Research Workshop

Mean-field approaches in Machine Learning and Statistics

Mean-field approaches in Machine Learning and Statistics

Part of the long program on Distributed Solutions to Complex Societal Problems

October 18-21, 2021


  • Marc Hoffmann (University of Paris-Dauphine)
  • Francis Bach (INRIA and Ecole Normale Superieure)


The aim of this workshop is to gather specialists from machine learning and statistics to applied probability and analysis who share a common interest in mean-field models. Potential applications range from mean-field games to stochastic algorithms and simulations, neural networks and frequentist or Bayesian statistical inference for interacting systems.

Confirmed Speakers:

  • Pierre Alquier (Center for Advanced Intelligence Project (AIP))
  • Alberto Bietti (New York University)
  • Joan Bruna (New York University)
  • Lenaic Chizat (Centre National de la Recherche Scientifique (CNRS))
  • Weinan E (Princeton University)
  • Franca Hoffmann (University of Bonn)
  • Florent Krzakala (EPFL (Ecole Polytechnique Fédérale de Lausanne))
  • Song Mei (University of California, Berkeley (UC Berkeley))
  • Patricia Reynaud-Bouret (CNRS Université Côte d’Azur)
  • Johannes Schmidt-Hieber (University of Twente)
  • Lenka Zdeborova (EPFL (Ecole Polytechnique Fédérale de Lausanne))
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