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Christophe Ambroise

Professeur des universités
Université d'Évry Val d'Essonne
Laboratoire de Mathématiques et Modélisation d'Évry (UMR 8071)
I.B.G.B.I., 23 Bd. de France, 91037 Évry Cedex
Bureau 421
☎ +33 (0) 1 64 85 35 25
Firstname.Lastname@genopole.cnrs.fr
Google citation page
Curriculum Vitae

Research Interest

My research work is mainly concerned with supervised and unsupervised learning based on probabilistic models

  • Methods: mixture models, additive models, Gaussian Graphical Models, sparse regression
  • Considered problems: semi-supervised learning, clustering, network inference, variable selection
  • Applications: microarray analysis, regulation network inference, genetics

Publications

Google citation page

2017
[1]
Stanislas, V., Dalmasso, C. & Ambroise, C. Eigen-Epistasis for detecting Gene-Gene interactions. BMC Bioinformatics, 18:54, BioMed Central, 2017. implementation
[2]
Guinot, F., Szafranski, M., Ambroise, C. & Samson, F. Learning the optimal scale for GWAS through hierarchical SNP aggregation. , 2017., (working paper or preprint). implementation
2016
[3]
Becu, J.M., Grandvalet, Y., Ambroise, C. & Dalmasso, C. Beyond support in two-stage variable selection. Statistics and Computing, 26:1-11, Springer Verlag (Germany), 2016. implementation
2015
[4]
Dehman, A., Ambroise, C. & Neuvial, P. Performance of a blockwise approach in variable selection using linkage disequilibrium information. BMC Bioinformatics, pages 14, BioMed Central, 2015. implementation
[5]
Latouche, P., Birmele, E. & Ambroise, C. Handbook of Mixed Membership Models and Their Applications. , pages 547-568Chapman and Hall, 2015.
[6]
Ambroise, C., Chiquet, J. & Szafranski, M. A greedy great approach to learn with complementary structured datasets. In Greed Is Great ICML Workshop, 2015. implementation
[7]
Becu, J.M., Grandvalet, Y., Ambroise, C. & Dalmasso, C. Significance testing for variable selection in high-dimension. In Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), pages 1-8, IEEE, 2015. implementation
[8]
Becu, J.M., Grandvalet, Y., Ambroise, C. & Dalmasso, C. Beyond Support in Two-Stage Variable Selection. , 2015., (working paper or preprint). implementation
2014
[9]
Latouche, P., Birmele, E. & Ambroise, C. Model Selection in Overlapping Stochastic Block Models. Electronic Journal of Statistics, 8:762-794, 2014.
2013
[10]
Dehman, A., Ambroise, C. & Neuvial, P. Incorporating linkage disequilibrium blocks in Genome-Wide Association Studies. In JOBIM proceeding 2013, ():, , 2013.
2012
[11]
Ambroise, C. & Matias, C. New consistent and asymptotically normal parameter estimates for random graph mixture models. Journal of the Royal Statistical Society: Series B, 74(1):3-35, 2012. implementation
[12]
Bouaziz, M., Paccard, C., Guedj, M. & Ambroise, C. SHIPS: Spectral Hierarchical Clustering for the Inference of Population Structure in Genetic Studies. PloS One, 7(10):e45685, 2012.
[13]
Latouche, P., Birmele, E. & Ambroise, C. Variational Bayesian Inference and Complexity Control for Stochastic Block Models. Statistical Modelling, 12(1):93-115, 2012. implementation
[14]
Grandvalet, Y., Chiquet, J. & Ambroise, C. Sparsity by Worst-Case Quadratic Penalties. , 2012. implementation
2011
[15]
Bouaziz, M., Ambroise, C. & Guedj, M. Accounting for Population Stratification in Practice: a Comparison of the Main Strategies Dedicated to Genome-Wide Association Studies. PLOS one, 6(12):, 2011. implementation
[16]
Chiquet, J., Grandvalet, Y. & Ambroise, C. Inferring Multiple Graphical Structures. Statistics and Computing, 21(4):537-553, 2011. implementation
[17]
Jeanmougin, M., Guedj, M. & Ambroise, C. Defining a robust biological prior from Pathway Analysis to drive Network Inference.. J-SFdS, 152(2):, 2011. implementation
[18]
Latouche, P., Birmele, E. & Ambroise, C. Overlapping Stochastic Block Models with Application to the French Political Blogosphere. Annals of Applied Statistics, 5(1):309-336, 2011.
2010
[19]
Charbonnier, C., Chiquet, J. & Ambroise, C. Weighted-Lasso for Structured Network Inference from Time Course Data. Statistical Applications in Genetics and Molecular Biology, 9(1):, 2010. implementation
[20]
Zanghi, H., Picard, F., Miele, V. & Ambroise, C. Strategies for Online Inference of Network Mixture. Annals of Applied Statistics, 4(2):687-714, 2010. implementation
[21]
