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.

• Maud Thomas Sorbonne Université maud.thomas@upmc.fr
• Clément Dombry Université de Franche-Comté clement.dombry@univ-fcomte.fr
• Anne Sabourin Télécom ParisTech anne.sabourin@telecom-paristech.fr
• Jonathan El Methni Université Paris Descartes jonathan.el-methni@parisdescartes.fr