I am full Professor of Applied Mathematics. I have a dual affiliation between the of and the of (part time). Previously, I worked at the of (Université Paris Cité) as a Professor. I was also a visiting senior researcher at . Until 2018, I was Assistant Professor and then Associate Professor at . 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 and for the . I am an expert for the European research council. I co-invented and developed the and softwares. I teach statistics and machine learning at UCA and Ecole Polytechnique. I am also responsible along with (INRIA) of the course "" of the of , 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:
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List of former and current postdoc and engineers:
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A patent is available for this technology we develop
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 [].
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 [].
Leroy, A., Latouche, P., Guedj, B., Gey, S. (2023). Cluster-Specific Predictions with Multi-Task Gaussian Processes. Journal of Machine Learning Research, 24, [].
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 [].
Ouadah, S., Latouche, P., Robin, S. (2022). Motif-based tests for bipartite networks. Electronic Journal of Statistics, 16(1), 293-330 [].
Media
A. Mestre, "Eric Zemmour, nouveau président de la fachosphère ?". In: LeMonde (2022), p1. and p. 16-17 [].
S. Laurent, "Comment la gauche sociale-démocrate a perdu la bataille des réseaux sociaux". In: LeMonde (2022), p. 16-17 [].
S. Auffret, "Brigitte Macron et Jean-Michel Trogneux, itinéraire d’une infox délirante". In: LeMonde (2022), p. 16-17 [].
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 [].
M. Koppe, "Que vaut vraiment le poids politique sur Twitter". In: CNRS le journal, de la découverte à l’innovation (2022) [].
P. Latouche, C. Bouveyron, D. Marié, G. Fouetillou, "Présidentielle 2017 : une réorganisation politique du web social ?". In: TheConversation (2017) [].
P. Latouche, C. Bouveyron, D. Marié, G. Fouetillou, "Présidentielle 2017 : une réorganisation politique du web social ?". In: Data analytics post (2017) [].
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 [].
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 [].
Liang, D., Corneli, M., Bouveyron, C., Latouche, P. (2023). The graph embedded topic model. Hal preprint [].
Liang, D., Corneli, M., Bouveyron, C., Latouche, P. (2022). Clustering by deep latent position model with graph convolution network [].
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 [].
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 [].
Leroy, A., Latouche, P., Guedj, B., Gey, S. (2023). Cluster-Specific Predictions with Multi-Task Gaussian Processes. Journal of Machine Learning Research, 24 [].
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 [].
Ouadah, S., Latouche, P., Robin, S. (2022). Motif-based tests for bipartite networks. Electronic Journal of Statistics, 16(1), 293-330 [].
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 [].
Jouvin, N., Bouveyron, C., Latouche, P. (2021). A Bayesian Fisher-EM algorithm for discriminative Gaussian subspace clustering. Statistics and Computing, 31(4), 44 [].
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 [].
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 [].
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) [].
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 [].
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 [].
Bouveyron, C., Latouche, P., Mattei, P. A. (2020). Exact dimensionality selection for Bayesian PCA. Scandinavian Journal of Statistics, 47(1), 196-211 [].
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 [].
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 [].
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 [].
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 [].
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 [].
Corneli, M., Latouche, P., Rossi, F. (2018). Multiple change points detection and clustering in dynamic networks. Statistics and Computing, 28, 989-1007 [].
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 [].
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 [].
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 [].
Zreik, R., Latouche, P., Bouveyron, C. (2017). The dynamic random subgraph model for the clustering of evolving networks. Computational Statistics, 32, 501-533 [].
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 [].
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 [].
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 [].
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. [].
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 [].
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 [].
Latouche, P., Birmelé, E. Ambroise, C. (2014). Model selection in overlapping stochastic block models. Electronic Journal of Statistics 8.1 (2014), 762-794 [].
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 [].
Latouche, P., Birmelé, E., Ambroise, C. (2012). Variational Bayesian inference and complexity control for stochastic block models. Statistical Modelling. 12.1, 93-115 [].
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 [].
Pierre Latouche
pierre.latouche at uca dot fr
pierre.latouche at polytechnique dot fr
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