Pierre Latouche

mardi 13 septembre 2011
par  Pierre Latouche

Curriculum vitae

Contact information

Maître de Conférences (Associate Professor) in Applied Mathematics
Laboratoire SAMM, Université Paris 1 (Panthéon-Sorbonne)

- Google scholar profile
- Email : pierre.latouche@univ-paris1.fr
Address : Laboratoire SAMM, Université Paris 1
90 rue de Tolbiac, 75013 Paris, France
Phone : +33 (0)1 44 07 88 26 - Fax : +33 (0)1 44 07 89 25


News | Publications | Research interest | Students | Teaching | Co-authors

News

Publications (see on Hal)

Papers (15)

  1. P. Latouche, S. Robin, and S. Ouadah. "Goodness of fit of logistic regression models for random graphs". In : Journal of Computational and Graphical Statistics (to appear).
  2. M. Corneli, P. Latouche, and F. Rossi. "Multiple change points detection and clustering in dynamic networks". In : Statistics and Computing (to appear).
  3. J. Wyse, N. Friel, and P. Latouche. "Inferring structure in bipartite networks using the latent block model and exact ICL". In : Network Science 5.1 (2017), p. 45-69.
  4. R. Zreik, P. Latouche, and C. Bouveyron. "The dynamic random subgraph model for the clustering of evolving networks". In : Computational Statistics (2016), p. 1-33.
  5. P ; Latouche and S. Robin. "Variational Bayes model averaging for gryphon functions and motifs frequencies inference in W-graph models". In : Statistics and Computing 26.6 (2016), p. 1173-1185.
  6. P. Latouche, P-A Mattei et al. "Combining a relaxed EM algorithm with Occam’s razor for Bayesian variable selection in high dimension regression". In : Journal of Multivariate Analysis 146 (2016), p. 177-190.
  7. M. Corneli, P. Latouche, and F. Rossi. "Exact ICL maximisation in a non stationary temporal extension of the stochastic block model for dynamic networks". In : Neurocomputing 192 (2016), p. 81-91.
  8. M. Corneli, P. Latouche, and F. Rossi. "Block modelling in dynamic networks with non homogenous Poisson processes and exact ICL". In : Social Network Analysis and Mining 6.1 (2016), p. 55-85.
  9. C. Bouveyron, P. Latouche, and R. Zreik. "The stochastic topic block model for the clustering of vertices in networks with textual edges". In : Statistics and Computing (2016), p. 1-21.
  10. R. Zreik, P. Latouche, and C. Bouveyron. "Classification automatique de réseaux dynamiques avec sous-graphes : étude du scandale Enron". In : Journal de la Société Française de Statistique 156.3 (2015), p. 166-191.
  11. E. Côme and P. Latouche. "Model selection and clustering in stochastic block models based on the exact integrated complete data likelihood". In : Statistical Modelling 15.6 (2015), p. 564-589.
  12. P. Latouche, E. Birmelé, and C. Ambroise. "Model selection in overlapping stochastic block models". In : Electronic Journal of Statistics 8.1 (2014), p. 762-794.
  13. Y. Jernite, P. Latouche et al. "The random subgraph model for the analysis of an ecclesiastical network in Merovingian Gaul". In : Annals of Applied Statistics 8.1 (2014), p. 377-405.
  14. P. Latouche, E. Birmelé, and C. Ambroise. "Variational Bayesian inference and complexity control for stochastic block models". In : Statistical Modelling 12.1 (2012), p. 93-115.
  15. P. Latouche, E. Birmelé, and C. Ambroise. "Overlapping stochastic block models with application to the French political blogosphere". In : Annals of Applied Statistics 5.1 (2011), p. 309-336.

Preprints (4)

  1. P. Latouche, C. Bouveyron, and P-A. Mattei. "Bayesian variable selection for globally sparse probabilistic PCA".
  2. P. Latouche, C. Bouveyron, and P-A. Mattei. "Exact dimensional selection for Bayesian PCA".
  3. S. Ouadah, S. Robin, and P. Latouche. "A degree-based goodness-of-fit test for heterogeneous random graph models".
  4. R. Rastelli, P. Latouche, and N. Friel. "Choosing the number of groups in a latent stochastic block model for dynamic networks".

Chapters (3)

  1. R. Zreik, P. Latouche, and C. Bouveyron. "Cluster identification in maritime flows with stochastic methods". In : Maritime Networks : Spatial Structures and Time Dynamics. Routledge, 2015.
  2. P. Latouche, E. Birmelé, and C. Ambroise. "Overlapping clustering methods for networks". In : Handbook of Mixed Membership Models and Their Applications. Chapman et Hall/CRC, 2014.
  3. P. Latouche, E. Birmelé, and C. Ambroise. "Bayesian methods for graph clustering". In : Advances in Data Handling and Business Intelligence". Springer, 2009.

Research interests

  • Network analysis
  • Sparse inference
  • High dimensional data
  • Graphical models
  • Model selection
  • Bayesian analysis
  • Variational approaches

Students

  • Nicolas Jouvin (ENS Cachan, MsC) 2017
  • Marco Corneli (current PhD student) 2014
  • Pierre-Alexandre Mattei (current PhD student) 2014
  • Rawyia Zreik (former PhD student) 2013
  • Charles Abner-Dadi (ENS Cachan, MsC) 2013
  • Ragheda el Hassan (ENS Cachan, MsC) 2013
  • Yacine Jernite (ENS Cachan, MsC) 2012
  • Laetitia Nouedoui (Paris 1, MsC) 2012
  • Anne-Claire (ENSAE, Stage) 2009

Teaching

- Probability and statistics
- Algebra, numerical analysis
- Datamining / Machine learning
- Programming

Some of my co-authors :

Christophe Ambroise, Etienne Birmelé, Charles Bouveyron, Julien Chiquet, Etienne Côme, Nial Friel, Sarah Ouadah, Stéphane Robin, Fabrice Rossi, Jason Wyse

Softwares :

- Linkage (plate-forme web) : Analysis of networks with textual edges
- Spinyreg (R package) : spare regression using spike and slab prior distributions
- Mixer (R package written in C++) : variational inference techniques for the stochastic bloc model. Can be used to classify the vertices of a network depending on their connection profiles
- Rambo (R package) : estimate the parameters, the number of classes and cluster vertices of a random network into groups with homogeneous connection profiles. The clustering is performed for directed graphs with typed edges (edges are assumed to be drawn from multinomial distributions) for which a partition of the vertices is available
- Netlab (Matlab) : some of the most important pattern recognition algorithms described by C.M. Bishop in “Neural Networks for Pattern Recognition” (Oxford University Press, 1995)
- Genoscript (WebObject) : a Web environment for transcriptom analysis


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