2-3 May 2019 Montpellier (France)

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).

 

Registration is free of charge but mandatoryRegister here

 

We 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"
 
Organisation committee:
  • Joseph Salmon, (Université de Montpellier)
  • Nicolas Verzelen (INRA)
  • André Mas (Université de Montpellier)
  • Benjamin Charlier (Université de Montpellier)

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