Zanghi, H., Volant, S. & Ambroise, C. Clustering based on random graph model embedding vertex features. Pattern Recognition Letters, 31(9):830-836, 2010.
[22]
Charbonnier, C., Chiquet, J. & Ambroise, C. Weighted-Lasso for Structured Network Inference for Time-Course data. In JOBIM'10, Montpellier, 2010.
[23]
Chiquet, J., Grandvalet, Y. & Ambroise, C. Inferring Multiple Graphical Structures. In Workshop MODGRAPHII, JOBIM'10, Montpellier, 2010.
[24]
Grandvalet, Y., Chiquet, J. & Ambroise, C. Inferring Multiple Regulation Networks. In Proceedings of the MLCB NIPS'10 Workshop, Vancouver, 2010.
[25]
Grandvalet, Y., Chiquet, J. & Ambroise, C. Inférence jointe de la structure de modèles graphiques gaussiens. In actes de CAp'10, Clermont-Ferrand, 2010.
2009
[26]
Ambroise, C., Chiquet, J. & Matias, C. Inferring sparse Gaussian graphical models with latent structure. Electronic Journal of Statistics, 3():205-238, 2009. implementation
[27]
Chiquet, J., Smith, A., Grasseau, G., Matias, C. & Ambroise, C. SIMoNe: Statistical Inference for MOdular NEtworks. Bioinformatics, 25(3):417-418, 2009. implementation
[28]
Ambroise, C. & Dang, M. Data Analysis. , ():289-318, Wiley, 2009.
[29]
Latouche, P., Birmele, E. & Ambroise, C. Advances in Data Analysis, Data Handling and Business Intelligence. , ():229-239, springer, 2009. implementation
[30]
Chiquet, J., Charbonnier, C. & Ambroise, C. SIMoNe : Statistical Inference of Modular Network. In Workshop MODGRAPH, JOBIM'09, Nantes, 2009.
[31]
Latouche, P., Birmele, E. & Ambroise, C. Uncovering overlapping clusters in biological networks. In Journées ouvertes en biologie, informatique et mathématiques (Jobim). Nantes, 2009.
2008
[32]
Birmele, E., Elati, M., Rouveirol, C. & Ambroise, C. Identification of functional modules based on transcriptional regulation structure. BMC Proceedings, 2((Suppl 4):S4):, 2008.
[33]
Zanghi, H., Ambroise, C. & Miele, V. Fast Online Graph Clustering via Erdös Renyi Mixture. Pattern Recognition, 41(12):3592-3599, 2008.
2007
[34]
Avalos, M., Grandvalet, Y. & Ambroise, C. Parsimonious additive models. CSDA, 51(6):2851-2870, 2007.
[35]
Same, A., Ambroise, C. & Govaert, G. An online Classification EM algorithm based on the mixture model. Statistics and Computing, 17(3):209-218, 2007.
2006
[36]
Cord, A., Ambroise, C. & Cocquerez, J. Feature Selection in Robust Clustering based on Laplace Mixture. Pattern Recognition Letters, 27(6):627-635, 2006.
[37]
Same, A., Ambroise, C. & Govaert, G. A classification EM algorithm for binned data. Computational Statistics and Data Analysis, 51(2):466-480, 2006.
[38]
Zhu, X., Ambroise, C. & McLachlan, G. Selection bias in working with the top genes in supervised classification of tissue samples. Statistical Methodology, 3():29-41, 2006.
2005
[39]
[40]
Avalos, M., Grandvalet, Y. & Ambroise, C. Discrimination par modèles additifs parcimonieux. Revue d'Intelligence Artificielle, 19():661-682, 2005.
[41]
Jones, L., Ng, S., Ambroise, C., Monico, K. & McLachlan, G. Use of microarray data via model-based classification in the study and prediction of survival from lung cancer. , ():163-173, Springer, 2005.
[42]
Sujka, N., Govaert, G. & Ambroise, C. Interpretable Clustering via Model-Based Divisive Hierarchical Classification. In 29th Annual GFKL (Gesellschaft für Klassifikation), 2005.
2004
[43]
, (eds). Analyzing microarray gene expression data. , (), Wiley, 2004. implementation
[44]
Avalos, M., Grandvalet, Y. & Ambroise, C. Généralisation du lasso aux modèles additifs.. In XXXVIèmes Journées de Statistique, 2004.
[45]
Avalos, M., Grandvalet, Y. & Ambroise, C. Penalized additive logistic regression for cardiovascular risk prediction.. In International Conference on Statistics in Health Sciences, 2004.
[46]
Charkaoui, N., Dubuisson, B., Ambroise, C. & Millemann, S. Decision tree classifer for vehicle failure isolation. In Fifth International Conference on Data Mining, Text Mining and their Business Applications, 2004.
[47]
Hamdan, H., Govaert, G., Ambroise, C. & Herve, C. A mixture model approach for acoustic emission control of pressure equipment. In 5th International Conference on Acoustical and Vibratory Surveillance Methods and Diagnostic Techniques, 2004.
2003
[48]
Ambroise, C. & Dang, M. Analyse de données. , ():100-121, Hermès, 2003.
[49]
[50]
[51]
Avalos, M., Grandvalet, Y. & Ambroise, C. Regularization Methods for Additive Models. In Advances in Intelligent Data Analysis V, Lecture Notes in Computer Science (LNCS), 2810:509-520, springer, 2003.
