I am Professor of Applied Mathematics. I have a dual affiliation between the LMBP laboratory of UCA in Aubière which is part of the MIAI institute located in Grenoble, one of the largest AI cluster in France, and the DepMAP of Ecole Polytechnique (part time) in Palaiseau. I am also a member of the Institut Universitaire de France (IUF) where I hold a chair in Mathematics. I am an associate Inria researcher and I am a member of the Maasai team at Centre Inria d'Université Côte d'Azur in Sophia Antipolis. Previously, I worked at the MAP5 laboratory of UPC (Université Paris Cité) as a Professor. I was also a visiting senior researcher at Mines Paris PSL. I am a reviewer for journals and conferences including JMLR, JASA, PNAS, Biometrika, JRSS-B, JRSS-C, NeurIPS, ICML, AISTATS. I serve as an associate editor for the Bayesian Analysis Journal. Previously, I served as an associate editor for the Journal of the Royal Statistical Society. I am an expert for the European research council. I co-invented and developed the Linkage, Topix, and Indago softwares. I teach statistics and machine learning at UCA and Ecole Polytechnique. I am also responsible along with P. A. Mattei of the course "introduction to probabilistic graphical models and deep generative models" of the master MVA of ENS Paris Saclay, the former course of F. Bach, G. Obozinski, and N. Chopin.
I am also deeply involved in the knowledge transfer of the algorithms / softwares I develop. I have a patent in the US and I am involved in entrepreneurial projects.
My research interests include:
* Statistical / Machine learning on networks, texts, and heterogenous data
* Statistical / Machine learning in high dimensions
* Deep graphical modelling / Deep learning
* Statistical learning with processes
* Tests and proofs
List of former and current PhD students:
* Rui Ma
* Seydina-Ousmane Niang
* Romain Fayat
* Pierre Linchamps
* Rémi Boutin
List of former and current postdoc and engineers:
Recent publications
Metodiev, M.; Perrot-Dockès, M.; Ouadah, S.; Irons, N. J.; Latouche, P.; Raftery, A. E. Easily Computed Marginal Likelihoods from Posterior Simulation Using the THAMES Estimator. Bayesian Analysis 2025, 20 (3), 1003–1030. https://doi.org/10.1214/24-BA1422.
Liang, D.; Corneli, M.; Bouveyron, C.; Latouche, P.; Yin, J. The Multiplex Deep Latent Position Model for the Clustering of Nodes in Multiview Networks. Neurocomputing 2025, 655, 131336. https://doi.org/10.1016/j.neucom.2025.131336.
Liang, D.; Corneli, M.; Bouveyron, C.; Latouche, P. Clustering by Deep Latent Position Model with Graph Convolutional Network. Adv Data Anal Classif 2025, 19 (1), 237–270. https://doi.org/10.1007/s11634-024-00583-9.
Boutin, R.; Latouche, P.; Bouveyron, C. The Deep Latent Position Topic Model for Clustering and Representation of Networks with Textual Edges. Scandinavian Journal of Statistics 2025. https://doi.org/10.48550/arXiv.2304.08242.
Boutin, R.; Latouche, P.; Bouveyron, C. The Deep Latent Position Block Model for Block Clustering and Latent Representation of Nodes in Networks. Stat Comput 2025, 35 (5), 151. https://doi.org/10.1007/s11222-025-10679-7.
Media
A. Mestre, "Eric Zemmour, nouveau président de la fachosphère ?". In: LeMonde (2022), p1. and p. 16-17 [web].
S. Laurent, "Comment la gauche sociale-démocrate a perdu la bataille des réseaux sociaux". In: LeMonde (2022), p. 16-17 [web].
S. Auffret, "Brigitte Macron et Jean-Michel Trogneux, itinéraire d’une infox délirante". In: LeMonde (2022), p. 16-17 [web].
M. Goar, N. Chapuis, "Présidentielle 2022 : faut-il se couper de Twitter, huis clos politique devenu hostile ?". In: LeMonde (2022), p. 1 and p. 16-19 [web].
M. Koppe, "Que vaut vraiment le poids politique sur Twitter". In: CNRS le journal, de la découverte à l’innovation (2022) [web].
P. Latouche, C. Bouveyron, D. Marié, G. Fouetillou, "Présidentielle 2017 : une réorganisation politique du web social ?". In: TheConversation (2017) [web].
