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)

En délégation CNRS au laboratoire MAP5, Université Paris Descartes
- 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


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

Linkage.fr

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 (4)

  1. R. Zreik, C. Ducruet, C. Bouveyron, and P. Latouche. "Cluster dynamics in the collapsing Soviet shipping network". In : Advances in Shipping Data Analysis and Modeling Tracking and Mapping Maritime Flows in the Age of Big Data. Routledge, 2017.
  2. R. Zreik, P. Latouche, C. Bouveyron, and C. Ducruet. "Cluster identification in maritime flows with stochastic methods". In : Maritime Networks : Spatial Structures and Time Dynamics. Routledge, 2015.
  3. 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.
  4. 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, current PhD student) 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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