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STATLEARN 2013 : "Challenging problems in Statistical Learning"

Statlearn 2013

The workshop Statlearn’13 will focus on the challenging problems in Statistical Learning and will be held in Bordeaux on April 8-9, 2013 . This workshop aims to summarize the new and future problems in statistical learning and to give a good idea of what already exists for dealing with these problems.This event is co-organized by Inria Bordeaux (CQFD and ALEA teams), the University of Bordeaux, IRSTEA, the University Paris 1 Panthéon-Sorbonne and CNRS, with the support of the Société Française de Statistique (SFdS).

  • Date : 8/04/2013 to 9/04/2013
  • Place : Place de la Victoire, Bordeaux
  • Guest(s) : ALEA, CQFD

This workshop splits into 4 half days (with 3 speakers per half day) on the following topics:

  • 1st half-day : Statistical learning  for  networks

In recent years, many mathematical works were done on networks. This session will present recent advances on this topic using statistical learning.

  • 2nd half-day :   Bayesian methods   and statistical learning

Bayesian methods are increasingly used in statistical learning. This session will present recent works on this topic as well as future challenges.

  • Third   half-day : Statistical learning   on   high-dimensional data and   applications

Learning a statistical model when the dimension p is larger than the number of observations n is a topical problem. In particular, the statistical processing of data from DNA sequencing falls into this category of problem. This session will provide an update on recent advances in this field across applications.

  • Fourth   half-day:   New   and Future   problems   in   Statistical Learning

This session aims to address the emerging issues and future that currently appear in statistical learning. These problems are often revealed by new requirements in some fields of application and this session will aim to stimulate the interest of young researchers to these specific problems.


Keywords: Research Teams CQFD and ALEA Conference Statistical Learning Statistiques Inria Bordeaux - Sud-Ouest