Rare and extreme events have a major impact on a wide variety of domains from environmental sciences (heat waves, flooding) to finance and insurance (financial crashes, reinsurance). Recent applications in risk management need to take complex and high dimensional data sets into account. On the other hand, motivated by a wide variety of applications including fraud detection, monitoring of complex networks and aviation safety management, unsupervised anomaly detection has recently received much attention in the machine learning community. This important area of Machine Learning is naturally related to extreme events analysis. As an example, when a complex system is monitored by several physical variables, controlling the false alarm rate is a major issue which can be addressed in the statistical framework of extreme value theory.
The purpose of this workshop is to bring together researchers and industrials from the extreme value statistics and the machine learning communities. With a concern for applications, the workshop will include presentations with industrial applications and a round table with industrials. Topics such as random forests, anomaly detection, risk measures and extreme quantile regression will be discussed.
Valérie Chavez (Université de Lausanne)
Dan Cooley (Colorado State University)
Anthony Davison (École Polytechnique Fédérale de Lausanne)
Sebastian Engelke (Université de Genève)
Vincent Feuillard (Airbus Group Innovations)
Stéphane Girard (Inria Grenoble Rhône-Alpes)
Adrien Hitz (University of Oxford)
Philippe Naveau (Laboratoire des Sciences du Climat et l'Environnement)
Albert Thomas (Huawei Technologies)
Jenny Wadsworth (Lancaster University)
Stefan Wager (Stanford Graduate School of Business)
Chen Zhou (Erasmus University Rotterdam)
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