Publications

McLachlan, G. J., Do, K. A., & Ambroise, C. (2004). Analyzing microarray gene expression data Wiley.

Chiquet, J., Grandvalet, Y., & Ambroise, C. (2009). Inferring Multiple Graphical Models Stat. and Genome Lab.

Latouche, P., Birmele, E., & Ambroise, C. (2009). Assessing a mixture model for graphs with a non asymptotic approximation of the marginal likelihood Stat. and Genome Lab.

Latouche, P., Birmele, E., & Ambroise, C. (2009). Overlapping Stochastic Block Models (No. arXiv:0910.2098v1). Stat. and Genome Lab.

Zanghi, H., Picard, F., Miele, V., & Ambroise, C. (2009). Strategies for Online Inference of Network Mixture Stat. and Genome Lab.

Charbonnier, C., Chiquet, J., & Ambroise, C. (to appear). Weighted-Lasso for Structured Network Inference from Time Course Data. Statistical Applications in Genetics and Molecular Biology.

Zanghi, H., Volant, S., & Ambroise, C. (to appear). Clustering based on Random Graph Model embedding Vertex Features. Pattern Recognition Letters.

Latouche, P., Birmele, E., & Ambroise, C. (2009). Advances in Data Analysis, Data Handling and Business Intelligence. In A. Fink, B. Lausen & W. Seidel & A. Ultsch (Eds.), (pp. 229-239). springer.

Ambroise, C., & Dang, M. (2009). Data Analysis. In G. Govaert (Ed.), (pp. 289-318). Wiley.

Christophe Ambroise Julien Chiquet, C. M. (2009). Inferring sparse Gaussian graphical models with latent structure. Electron. J. Statist., 3, 205-238.

Chiquet, J., Smith, A., Grasseau, G., Matias, C., & and Ambroise, C. (2008). SIMoNe: Statistical Inference for MOdular NEtworks. Bioinformatics.

Zanghi, H., Ambroise, C., & Miele, V. (2008). Fast Online Graph Clustering via Erdös Renyi Mixture. Pattern Recognition, 41(12), 3592-3599.

Birmele, E., E., M., Rouveirol, C., & Ambroise, C. (2008). Identification of functional modules based on transcriptional regulation structure.

Avalos, M., Grandvalet, Y., & Ambroise, C. (2007). Parsimonious additive models. CSDA, 51(6), 2851-2870.

Same, A., Ambroise, C., & Govaert, G. (2007). An online Classification EM algorithm based on the mixture model. Statistics and Computing, 17(3), 209-218.

Cord, A., Ambroise, C., & Cocquerez, J. (2006). Feature Selection in Robust Clustering based on Laplace Mixture. Pattern Recognition Letters.

Same, A., Ambroise, C., & Govaert, G. (2006). A classification EM algorithm for binned data. Computational Statistics and Data Analysis, 51(2), 466-480.

Zhu, X., Ambroise, C., & McLachlan, G. J. (2006). Selection bias in working with the top genes in supervised classification of tissue samples. Statistical Methodology, 3, 29-41.

Jones, L., Ng, S. K., Ambroise, C., , M. K., & McLachlan, G. J. (2005). Use of microarray data via model-based classification in the study and prediction of survival from lung cancer. In J. S. Shoemaker & S. M. Lin (Eds.), (pp. 163-173). New York: Springer.

Avalos, M., Grandvalet, Y., & Ambroise, C. (2005). Discrimination parmodèles additifs parcimonieux. Revue d'Intelligence Artificielle, 19, 661-682.

Ambroise, C., & Dang, M. (2003). Analyse de données. In G. Govaert (Ed.), (pp. 100-121). Hermès.

Ambroise, C., & McLachlan, G. J. (2002). Selection Bias in Gene Extraction in Tumour Classification on Basis of Microarray Gene Expression Data. PNAS, 99(10), 6562-6566.

Ambroise, C., & Grandvalet, Y. (2001). Prediction of ozone peaks by mixture model. Ecological Modeling, 245, 275-289.

Ambroise, C., & Govaert, G. (1998). Convergence Proof of an EM-type Algorithm for Spatial Clustering. Pattern Recognition Letters, 19, 919-927.

Ambroise, C., Sèze, G., Badran, S., & Thiria, S. (1998). Hierarchical clustering of self organizing map for cloud classification. Neurocomputing, 30, 47-52.

Ambroise, C., Dang, M., & Govaert, G. (1997). Clustering of spatial data by the EM algorithm. In A. Soares, J. Gomez-Hernandez & R. Froidevaux (Eds.), (pp. 493-504). Kluwer Academic Publisher.

Ambroise, C., & Govaert, G. (1996). Constrained Clustering and Kohonen Self-Organizing Maps. Journal of Classification, 13(2), 299-313.

Ambroise, C., & Govaert, G. (2006). Model based hierarchical co-clustering. Paper presented at the COMPSTAT 2006, Rome, Italie.

Yousfi, K., Ambroise, C., Cocquerez, J. P., & Chevelu, J. (2006). Driving hierarchy construction via supervised learning: Application to Osteo-Articular medical images database. Paper presented at the ICIP 06 : IEEE International Conference on Image Processing, Atlanta, USA.

Yousfi, K., Ambroise, C., Cocquerez, J. P., & Chevelu, J. (2006). Supervised learning for guiding hierarchy construction: Application to Osteo-Articular medical images database. Paper presented at the ICPR 06 : IEEE International Conference on Pattern Recognition, Hong Kong, China.

