I am full Professor of Applied Mathematics. I have a dual affiliation between the LMBP laboratory of UCA and the DepMAP of Ecole Polytechnique (part time). 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. 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, AISTATS, 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 UCA and Ecole Polytechnique. I am also responsible along with P. A. Mattei (INRIA) 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.
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:
* Romain Fayat
* Pierre Linchamps
* Rémi Boutin
List of former and current postdoc and engineers:
Recent publications
Leroy, A., Latouche, P., Guedj, B., Gey, S. (2023). Cluster-Specific Predictions with Multi-Task Gaussian Processes. In ICML 2023.
Boutin, R., Bouveyron, C., Latouche, P. (2023). Embedded topics in the stochastic block model. Statistics and Computing, 33(5), 1-20 [web].
Linchamps, P., Stoetzel, E., Robinet, F., Hanon, R., Latouche, P., Cornette, R. (2023). Bioclimatic inference based on mammal community using machine learning regression models: perspectives for paleoecological studies. Frontiers in Ecology and Evolution, 11, 1178379 [web].
Leroy, A., Latouche, P., Guedj, B., Gey, S. (2023). Cluster-Specific Predictions with Multi-Task Gaussian Processes. Journal of Machine Learning Research, 24, [web].
Leroy, A., Latouche, P., Guedj, B., Gey, S. (2022). MAGMA: inference and prediction using multi-task Gaussian processes with common mean. Machine Learning, 111(5), 1821-1849 [web].
Ouadah, S., Latouche, P., Robin, S. (2022). Motif-based tests for bipartite networks. Electronic Journal of Statistics, 16(1), 293-330 [web].
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
Boutin, R., Latouche, P., Bouveyron, C. (2023). The Deep Latent Position Topic Model for Clustering and Representation of Networks with Textual Edges. arXiv preprint [web].
Liang, D., Corneli, M., Bouveyron, C., Latouche, P. (2023). The graph embedded topic model. Hal preprint [web].
Liang, D., Corneli, M., Bouveyron, C., Latouche, P. (2022). Clustering by deep latent position model with graph convolution network [web].
Publications
Leroy, A., Latouche, P., Guedj, B., Gey, S. (2023). Cluster-Specific Predictions with Multi-Task Gaussian Processes. In ICML 2023.
Boutin, R., Bouveyron, C., Latouche, P. (2023). Embedded topics in the stochastic block model. Statistics and Computing, 33(5), 1-20 [web].
Linchamps, P., Stoetzel, E., Robinet, F., Hanon, R., Latouche, P., Cornette, R. (2023). Bioclimatic inference based on mammal community using machine learning regression models: perspectives for paleoecological studies. Frontiers in Ecology and Evolution, 11, 1178379 [web].
Leroy, A., Latouche, P., Guedj, B., Gey, S. (2023). Cluster-Specific Predictions with Multi-Task Gaussian Processes. Journal of Machine Learning Research, 24 [web].
Leroy, A., Latouche, P., Guedj, B., Gey, S. (2022). MAGMA: inference and prediction using multi-task Gaussian processes with common mean. Machine Learning, 111(5), 1821-1849 [web].
Ouadah, S., Latouche, P., Robin, S. (2022). Motif-based tests for bipartite networks. Electronic Journal of Statistics, 16(1), 293-330 [web].
Liang, D., Corneli, M., Bouveyron, C., Latouche, P. (2021). DeepLTRS: A deep latent recommender system based on user ratings and reviews. Pattern Recognition Letters, 152, 267-274 [web].
Jouvin, N., Bouveyron, C., Latouche, P. (2021). A Bayesian Fisher-EM algorithm for discriminative Gaussian subspace clustering. Statistics and Computing, 31(4), 44 [web].
Côme, E., Jouvin, N., Latouche, P., Bouveyron, C. (2021). Hierarchical clustering with discrete latent variable models and the integrated classification likelihood. Advances in Data Analysis and Classification, 15(4), 957-986 [web].
Jouvin, N., Latouche, P., Bouveyron, C., Bataillon, G., Livartowski, A. (2021). Greedy clustering of count data through a mixture of multinomial PCA. Computational Statistics, 36, 1-33 [web].
Liang, D., Corneli, M., Latouche, P., Bouveyron, C. (2020). Missing rating imputation based on product reviews via deep latent variable models. In ICML 2020 (Artemiss) [web].
Corneli, M., Bouveyron, C., Latouche, P. (2020). Co-clustering of ordinal data via latent continuous random variables and not missing at random entries. Journal of Computational and Graphical Statistics, 29(4), 771-785 [web].
