Graphical models are an effective tool for representing the dependency structure of random variables, including conditional independence and causality. These models are used, for instance, as clustering and dimension reduction methods in learning algorithms. Recent work has also highlighted the importance of such models in the study of extreme data dependency. This workshop will bring together researchers working on graphical models and their use in various fields of statistics such as the extreme value analysis. The various talks will present the plurality of approaches and the different inference methods that are used. In particular, the aim will be to discuss recent theoretical advances in this field, as well as the many resulting applications. 

• May, 15-17 2024
  
  
• Université de Montpellier - campus Triolet
  place Eugène Bataillon, 34090 Montpellier, France
  Building 25 - Room 01
  
  
  

To be updated

• Alexis Boulin (Université Côte d’Azur)
• Rémi Boutin (Université Paris Cité)
• Gloria Buriticá (Université de Genève)
  
• Lucas Butsch (Karlsruhe Institute of Technology)
  
• Valérie Chavez-Demoulin (Université de Lausanne)
• Manuel Hentschel (University of Geneva)
• Giulia Marchello (Inria, team PreMeDICaL)
  
• Anas Mourahib (UCLouvain)
  
• Alexander Reisach (Université Paris Cité)
• Pierre Ribereau (Université Claude Bernard Lyon 1)

Participation at the workshop is free but registration is mandatory. It is open until April, 3rd April 26th.

Emilie Chautru (Mines Paris - PSL)
Marine Demangeot (Université Paul Valéry Montpellier 3)
Nicolas Meyer (Université de Montpellier, Inria Lemon)
Maud Thomas (Sorbonne Université)
Gwladys Toulemonde (Université de Montpellier, Inria Lemon)

                                                                                                   This workshop is organized as part of the Halles de l'IA.