GRAPH SIGNALS : LEARNING AND OPTIMIZATION PERSPECTIVES

The workshop "Graph signals : learning and optimization perspectives" will take place on May 2 and May 3, 2019 in Montpellier (Université de Montpellier, Amphithéâtre Serge Peytavin - Polytech - Bâtiment 31, Campus Triolet).

e will have the please to have the following keynote speakers :

• Sophie Achard (CNRS -GIPSA-LAB) : "Graph inference by multiple testing: application to brain connectivity"
  
• Alain Barrat (Aix Marseille Univ, Université de Toulon, CNRS) : "Finding structures in temporal networks"
  
• Aurélien Bellet (INRIA Lille) : "Decentralized Machine Learning on Graphs"
• Anna Ben-Hamou (Sorbonne Université) : "Estimating graph parameters with random walks"
• Pierre Borgnat (CNRS, ENS Lyon) : "Graph Signal Processing on directed graph for modeling and learning"
  
• Nicolas Keriven (ENS) : Consistency of smoothed spectral clustering in the dynamic stochastic block model
  
• Loic Landrieu  (IGN)  : "Cut-Pursuit: A Working-Set Strategy for Graph-Structured Regularization"
• Andreas Loukas  (EPFL) : "Demystifying graph coarsening: spectral and cut guarantees"
  
• Nelly Pustelnik (CNRS, ENS Lyon) : "Discrete Mumford-Shah model: from image restoration to graph analysis"
• Vivian Vialon (CIRC) : "Data shared lasso: a simple L1 regularization method for the modeling of stratified data"
  

• Joseph Salmon, (Université de Montpellier)
• Nicolas Verzelen (INRA)
• André Mas (Université de Montpellier)
• Benjamin Charlier (Université de Montpellier)

• Samuel Vaiter (Université de Bourgogne)
• Charles-Alban Deledalle (CNRS)
• Pierre Bellec (Rutgers University)
• Xavier Dupuis (Université de Bourgogne)
• Nicolas Tremblay (Univ Grenoble-Alpe)