| Overlapping stochastic block models with application to the french political blogosphere P Latouche, E Birmelé, C Ambroise | 267 | 2011 |
| Variational Bayesian inference and complexity control for stochastic block models P Latouche, E Birmele, C Ambroise Statistical Modelling 12 (1), 93-115, 2012 | 220 | 2012 |
| Model selection and clustering in stochastic block models based on the exact integrated complete data likelihood E Côme, P Latouche Statistical Modelling 15 (6), 564-589, 2015 | 146 | 2015 |
| The stochastic topic block model for the clustering of vertices in networks with textual edges C Bouveyron, P Latouche, R Zreik Statistics and Computing 28 (1), 11-31, 2018 | 73 | 2018 |
| Inferring structure in bipartite networks using the latent blockmodel and exact ICL J Wyse, N Friel, P Latouche Network Science 5 (1), 45-69, 2017 | 53 | 2017 |
| Variational Bayes model averaging for graphon functions and motif frequencies inference in W-graph models P Latouche, S Robin Statistics and Computing 26 (6), 1173-1185, 2016 | 49 | 2016 |
| Multiple change points detection and clustering in dynamic networks M Corneli, P Latouche, F Rossi Statistics and Computing 28 (5), 989-1007, 2018 | 42 | 2018 |
| Exact ICL maximization in a non-stationary temporal extension of the stochastic block model for dynamic networks M Corneli, P Latouche, F Rossi Neurocomputing 192, 81-91, 2016 | 38 | 2016 |
| Graphs in machine learning: an introduction P Latouche, F Rossi European Symposium on Artificial Neural Networks, Computational Intelligence …, 2015 | 36 | 2015 |
| The random subgraph model for the analysis of an ecclesiastical network in Merovingian Gaul Y Jernite, P Latouche, C Bouveyron, P Rivera, L Jegou, S Lamassé | 35 | 2014 |
| Bayesian methods for graph clustering P Latouche, E Birmelé, C Ambroise Advances in Data Analysis, Data Handling and Business Intelligence …, 2009 | 35 | 2009 |
| Bayesian variable selection for globally sparse probabilistic PCA C Bouveyron, P Latouche, PA Mattei | 33 | 2018 |
| Exact dimensionality selection for Bayesian PCA C Bouveyron, P Latouche, PA Mattei Scandinavian Journal of Statistics 47 (1), 196-211, 2020 | 31 | 2020 |
| MAGMA: inference and prediction using multi-task Gaussian processes with common mean A Leroy, P Latouche, B Guedj, S Gey Machine Learning 111 (5), 1821-1849, 2022 | 29 | 2022 |
| Model selection in overlapping stochastic block models P Latouche, E Birmelé, C Ambroise | 28 | 2014 |
| The dynamic random subgraph model for the clustering of evolving networks R Zreik, P Latouche, C Bouveyron Computational Statistics 32 (2), 501-533, 2017 | 27 | 2017 |
| Hierarchical clustering with discrete latent variable models and the integrated classification likelihood E Côme, N Jouvin, P Latouche, C Bouveyron Advances in Data Analysis and Classification 15 (4), 957-986, 2021 | 25 | 2021 |
| Co-clustering of ordinal data via latent continuous random variables and not missing at random entries M Corneli, C Bouveyron, P Latouche Journal of Computational and Graphical Statistics 29 (4), 771-785, 2020 | 25 | 2020 |
| Choosing the number of groups in a latent stochastic blockmodel for dynamic networks R Rastelli, P Latouche, N Friel Network Science 6 (4), 469-493, 2018 | 24 | 2018 |
| Cluster-specific predictions with multi-task Gaussian processes A Leroy, P Latouche, B Guedj, S Gey Journal of Machine Learning Research 24 (5), 1-49, 2023 | 23 | 2023 |
Charles BouveyronProfessor of Statistics, Chair in AI at Institut 3IA Côte d'Azur, Inria, Université Côte d'AzurVerified email at inria.fr