P. Latouche, C. Bouveyron, D. Marié, G. Fouetillou, "Présidentielle 2017 : une réorganisation politique du web social ?". In: Data analytics post (2017) [web].
P. Latouche, C. Bouveyron, D. Marié, G. Fouetillou, "Présidentielle 2017 : une réorganisation politique du web social ?". In: Panthéon-Sorbonne magazine (2017).
P. Latouche, C. Bouveyron, "Les échanges de données au peigne fin". In: CNRS, le journal (2017), p. 9 [web].
P. Latouche, C. Bouveyron, "Des réseaux, des textes, et de la statistique !". In: Lettre de l’INSMI (2016).
Preprints
Fayat, R., Sarraudy, M., Léna, C., Popa, D., Latouche, P., & Dugué, G. P. (2025). Fine decomposition of rodent behavior via unsupervised segmentation and clustering of inertial signals (p. 2024.09.20.613901). bioRxiv. https://doi.org/10.1101/2024.09.20.613901
Metodiev, M., Irons, N. J., Perrot-Dockès, M., Latouche, P., & Raftery, A. E. (2025). Easily computed marginal likelihoods for multivariate mixture models using the THAMES estimator (No. arXiv:2504.21812). arXiv. https://doi.org/10.48550/arXiv.2504.21812
Metodiev, M., Perrot-Dockès, M., Ouadah, S., Fosdick, B. K., Robin, S., Latouche, P., & Raftery, A. E. (2024). A structured estimator for large covariance matrices in the presence of pairwise and spatial covariates (No. arXiv:2411.04520). arXiv. https://doi.org/10.48550/arXiv.2411.04520
Niang, S. O., Bouveyron, C., Corneli, M., Latouche, P., & Boutin, R. (2024). The deep latent position block model for the clustering of nodes in multi-graphs. https://hal.science/hal-04840577
Yapi, A., Latouche, P., Guillin, A., & Bailly, Y. (2025). A new machine learning framework for occupational accidents forecasting with safety inspections integration (No. arXiv:2507.00089). arXiv. https://doi.org/10.48550/arXiv.2507.00089
Publications
Metodiev, M.; Perrot-Dockès, M.; Ouadah, S.; Irons, N. J.; Latouche, P.; Raftery, A. E. Easily Computed Marginal Likelihoods from Posterior Simulation Using the THAMES Estimator. Bayesian Analysis 2025, 20 (3), 1003–1030. https://doi.org/10.1214/24-BA1422.
Liang, D.; Corneli, M.; Bouveyron, C.; Latouche, P.; Yin, J. The Multiplex Deep Latent Position Model for the Clustering of Nodes in Multiview Networks. Neurocomputing 2025, 655, 131336. https://doi.org/10.1016/j.neucom.2025.131336.
Liang, D.; Corneli, M.; Bouveyron, C.; Latouche, P. Clustering by Deep Latent Position Model with Graph Convolutional Network. Adv Data Anal Classif 2025, 19 (1), 237–270. https://doi.org/10.1007/s11634-024-00583-9.
Boutin, R.; Latouche, P.; Bouveyron, C. The Deep Latent Position Topic Model for Clustering and Representation of Networks with Textual Edges. Scandinavian Journal of Statistics 2025. https://doi.org/10.48550/arXiv.2304.08242.
Boutin, R.; Latouche, P.; Bouveyron, C. The Deep Latent Position Block Model for Block Clustering and Latent Representation of Nodes in Networks. Stat Comput 2025, 35 (5), 151. https://doi.org/10.1007/s11222-025-10679-7.
Liang, D.; Corneli, M.; Bouveyron, C.; Latouche, P. The Graph Embedded Topic Model. Neurocomputing 2023, 562, 126900. https://doi.org/10.1016/j.neucom.2023.126900.
Boutin, R.; Bouveyron, C.; Latouche, P. Embedded Topics in the Stochastic Block Model. Stat Comput 2023, 33 (5), 95. https://doi.org/10.1007/s11222-023-10265-9.
Linchamps, P.; Stoetzel, E.; Robinet, F.; Hanon, R.; Latouche, P.; Cornette, R. Bioclimatic Inference Based on Mammal Community Using Machine Learning Regression Models: Perspectives for Paleoecological Studies. Front. Ecol. Evol. 2023, 11. https://doi.org/10.3389/fevo.2023.1178379.
