ia amieslogo-grand-lyon-la-metropole logo-madicssiteon0-e5814google_2015_logo_detail

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 2017. Statlearn’17 is a conference of the French Society of Statistics (SFdS). The talks will be in English. 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 are open here.


The tutorial will held on ‘Porte des Alpes’, which is situated in the Bron area of Lyon near the ‘Porte des Alpes’ commercial centre (see details here to come at ‘Porte des Alpes’ Campus and here for the map of this Campus). The room is 3.214, on the second floor of “Bâtiment 3 de l’IUT”.

The workshop will held in “Grand Amphithéâtre” on ‘Berges du Rhône’ campus, which is situated in Lyon 7 (fr. 7ème arrondissement), in the faculties neighbourhood, in the center of town (see details here).

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
    • 8:30-09:00: registration
    • 9:00-10:00: Nicolas Vayatis (Ecole Normale Supérieure de Cachan, web): Diffusion phenomena in networks : virality, influence and control
    • 10:00-10:30: coffee
    • 10:30-11:30: Hugo Larochelle (Google, web): Autoregressive Generative Models with Deep Learning
    • 11:30-12:30: Rémi Bardenet (CNRS – Université de Lille, web): On MCMC methods for tall data
  • Thursday, April 6th, 14:30-18:00: High-dimensional & big data
    • 14:30-15:30: John W. Emerson (Yale University, web): Scalable Programming Strategies for Massive Data in R
    • 15:30-16:00: coffee
    • 16:00-17:00: Grégory Nuel (CNRS – Université Pierre et Marie Curie, web): An Adaptive Ridge Procedure for L0 Regularization and Applications
    • 17:00-18:00: Sébastien Loustau (artfact, web): Real time community detection in large networks
  • Thursday, April 6th, 18:00-21:00: Poster session and cocktail
  • Friday, April 7th, 9:00-12:30: Text mining
    • 9:00-10:00: Martin Jaggi (Ecole Polytechnique Fédérale de Lausanne, web): From Word Vectors to Sentence Representations
    • 10:00-10:30: coffee
    • 10:30-11:30: Benjamin Piwowarski (CNRS – Université Pierre et Marie Curie, web): Novelties and limits of neural approaches for information access
    • 11:30-12:30: Xavier Tannier (Université Paris Sud, web): NLP-driven Data Journalism: Event-based Extraction and Aggregation of International Alliances Relations
  • Friday, April 7th, 14:30-16:30: New and future problems in statistical learning
    • 14:30-15:30: Christophe Biernacki (Université de Lille, web): About two disinherited sides of statistics: data units and computational saving
    • 15:30-16:30: 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)