing with extreme values is a major challenge in probabilistic modeling, of great
importance in various application domains such as economics, engineering and life …

The goal of this paper is to develop distributionally robust optimization (DRO) estimators,
specifically for multidimensional Extreme Value Theory (EVT) statistics. EVT supports using …

Copulas are popular in high-dimensional statistical applications as they allow for
dependence modeling with arbitrary margins. They are also used in rare event analysis …

Extreme events are potentially catastrophic events that occur infrequently within an
observation time frame, and it is necessary to understand the distribution of these events to …