Charkaoui, N., Dubuisson, B., Ambroise, C., & Millemann, S. (2005). A classifer solution for several classes simultaneous occurrence : application to vehicle failure isolation. Paper presented at the Proceedings PRIP05 Eighth International Conference on Pattern Recognition and Information Processing.

Charkaoui, N., Dubuisson, B., Ambroise, C., & Boatas, A. (2005). Pattern recognition method for offboard automotive vehicle failure isolation. Paper presented at the In Proceedings of 16th IFAC World Congress.

Charkaoui, N., Dubuisson, B., Ambroise, C., & Millemann, S. (2004). Decision tree classifer for vehicle failure isolation. Paper presented at the Fifth International Conference on Data Mining, Text Mining and their Business Applications.

Hamdan, H., Govaert, G., Ambroise, C., & Hervé, C. (2004). A mixture model approach for acoustic emission control of pressure equipment. Paper presented at the 5th International Conference on Acoustical and Vibratory Surveillance Methods and Diagnostic Techniques.

Gosselin, P. -H., Najjar, M., Cord, M., & Ambroise, C. (2004). Discriminative Classification vs Modeling Methods in CBIR. Paper presented at the Proc. of Conf. on Advanced Concepts for Intelligent Vision Systems, ACIVS'2004.

Najjar, M., Ambroise, C., & Cocquerez, J. P. (2003). Feature Selection for Semi Supervised Learning Applied to Image Retrieval. Paper presented at the ICIP03, International Conference on Image Processing, Barcelena, Spain.

Najjar, M., Ambroise, C., & Cocquerez, J. P. (2003). Image Retrieval Using Mixture Models and EM Algorithm. Paper presented at the 13th Scandinavian Conference, SCIA 2003, Göteborg, Sweden.

Same, A., Ambroise, C., & Govaert, G. (2004). A mixture model approach for on-line clustering. Paper presented at the COMPSTAT 2004 Proceedings, Prague.

Avalos, M., Grandvalet, Y., & Ambroise, C. (2003). Regularization Methods for Additive Models. Paper presented at the Advances in Intelligent Data Analysis V, Lecture Notes in Computer Science (LNCS), Berlin, Germany.

Same, A., Ambroise, C., & Govaert, G. (2003). A mixture model approach for binned data clustering. Paper presented at the Advances in Intelligent Data Analysis V, Lecture Notes in Computer Science (LNCS), Berlin, Germany.

Jones, L., Ng, S. K., Ambroise, C., Monico, K., & Mclachlan, G. J. (2003). Use of microarray data via model-based classification in the study and prediction of survival from lung cancer (Accepted as a finalist paper). Paper presented at the Challenge at The fourth international conference for the Critical Assessment of Microarray Data Analysis.

Ambroise, C., & Govaert, G. (2002). A Mixture Model Approach to Datacube Clustering (Invited). Paper presented at the 26th Annual GFKL (Gesellschaft für Klassifikation).

d'Alché-Buc , F., Grandvalet, Y., & Ambroise, C. (2002). Semi-supervised marginboost. Paper presented at the Advances in Neural Information Processing Systems 14, Vancouver, Canada.

Same, A., Govaert, G., & Ambroise, C. (2002). Classification de données discrètisées. Paper presented at the 34ème journées de statistiques.

Ambroise, C. (2001). Intégration de données qualitatives et quantitatives par les modèles de mélange(Invited). Paper presented at the Journée Didactique IS2 sur les Mélanges de Lois de Probabilités.

Ambroise, C., Denoeux, T., Govaert, G., & Smets, P. (2001). Learning from an imprecise teacher: probabilistic and evidential approaches. Paper presented at the Proceeding of ASMDA 2001.

Ambroise, C., & Govaert, G. (2001). Clustering and models. Paper presented at the Classification, Automation and New Media. Proceedings of the 24th Annual Conference of the Gesellshaft für Klassification.

Grandvalet, Y., D'alché-Buc, F., & Ambroise, C. (2001). Boosting Mixture Models for semi-supervised tasks. Paper presented at the ICANN 2001, Vienne, Austria.

Ambroise, C., & Govaert, G. (2000). Clustering by Maximizing a Fuzzy Classification Maximum Likelihood Criterion. Paper presented at the Compstat 2000, Prodeedings in Computational Statistics, 14th Symposium held in Utrecht, The Netherlands.

Ambroise, C., & Govaert, G. (2000). EM Algorithm for Partially Known Labels. Paper presented at the Data Analysis, Classification, and Related Methods, Proceedings of the 7th Conference of the International Federation of Classication Societies (IFCS-2000), University of Namur, Belgium, Berlin.

Ambroise, C., & Govaert, G. (2000). Mixture Models and Clustering (Invited). Paper presented at the 24th Annual GFKL (Gesellschaft für Klassifikation).

Ambroise, C., & Govaert, G. (1999). Classification spatiale utilisant des échantillons partiellement classés. Paper presented at the XXXI Journées de Statistique, Résumés.

Granvalet, Y., Ambroise, C., & Canu, S. (1999). Local learning by sparse radial basis functions. Paper presented at the ICANN99, Edinburgh, Scottland.

Ambroise, C., & Govaert, G. (1996). Analyzing Dissimilarity Matrices using Kohonen Maps. Paper presented at the Proceeding of IFCS96.

Ambroise, C., & Govaert, G. (1995). Self-organization for Gaussian Parsimonious Clustering. Paper presented at the Proceeding of ICANN1995.