Leroy, A.; Latouche, P.; Guedj, B.; Gey, S. Cluster-Specific Predictions with Multi-Task Gaussian Processes. Journal of Machine Learning Research 2023, 24 (5), 1–49. https://www.jmlr.org/papers/volume24/20-1321/20-1321.pdf.
Leroy, A.; Latouche, P.; Guedj, B.; Gey, S. MAGMA: Inference and Prediction Using Multi-Task Gaussian Processes with Common Mean. Mach Learn 2022, 111 (5), 1821–1849. https://doi.org/10.1007/s10994-022-06172-1.
Ouadah, S.; Latouche, P.; Robin, S. Motif-Based Tests for Bipartite Networks. Electronic Journal of Statistics 2022, 16 (1), 293–330. https://doi.org/10.1214/21-EJS1944.
Jouvin, N.; Latouche, P.; Bouveyron, C.; Bataillon, G.; Livartowski, A. Greedy Clustering of Count Data through a Mixture of Multinomial PCA. Comput Stat 2021, 36 (1), 1–33. https://doi.org/10.1007/s00180-020-01008-9.
Liang, D.; Corneli, M.; Bouveyron, C.; Latouche, P. DeepLTRS: A Deep Latent Recommender System Based on User Ratings and Reviews. Pattern Recognition Letters 2021, 152, 267–274. https://doi.org/10.1016/j.patrec.2021.10.022.
Jouvin, N.; Bouveyron, C.; Latouche, P. A Bayesian Fisher-EM Algorithm for Discriminative Gaussian Subspace Clustering. Stat Comput 2021, 31 (4), 44. https://doi.org/10.1007/s11222-021-10018-6.
Côme, E.; Jouvin, N.; Latouche, P.; Bouveyron, C. Hierarchical Clustering with Discrete Latent Variable Models and the Integrated Classification Likelihood. Adv Data Anal Classif 2021, 15 (4), 957–986. https://doi.org/10.1007/s11634-021-00440-z.
Corneli, M.; Bouveyron, C.; Latouche, P. Co-Clustering of Ordinal Data via Latent Continuous Random Variables and Not Missing at Random Entries. Journal of Computational and Graphical Statistics 2020, 29 (4), 771–785. https://doi.org/10.1080/10618600.2020.1739533.
Ouadah, S.; Robin, S.; Latouche, P. Degree-Based Goodness-of-Fit Tests for Heterogeneous Random Graph Models: Independent and Exchangeable Cases. Scandinavian Journal of Statistics 2020, 47 (1), 156–181. https://doi.org/10.1111/sjos.12410.
Bouveyron, C.; Latouche, P.; Mattei, P.-A. Exact Dimensionality Selection for Bayesian PCA. Scandinavian Journal of Statistics 2020, 47 (1), 196–211. https://doi.org/10.1111/sjos.12424.
Bergé, L. R.; Bouveyron, C.; Corneli, M.; Latouche, P. The Latent Topic Block Model for the Co-Clustering of Textual Interaction Data. Computational Statistics & Data Analysis 2019, 137, 247–270. https://doi.org/10.1016/j.csda.2019.03.005.
Corneli, M.; Bouveyron, C.; Latouche, P.; Rossi, F. The Dynamic Stochastic Topic Block Model for Dynamic Networks with Textual Edges. Stat Comput 2019, 29 (4), 677–695. https://doi.org/10.1007/s11222-018-9832-4.
Rastelli, R.; Latouche, P.; Friel, N. Choosing the Number of Groups in a Latent Stochastic Blockmodel for Dynamic Networks. Network Science 2018, 6 (4), 469–493. https://doi.org/10.1017/nws.2018.19.
Corneli, M.; Latouche, P.; Rossi, F. Multiple Change Points Detection and Clustering in Dynamic Networks. Stat Comput 2018, 28 (5), 989–1007. https://doi.org/10.1007/s11222-017-9775-1.
Latouche, P.; Robin, S.; Ouadah, S. Goodness of Fit of Logistic Regression Models for Random Graphs. Journal of Computational and Graphical Statistics 2018, 27 (1), 98–109. https://doi.org/10.1080/10618600.2017.1349663.
Bouveyron, C.; Latouche, P.; Zreik, R. The Stochastic Topic Block Model for the Clustering of Vertices in Networks with Textual Edges. Stat Comput 2018, 28 (1), 11–31. https://doi.org/10.1007/s11222-016-9713-7.
