Pierre Latouche

682 total citations
26 papers, 277 citations indexed

About

Pierre Latouche is a scholar working on Artificial Intelligence, Statistical and Nonlinear Physics and Molecular Biology. According to data from OpenAlex, Pierre Latouche has authored 26 papers receiving a total of 277 indexed citations (citations by other indexed papers that have themselves been cited), including 19 papers in Artificial Intelligence, 11 papers in Statistical and Nonlinear Physics and 6 papers in Molecular Biology. Recurrent topics in Pierre Latouche's work include Bayesian Methods and Mixture Models (13 papers), Complex Network Analysis Techniques (11 papers) and Advanced Clustering Algorithms Research (6 papers). Pierre Latouche is often cited by papers focused on Bayesian Methods and Mixture Models (13 papers), Complex Network Analysis Techniques (11 papers) and Advanced Clustering Algorithms Research (6 papers). Pierre Latouche collaborates with scholars based in France, Luxembourg and United States. Pierre Latouche's co-authors include Étienne Côme, Étienne Birmelé, Christophe Ambroise, Charles Bouveyron, Fabrice Rossi, Laurent Bergé, Benjamin Guedj, Julien Chiquet, Stéphane Robin and Raphaël Cornette and has published in prestigious journals such as Neurocomputing, Machine Learning and Computational Statistics & Data Analysis.

In The Last Decade

Pierre Latouche

22 papers receiving 255 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Pierre Latouche France 10 158 147 44 36 27 26 277
Alejandro García del Amo Spain 9 185 1.2× 41 0.3× 20 0.5× 33 0.9× 12 0.4× 11 333
Étienne Birmelé France 7 78 0.5× 54 0.4× 32 0.7× 53 1.5× 8 0.3× 26 221
Cencheng Shen United States 8 36 0.2× 57 0.4× 29 0.7× 34 0.9× 36 1.3× 25 188
Bernhard Sch lkopf Germany 4 68 0.4× 147 1.0× 18 0.4× 15 0.4× 40 1.5× 4 234
Tai Qin United States 3 76 0.5× 46 0.3× 10 0.2× 15 0.4× 13 0.5× 3 107
Will Wei Sun United States 11 21 0.1× 95 0.6× 28 0.6× 30 0.8× 39 1.4× 23 292
Vincent Q. Vu United States 7 23 0.1× 67 0.5× 70 1.6× 27 0.8× 33 1.2× 13 268
Steven de Rooij Netherlands 9 13 0.1× 118 0.8× 25 0.6× 16 0.4× 11 0.4× 18 162
Yu. L. Pavlov Russia 10 78 0.5× 68 0.5× 40 0.9× 8 0.2× 8 0.3× 34 301
Ali Pinar United States 3 148 0.9× 83 0.6× 4 0.1× 13 0.4× 43 1.6× 4 205

Countries citing papers authored by Pierre Latouche

Since Specialization
Citations

This map shows the geographic impact of Pierre Latouche's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Pierre Latouche with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Pierre Latouche more than expected).

Fields of papers citing papers by Pierre Latouche

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Pierre Latouche. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Pierre Latouche. The network helps show where Pierre Latouche may publish in the future.

Co-authorship network of co-authors of Pierre Latouche

This figure shows the co-authorship network connecting the top 25 collaborators of Pierre Latouche. A scholar is included among the top collaborators of Pierre Latouche based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Pierre Latouche. Pierre Latouche is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Latouche, Pierre, et al.. (2024). Easily Computed Marginal Likelihoods from Posterior Simulation Using the THAMES Estimator. Bayesian Analysis. 20(3). 1 indexed citations
2.
Bouveyron, Charles, et al.. (2024). Clustering by deep latent position model with graph convolutional network. Advances in Data Analysis and Classification. 19(1). 237–270.
3.
Stoetzel, Emmanuelle, et al.. (2023). Bioclimatic inference based on mammal community using machine learning regression models: perspectives for paleoecological studies. Frontiers in Ecology and Evolution. 11. 4 indexed citations
4.
Bouveyron, Charles, et al.. (2023). The graph embedded topic model. Neurocomputing. 562. 126900–126900. 3 indexed citations
5.
Latouche, Pierre, et al.. (2022). MAGMA: inference and prediction using multi-task Gaussian processes with common mean. Machine Learning. 111(5). 1821–1849. 6 indexed citations
6.
Bouveyron, Charles, et al.. (2022). Deep latent position model for node clustering in graphs. HAL (Le Centre pour la Communication Scientifique Directe). 217–222.
7.
Bouveyron, Charles, et al.. (2020). A Bayesian Fisher-EM algorithm for discriminative Gaussian subspace\n clustering. arXiv (Cornell University). 1 indexed citations
8.
Bergé, Laurent, et al.. (2019). The latent topic block model for the co-clustering of textual interaction data. Computational Statistics & Data Analysis. 137. 247–270. 7 indexed citations
9.
Bouveyron, Charles, et al.. (2018). The dynamic stochastic topic block model for dynamic networks with textual edges. Statistics and Computing. 29(4). 677–695. 3 indexed citations
10.
Latouche, Pierre, et al.. (2017). Goodness of Fit of Logistic Regression Models for Random Graphs. Journal of Computational and Graphical Statistics. 27(1). 98–109. 6 indexed citations
11.
Latouche, Pierre, et al.. (2017). Multiple change points detection and clustering in dynamic networks. Statistics and Computing. 28(5). 989–1007. 12 indexed citations
12.
Bouveyron, Charles, et al.. (2016). Globally Sparse Probabilistic PCA. International Conference on Artificial Intelligence and Statistics. 976–984. 4 indexed citations
13.
Bouveyron, Charles, et al.. (2016). The stochastic topic block model for the clustering of vertices in networks with textual edges. Statistics and Computing. 28(1). 11–31. 15 indexed citations
14.
Latouche, Pierre, et al.. (2016). Exact ICL maximization in a non-stationary temporal extension of the stochastic block model for dynamic networks. Neurocomputing. 192. 81–91. 12 indexed citations
15.
Latouche, Pierre, et al.. (2016). The dynamic random subgraph model for the clustering of evolving networks. Computational Statistics. 32(2). 501–533. 11 indexed citations
16.
Latouche, Pierre, et al.. (2015). Combining a relaxed EM algorithm with Occam’s razor for Bayesian variable selection in high-dimensional regression. Journal of Multivariate Analysis. 146. 177–190. 7 indexed citations
17.
Latouche, Pierre, Étienne Birmelé, & Christophe Ambroise. (2014). Model selection in overlapping stochastic block models. Electronic Journal of Statistics. 8(1). 9 indexed citations
18.
Latouche, Pierre, Étienne Birmelé, & Christophe Ambroise. (2012). Variational Bayesian inference and complexity control for stochastic block models. Statistical Modelling. 12(1). 93–115. 73 indexed citations
19.
Latouche, Pierre, Étienne Birmelé, & Christophe Ambroise. (2009). Assessing a mixture model for graphs with a non asymptotic approximation of the marginal likelihood. arXiv (Cornell University).
20.
Latouche, Pierre, Étienne Birmelé, & Christophe Ambroise. (2008). Bayesian Methods for Graph Clustering.. 229–239. 3 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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