Ouadah, S., Robin, S., Latouche, P. (2020). Degree‐based goodness‐of‐fit tests for heterogeneous random graph models: Independent and exchangeable cases. Scandinavian Journal of Statistics, 47(1), 156-181 [web].
Bouveyron, C., Latouche, P., Mattei, P. A. (2020). Exact dimensionality selection for Bayesian PCA. Scandinavian Journal of Statistics, 47(1), 196-211 [web].
Bergé, L. R., Bouveyron, C., Corneli, M., Latouche, P. (2019). The latent topic block model for the co-clustering of textual interaction data. Computational Statistics & Data Analysis, 137, 247-270 [web].
Corneli, M. Bouveyron, C. Latouche, P., F. Rossi. (2019).The dynamic stochastic topic block model for time evolving networks with textual edges. Statistics and Computing [web].
Latouche, P., Bouveyron, C., Mattei, P-A. (2018). Bayesian variable selection for globally sparse probabilistic PCA. Electronic Journal of Statistics, 12.2 (2018), 3036-3070 [web].
Rastelli, R., Latouche, P., & Friel, N. (2018). Choosing the number of groups in a latent stochastic blockmodel for dynamic networks. Network Science, 6(4), 469-493 [web].
Latouche, P., Robin, S., Ouadah, S. (2018). Goodness of fit of logistic regression models for random graphs. Journal of Computational and Graphical Statistics, 27(1), 98-109 [web].
Corneli, M., Latouche, P., Rossi, F. (2018). Multiple change points detection and clustering in dynamic networks. Statistics and Computing, 28, 989-1007 [web].
Bouveyron, C., Latouche, P., Zreik, R. (2018). The stochastic topic block model for the clustering of vertices in networks with textual edges. Statistics and Computing, 28, 11-31 [web].
Latouche, P, Bouveyron, C., Marié, D., Fouetillou, G. (2017). Présidentielle 2017 : l’analyse des tweets renseigne sur les recompositions politiques. Statistique et Société 5.3 [web].
Wyse, J., Friel, N., Latouche, P. (2017). Inferring structure in bipartite networks using the latent blockmodel and exact ICL. Network Science, 5(1), 45-69 [web].
Zreik, R., Latouche, P., Bouveyron, C. (2017). The dynamic random subgraph model for the clustering of evolving networks. Computational Statistics, 32, 501-533 [web].
Latouche, P., Bouveyron, C., Mattei, P-A. (2016). Bayesian variable selection for globally sparse probabilistic PCA. AISTATS 2016.
Latouche, P., Robin, S. (2016). Variational Bayes model averaging for graphon functions and motif frequencies inference in W-graph models. Statistics and Computing, 26, 1173-1185 [web].
Latouche, P., Mattei, P. A., Bouveyron, C., Chiquet, J. (2016). Combining a relaxed EM algorithm with Occam’s razor for Bayesian variable selection in high-dimensional regression. Journal of Multivariate Analysis, 146, 177-190 [web].
Corneli, M., Latouche, P., Rossi, F. (2016). Exact ICL maximization in a non-stationary temporal extension of the stochastic block model for dynamic networks. Neurocomputing, 192, 81-91 [web].
Corneli, M., Latouche, P., Rossi, F. (2016). Block modelling in dynamic networks with non-homogeneous poisson processes and exact ICL. Social Network Analysis and Mining, 6, 1-14. [web].
Zreik, R., Latouche, P., Bouveyron, C. (2015). Classification automatique de réseaux dynamiques avec sous-graphes: étude du scandale Enron. Journal de la Société Française de Statistique, 156(3), 166-191 [web].
Côme, E., Latouche, P. (2015). Model selection and clustering in stochastic block models based on the exact integrated complete data likelihood. Statistical Modelling, 15(6), 564-589 [web].
Latouche, P., Birmelé, E. Ambroise, C. (2014). Model selection in overlapping stochastic block models. Electronic Journal of Statistics 8.1 (2014), 762-794 [web].
Jernite, Y., Latouche, P., Bouveyron, C. (2014). The random subgraph model for the analysis of an ecclesiastical network in Merovingian Gaul. Annals of Applied Statistics 8.1, 377-405 [web].
Latouche, P., Birmelé, E., Ambroise, C. (2012). Variational Bayesian inference and complexity control for stochastic block models. Statistical Modelling. 12.1, 93-115 [web].
Latouche, P., Birmelé, E., Ambroise, C. (2011). Overlapping stochastic block models with application to the French political blogosphere. Annals of Applied Statistics 5.1, 309-336 [web].
- 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 fr
.