Bouveyron, C.; Latouche, P.; Mattei, P.-A. Bayesian Variable Selection for Globally Sparse Probabilistic PCA. Electronic Journal of Statistics 2018, 12 (2), 3036–3070. https://doi.org/10.1214/18-EJS1450.
Zreik, R.; Latouche, P.; Bouveyron, C. The Dynamic Random Subgraph Model for the Clustering of Evolving Networks. Comput Stat 2017, 32 (2), 501–533. https://doi.org/10.1007/s00180-016-0655-5.
Wyse, J.; Friel, N.; Latouche, P. Inferring Structure in Bipartite Networks Using the Latent Blockmodel and Exact ICL. Network Science 2017, 5 (1), 45–69. https://doi.org/10.1017/nws.2016.25.
Latouche, P.; Bouveyron, C.; Marie, D.; Fouetillou, G. Présidentielle 2017 : l’analyse des tweets renseigne sur les recompositions politiques. Statistique et Société 2017, 5 (3), 39–44. https://statistique-et-societe.fr/index.php/stat_soc/article/view/660/626.
Latouche, P.; Robin, S. Variational Bayes Model Averaging for Graphon Functions and Motif Frequencies Inference in W-Graph Models. Stat Comput 2016, 26 (6), 1173–1185. https://doi.org/10.1007/s11222-015-9607-0.
Corneli, M.; Latouche, P.; Rossi, F. Block Modelling in Dynamic Networks with Non-Homogeneous Poisson Processes and Exact ICL. Soc. Netw. Anal. Min. 2016, 6 (1), 55. https://doi.org/10.1007/s13278-016-0368-3.
Corneli, M.; Latouche, P.; Rossi, F. Exact ICL Maximization in a Non-Stationary Temporal Extension of the Stochastic Block Model for Dynamic Networks. Neurocomputing 2016, 192, 81–91. https://doi.org/10.1016/j.neucom.2016.02.031.
Latouche, P.; Mattei, P.-A.; Bouveyron, C.; Chiquet, J. Combining a Relaxed EM Algorithm with Occam’s Razor for Bayesian Variable Selection in High-Dimensional Regression. Journal of Multivariate Analysis 2016, 146, 177–190. https://doi.org/10.1016/j.jmva.2015.09.004.
Côme, E.; Latouche, P. Model Selection and Clustering in Stochastic Block Models Based on the Exact Integrated Complete Data Likelihood. Statistical Modelling 2015, 15 (6), 564–589. https://doi.org/10.1177/1471082X15577017.
Zreik, R.; Latouche, P.; Bouveyron, C. Classification automatique de réseaux dynamiques avec sous-graphes : étude du scandale Enron. Journal de la société française de statistique 2015, 156 (3), 166–191. https://www.numdam.org/item/JSFS_2015__156_3_166_0.pdf.
Latouche, P.; Birmelé, E.; Ambroise, C. Model Selection in Overlapping Stochastic Block Models. Electronic Journal of Statistics 2014, 8 (1), 762–794. https://doi.org/10.1214/14-EJS903.
Jernite, Y.; Latouche, P.; Bouveyron, C.; Rivera, P.; Jegou, L.; Lamassé, S. The Random Subgraph Model for the Analysis of an Ecclesiastical Network in Merovingian Gaul. The Annals of Applied Statistics 2014, 8 (1), 377–405. https://doi.org/10.1214/13-AOAS691.
Latouche, P.; Birmelé, E.; Ambroise, C. Variational Bayesian Inference and Complexity Control for Stochastic Block Models. Statistical Modelling 2012, 12 (1), 93–115. https://doi.org/10.1177/1471082X1001200105.
Latouche, P.; Birmelé, E.; Ambroise, C. Overlapping Stochastic Block Models with Application to the French Political Blogosphere. The Annals of Applied Statistics 2011, 5 (1), 309–336. https://projecteuclid.org/journalArticle/Download?urlId=10.1214%2F10-AOAS382.
- thamesmix (R package): truncated harmonic mean estimator of the marginal likelihood for mixtures
- 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
- 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
Pierre Latouche
pierre.latouche at uca dot fr
pierre.latouche at polytechnique dot edu
pierre.latouche at math dot cnrs dot fr
pierre.latouche at inria dot fr
.