[52]
McLachlan, G. & Ambroise, C. Selection bias in gene extraction in tumour classification. In 16th Australian Statistical Conference, 2003.
[53]
Same, A., Ambroise, C. & Govaert, G. A mixture model approach for binned data clustering. In Advances in Intelligent Data Analysis V, Lecture Notes in Computer Science (LNCS), 2810:265-274, springer, 2003.
[54]
2002
[55]
Ambroise, C. & McLachlan, G. Selection Bias in Gene Extraction in Tumour Classification on Basis of Microarray Gene Expression Data. PNAS, 99(10):6562-6566, 2002.
[56]
Ambroise, C. & Govaert, G. A Mixture Model Approach to Datacube Clustering (Invited). In 26th Annual GFKL (Gesellschaft für Klassifikation), 2002.
[57]
Same, A., Govaert, G. & Ambroise, C. Classification de données discrètisées. In 34ème journées de statistiques, 2002.
2001
[58]
Ambroise, C. & Grandvalet, Y. Prediction of ozone peaks by mixture model. Ecological Modeling, 245():275-289, 2001.
[59]
Ambroise, C. Intégration de données qualitatives et quantitatives par les modèles de mélange(Invited). In Journée Didactique IS2 sur les Mélanges de Lois de Probabilités, 2001.
[60]
Ambroise, C., Denoeux, T., Govaert, G. & Smets, P. Learning from an imprecise teacher: probabilistic and evidential approaches. In Proceeding of ASMDA 2001, 2001.
[61]
Ambroise, C. & Govaert, G. Clustering and models. In Classification, Automation and New Media. Proceedings of the 24th Annual Conference of the Gesellshaft für Klassification, pages 1-16, springer, 2001.
[62]
Ambroise, C. & Govaert, G. Modèle de mélange et cartes de Kohonen (Invited). In Séminaire Méthodes Neuronales organisé par la Société Francaise de Statistique, 2001.
[63]
Ambroise, C. & Govaert, G. A mixture model approach for classifying doubtful labeled data. In Mixtures 2001, Recent Developments on Mixture Modelling, 2001.
[64]
Grandvalet, Y., D'alche-Buc, F. & Ambroise, C. Boosting Mixture Models for semi-supervised tasks. In ICANN 2001, pages 41-48, springer, 2001.
2000
[65]
Ambroise, C. & Govaert, G. Clustering by Maximizing a Fuzzy Classification Maximum Likelihood Criterion. In Compstat 2000, Prodeedings in Computational Statistics, 14th Symposium held in Utrecht, The Netherlands, pages 186-192, 2000.
[66]
Ambroise, C. & Govaert, G. EM Algorithm for Partially Known Labels. In Data Analysis, Classification, and Related Methods, Proceedings of the 7th Conference of the International Federation of Classication Societies (IFCS-2000), University of Namur, Belgium, pages 161-166, springer, 2000.
[67]
Ambroise, C. & Govaert, G. Mixture Models and Clustering (Invited). In 24th Annual GFKL (Gesellschaft für Klassifikation), 2000.
[68]
Ambroise, C. & Grandvalet, Y. Prediction of ozone peaks by mixture models. In International Conference on Applications of Machine Learning to Ecological Modelling, 2000.
1999
[69]
Ambroise, C. & Govaert, G. Classification spatiale utilisant des échantillons partiellement classés. In XXXI Journées de Statistique, Résumés, pages 407-410, 1999.
[70]
Granvalet, Y., Ambroise, C. & Canu, S. Local learning by sparse radial basis functions. In ICANN99, pages 233-238, IEE, 1999.
1998
[71]
Ambroise, C. & Govaert, G. Convergence Proof of an EM-type Algorithm for Spatial Clustering. Pattern Recognition Letters, 19():919-927, 1998.
[72]
Ambroise, C., Seze, G., Badran, S. & Thiria, S. Hierarchical clustering of self organizing map for cloud classification. Neurocomputing, 30():47-52, 1998.
1997
[73]
1996
[74]
Ambroise, C. & Govaert, G. Constrained Clustering and Kohonen Self-Organizing Maps. Journal of Classification, 13(2):299-313, 1996.
[75]
Ambroise, C. & Govaert, G. Analyzing Dissimilarity Matrices using Kohonen Maps. In Proceeding of IFCS96, 1:425-430, 1996.
[76]
1995
[77]
Ambroise, C. & Govaert, G. Self-organization for Gaussian Parsimonious Clustering. In Proceeding of ICANN1995, 1:425-430, 1995.

Formation

  • We do organize formation through cnrs formation entreprise. Two modules are availables:
    • Bases statistiques et tests d'hypothèses avec R
    • Outils de statistique non paramétrique avec R

Phd Students

Present

  • Florent Guinot en co-encadrement avec Marie Szafranski
  • Virginie Stanislas en co-encadrement avec Cyril Dalmasso
  • Arthur Frouin en co-encadrement avec Edith LeFloch et Jean-François Deleuze

Past

members/cambroise/welcome.txt · Last modified: 2017/10/09 09:29 by Christophe Ambroise

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