The workshop Statlearn is a premier event held every year, which focuses on current and upcoming trends in Statistical Learning. Statlearn’17, the 8th edition of the workshop, will be held in Lyon on April, 6-7 2016. Statlearn’17 is a conference of the French Society of Statistics (SFdS). The workshop will be preceded by a 1-day spring school (tutorials)!


The registration is free but mandatory. Tutorials will be limited to 50 participants.
Registration will open about mid-December.

Tutorials (summary):

  • Wednesday, April 5th, 10:00-13:00:
    Model-based clustering and classification for high-dimensional data (with R),
    Charles Bouveyron (Univ. Paris Descartes, web)
  • Wednesday, April 5th, 15:00-18:00:
    Intermediate R Programming: The transition from “using” to “scientific computing”,
    John W. Emerson (Yale University, web)

Workshop (summary):

  • Thursday, April 6th, 9:00-12:30: Statistical advances in machine learning
    • Nicolas Vayatis (Ecole Normale Supérieure de Cachan, web)
    • Hugo Larochelle (Google, web): Autoregressive Generative Models with Deep Learning
    • Rémi Bardenet (CNRS – Université de Lille, web)
  • Thursday, April 6th, 14:30-18:00: High-dimensional & big data
    • John W. Emerson (Yale University, web): Scalable Programming Strategies for Massive Data in R
    • Grégory Nuel (CNRS – Université Pierre et Marie Curie, web): An Adaptive Ridge Procedure for L0 Regularization and Applications
    • Sébastien Loustau (artfact, web): Real time community detection in large networks
  • Thursday, April 6th, 18:00-21:00: Poster session
  • Friday, April 7th, 9:00-12:30: Text mining
    • Martin Jaggi (Ecole Polytechnique Fédérale de Lausanne, web)
    • Benjamin Piwowarski (CNRS – Université Pierre et Marie Curie, web)
    • Xavier Tannier (Université Paris Sud, web)
  • Friday, April 6th, 14:30-16:30: New and future problems in statistical learning
    • Christophe Biernacki (Université de Lille, web)
    • Christian Robert (Université Paris Dauphine, web): ABC random forests for Bayesian parameter inference

Scientific committee:

Charles Bouveyron (Univ. Paris Descartes), Mathieu Emily (Agrocampus Ouest), Chloé Friguet (Univ. Bretagne-Sud), Julien Jacques (Univ. Lyon), Pierre Latouche (Univ. Paris 1)