Pierre Latouche

Professor of Statistics and Machine Learning
Laboratoire MAP5
Université de Paris

Ecole Polytechnique

I am Professor of Applied Mathematics at the MAP5 laboratory of the University of Paris. My group is involved in problems related to learning and statistics. Until 2018, I was Assistant Professor and then Associate Professor at Université Paris 1 Panthéon-Sorbonne.  I both studied in France and in the UK. I am a reviewer for journals and conferences including JASA, PNAS, Biometrika, NeurIPS, ICML, JRSS-B, JRSS-C. I serve as an associate editor for the Journal of the Royal Statistical Society and for the Bayesian Analysis Journal. I am an expert for the European research council. I co-invented and developed the Linkage and Topix softwares. I teach statistics and machine learning at the University of Paris and Ecole Polytechnique where I also have a (part time) professor position. In particular, I am involved in the master Artificial Intelligence & Advanced Visual Computing. Moreover, I am the director of a bachelor in data science at the University of Paris. I am also responsible along with P. A. Mattei (INRIA) of the (deep) probabilistic graphical models course of the master MVA of ENS Paris Saclay.


I am a member of the international working group on model based clustering created by Adrian Raftery from the University of Washington. Meetings are hold regularly in north America and in Europe.  I am also a member of the European cooperation for statistics of network data science action which brings together European researchers of the field.


Since 2016, I have been strongly involved in Cyber defense applications.  


I am also deeply involved in the knowledge transfer of the algorithms / softwares I develop. I have a patent in the US as well as one pending in Europe and I am involved in entrepreneurial projects.


My research interests include: 

* Statistical learning on networks, texts, and heterogenous data

* Statistical learning in high dimensions

* Statistical learning with processes

* Tests and proofs

* Deep graphical modelling / Deep learning

List of former and current postdoc and engineers:

* Carlos Ocanto Dávila

* Stéphane Petiot

* Damien Marié
* Laurent Bergé 

.

News

The very latest news regarding research and teaching

Linkage

A US patent is available for this technology we develop

Topix 

  

Publications


Recent papers

S. Ouadah, P. Latouche, S. Robin. "Motif-based tests for bipartite networks". In: Electronic Journal of Statistics (2021), p. 293-330 [web].

D. Liang, M. Corneli, C. Bouveyron, P. Latouche. "DeepLTRS: A deep latent recommender system based on user ratings and reviews". In: Pattern Recognition Letters (2021), p. 267-274 [web].

N. Jouvin, C. Bouveyron, P. Latouche. "A Bayesian Fisher-EM Algorithm for Discriminative Gaussian Subspace Clustering". In: Statistics and Computing (2020), p. 1-20 [web].

E. Côme, P. Latouche, N. Jouvin, and C. Bouveyron. "Hierarchical clustering with discrete latent variable models and the integrated classification likelihood". In: Journal of Advances in Data Analysis and Classification (2020), p. 957-98 [web].

N. Jouvin, P. Latouche, C. Bouveyron, G. Bataillon, and A. Livartowski. "Greedy clustering of count data through a mixture of multinomial PCA". In: Computational Statistics (2020), p. 1-33 [web]. 

M. Corneli, C. Bouveyron, and P. Latouche. "Co-clustering of ordinal data via latent continuous random variables and a classification EM algorithm". In: Journal of Computational and Graphical Statistics (2020), p. 771-785 [web]. 


Preprints

D. Liang, M. Corneli, C. Bouveyron, P. Latouche. "Clustering by deep latent position model with graph convolution network".

A. Leroy, P. Latouche, B. Guedj, and S. Gey. "Cluster-Specific Predictions with Multi-Task Gaussian Processes" [web].

A. Leroy, P. Latouche, B. Guedj, and S. Gey. "MAGMA: Inference and prediction with multi-task Gaussian processes" [web].


Papers

S. Ouadah, P. Latouche, S. Robin. "Motif-based tests for bipartite networks" In: Electronic Journal of Statistics (2021), p. 293-330 [web].


D. Liang, M. Corneli, C. Bouveyron, P. Latouche. "DeepLTRS: A deep latent recommender system based on user ratings and reviews". In: Pattern Recognition Letters (2021), p. 267-274 [web].


N. Jouvin, C. Bouveyron, P. Latouche. "A Bayesian Fisher-EM Algorithm for Discriminative Gaussian Subspace Clustering" In: Statistics and Computing (2020), p. 1-20 [web].


E. Côme, P. Latouche, N. Jouvin, and C. Bouveyron. "Hierarchical clustering with discrete latent variable models and the integrated classification likelihood". In: Journal of Advances in Data Analysis and Classification (2020), p. 957-986 [web].


