Past edition:  Statlearn’16 – Vannes, April 2016, 7&8

Program:

PDF version of the program: [pdf]

Thursday, 7th April 2016

9h00 – 9h30 Welcome and opening session

9h30- 12h30 Session 1: Topic Modeling / Text mining 

  • Cédric Archambeau (University College London – UK / Amazon – Berlin – Germany) –  Latent IBP Compound Dirichlet Allocation: Sparse Topic Models Fit for Natural Languages. – SLIDES
  • Julien Velcin (ERIC / Université Lyon 2 – Lyon – France) – Joint extraction of topics and sentiments – SLIDES
  • Quentin Pleplé (EPSI, Big Datext et Short Edition – Grenoble – France) - Interactive Topic  Modeling – SLIDES

Abstracts

14h00- 17h30 Session 2: High dimension and applications 

  • Ernest Fokoué (Rochester Institute of Technology – USA) - Statistical Machine Learning with High Multidimensional Arrays of Data
  • Mathieu Fauvel (INRA et INPT-ENSAT – Toulouse – France) – Spectral-spatial classification of high-dimensional remote sensing images – SLIDES
  • Emeline Perthame (Inria Grenoble – Rhone-Alpes – France) – Variable selection for correlated data in high dimension using decorrelation methods – SLIDES

Abstracts

18h00- 20h00 Poster Session (with local food specialties)

Friday, 8th April 2016

9h00- 12h00 Session 3: Optimal transport and learning

  • Marco Cuturi (Kyoto University – Japan) – Regularized Optimal Transport and Applications – SLIDES
  • Jérémie Bigot (Université de Bordeaux et Institut de Mathématiques de Bordeaux – France) – A Forward-Backward algorithm for geodesic PCA of histograms in the Wasserstein space – SLIDES
  • Rémi Flamary (Université de Nice Sophia-Antipolis – France) – Optimal transport for domain adaptation

Abstracts

13h30 -16h00 Session 4: Recent advances in statistical learning

Abstracts