N. Jouvin, P. Latouche, C. Bouveyron, G. Bataillon, and A. Livartowski. "Greedy clustering of count data through a mixture of multinomial PCA". In: Computational Statistics (2020), p. 1-33 [web]. 



M. Corneli, C. Bouveyron, and P. Latouche. "Co-clustering of ordinal data via latent continuous random variables and a classification EM algorithm". In: Journal of Computational and Graphical Statistics (2020), p. 771-785 [
web]. 



S. Ouadah, S. Robin, and P. Latouche. "A degree-based goodness-of-fit test for heterogeneous random graph models". In: Scandinavian Journal of Statistics (2019), p. 156-181 [
web].



L. Bergé, C. Bouveyron, C. Corneli, and P. Latouche. "The latent topic block model for the co-clustering of textual interaction data". In: Journal of Computational Statistics and Data Analysis (2019), p. 247-270 [
web].



G. Fouetillou, P. Latouche, C. Bouveyron, and P-A. Mattei. "Exact dimensionalality selection for Bayesian PCA". In: Journal of Statistical Planning and Inference (2019), p. 196-211 [
web].



M. Corneli, C. Bouveyron, P. Latouche, and F. Rossi. "The dynamic stochastic topic block model for time evolving networks with textual edges". In: Statistics and Computing (2019), in press [web].



P. Latouche, C. Bouveyron, and P-A. Mattei. "Bayesian variable selection for globally sparse probabilistic PCA". In: Electronic Journal of Statistics 12.2 (2018), p. 3036-3070 [
web].



R. Rastelli, P. Latouche, and N. Friel. "Choosing the number of groups in a latent stochastic block model for dynamic networks". In: Network Science 6.4 (2018), p. 469-493 [
web].



P. Latouche, S. Robin, and S. Ouadah. "Goodness of fit of logistic regression models for random graphs". In: Journal of Computational and Graphical Statistics (2018), p. 98-109 [
web].



M. Corneli, P. Latouche, and F. Rossi. "Multiple change points detection and clustering in dynamic networks". In: Statistics and Computing (2018), p. 989-1007 [
web].



P. Latouche, C. Bouveyron, D. Marié, and G. Fouetillou. "Présidentielle 2017 : l’analyse des tweets renseigne sur les recompositions politiques". In: Statistique et Société 5.3 (2017) [
web].



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 [
web].



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 [
web].



P ; Latouche and S. Robin. "Variational Bayes model averaging for graphon functions and motifs frequencies inference in W-graph models". In: Statistics and Computing 26.6 (2016), p. 1173-1185 [
web].



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 [
web].



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 [
web].



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 [
web].



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 [
web].



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 [
web].



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 [
web].



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 [
web].



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 [
web].



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 [
web].



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 [
web].




Chapters

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) [web].



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) [
web].



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) [
web].



P. Latouche, E. Birmelé, and C. Ambroise. "Bayesian methods for graph clustering". In : Advances in Data Handling and Business Intelligence". Springer (2009) [
web].



Popularization

P. Latouche, C. Bouveyron, D. Marié, and G. Fouetillou. "Présidentielle 2017 : une réorganisation politique du web social ?". In Data analytics post (2017).



P. Latouche, C. Bouveyron, D. Marié, and G. Fouetillou. "Présidentielle 2017 : une réorganisation politique du web social ?". Panthéon-Sorbonne magazine (2017).



P. Latouche and C. Bouveyron. "Les échanges de données au peigne fin". CNRS, le journal (2017).



P. Latouche and C. Bouveyron. "Des réseaux, des textes, et de la statistique !". Lettre de l’INSMI (2016).



Softwares

- Greedy (R package): an ensemble of algorithms that enable the clustering of networks and data matrix such as document/term matrix with different type of generative model

- MAGMA (R package): inference and prediction with multi-task Gaussian processes

- MAGMAclust (R package): inference and prediction with cluster-specific multi-task
Gaussian processes

- Topix (web):  allows to summarize massive and possibly extremely sparse data bases involving text

- FisherEM (R package): efficient method for the clustering of high-dimensional data

- ordinalLBM (R package): implements functions for simulation and estimation of the ordinal latent block model (OLBM)

MoMPCA (R package): inference and clustering for mixture of multinomial principal component analysis

Linkage (web): analysis of networks with textual edges

- GSPPCA (R package): implements the GSPPCA algorithm for high-dimensional unsupervised feature selection 

- Spinyreg (R package): spare regression using spike and slab prior distributions

- GofNetwork (R package): assess the goodness of fit of network models in the presence of covariates

- 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