Christophe Ambroise

Professeur des universités (UEVE)

tel : +33 1 64 85 35 25
fax :

Laboratoire Statistique et Génome
UMR CNRS 8071, USC INRA
23 boulevard de France
91037 Évry, France


Research Interest

My google citation page

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

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

Teaching

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

Links

Phd Students

Present

Past

  • Micheline Najjar,
  • Marta Avalos,
  • Allou Samé,
  • Nasser Charkaoui,
  • Karim Yousfi,
  • Hugo Zanghi,
  • Camille Charbonnier,
  • Matthieu Bouaziz

Publications

Restrict to author: Restrict to theme:

List of publications by ambroise ordered by year
to appear
Journal article
  • Model Selection in Overlapping Stochastic Block Models
    Latouche, P. and Birmele, E. and Ambroise, C.
    Electronic Journal of Statistics
Book chapter
  • Handbook on mixed membership model
    Latouche, P. and Birmele, E. and Ambroise, C. pp. 1--20
    Chapman and Hall
2013
In proceedings
  • Incorporating linkage disequilibrium blocks in Genome-Wide Association Studies
    Dehman, A. and Ambroise, C. and Neuvial, P.
    JOBIM proceeding 2013
2012
Preprint
Journal article
  • New consistent and asymptotically normal parameter estimates for random graph mixture models
    Ambroise, C. and Matias, C.
    Journal of the Royal Statistical Society: Series B Vol. 74 No. 1 pp. 3-35
    http://onlinelibrary.wiley.com/doi/10.1111/j.1467-9868.2011.01009.x/abstract
  • SHIPS: Spectral Hierarchical Clustering for the Inference of Population Structure in Genetic Studies
    Bouaziz, M. and Paccard, C. and Guedj, M. and Ambroise, C.
    PloS One Vol. 7 No. 10 pp. e45685
  • Variational Bayesian Inference and Complexity Control for Stochastic Block Models
    Latouche, P. and Birmelé, E. and Ambroise, C.
    Statistical Modelling Vol. 12 No. 1 pp. 93-115
    http://arxiv.org/abs/0912.2873v2
2011
Journal article
2010
Journal article
  • Clustering based on random graph model embedding vertex features
    Zanghi, H. and Volant, S. and Ambroise, C.
    Pattern Recognition Letters Vol. 31 No. 9 pp. 830-836
  • Strategies for Online Inference of Network Mixture
    Zanghi, H. and Picard, F. and Miele, V. and Ambroise, C.
    Annals of Applied Statistics Vol. 4 No. 2 pp. 687-714
    http://arxiv.org/abs/0910.2034
  • Weighted-Lasso for Structured Network Inference from Time Course Data
    Charbonnier, C. and Chiquet, J. and Ambroise, C.
    Statistical Applications in Genetics and Molecular Biology Vol. 9 No. 1
    http://www.bepress.com/sagmb/vol9/iss1/art15
In proceedings
  • Inférence jointe de la structure de modèles graphiques gaussiens
    Grandvalet, Y. and Chiquet, J. and Ambroise, C.
    actes de CAp'10, Clermont-Ferrand
  • Inferring Multiple Graphical Structures
    Chiquet, J. and Grandvalet, Y. and Ambroise, C.
    Workshop MODGRAPHII, JOBIM'10, Montpellier
  • Inferring Multiple Regulation Networks
    Grandvalet, Y. and Chiquet, J. and Ambroise, C.
    Proceedings of the MLCB NIPS'10 Workshop, Vancouver
  • Weighted-Lasso for Structured Network Inference for Time-Course data
    Charbonnier, C. and Chiquet, J. and Ambroise, C.
    JOBIM'10, Montpellier
2009
Journal article
In proceedings
  • SIMoNe : Statistical Inference of Modular Network
    Chiquet, J. and Charbonnier, C. and Ambroise, C.
    Workshop MODGRAPH, JOBIM'09, Nantes
  • Uncovering overlapping clusters in biological networks
    Latouche, P. and Birmelé, E. and Ambroise, C.
    Journées ouvertes en biologie, informatique et mathématiques (Jobim). Nantes
Book chapter
2008
Journal article
  • Fast Online Graph Clustering via Erdös Renyi Mixture
    Zanghi, H. and Ambroise, C. and Miele, V.
    Pattern Recognition Vol. 41 No. 12 pp. 3592--3599
  • Identification of functional modules based on transcriptional regulation structure
    Birmelé, E. and Elati, M. and Rouveirol, C. and Ambroise, C.
    BMC Proceedings Vol. 2 No. (Suppl 4):S4
2007
Journal article
2006
Journal article
  • A classification EM algorithm for binned data
    Same, A. and Ambroise, C. and Govaert, G.
    Computational Statistics and Data Analysis Vol. 51 No. 2 pp. 466-480
  • Feature Selection in Robust Clustering based on Laplace Mixture
    Cord, A. and Ambroise, C. and Cocquerez, J.
    Pattern Recognition Letters Vol. 27 No. 6 pp. 627--635
  • Selection bias in working with the top genes in supervised classification of tissue samples
    Zhu, X. and Ambroise, C. and McLachlan, G.J.
    Statistical Methodology Vol. 3 pp. 29-41
2005
Journal article
  • Discrimination par modèles additifs parcimonieux
    Avalos, M. and Grandvalet, Y. and Ambroise, C.
    Revue d'Intelligence Artificielle Vol. 19 pp. 661--682
In proceedings
  • Interpretable Clustering via Model-Based Divisive Hierarchical Classification
    Sujka, N. and Govaert, G. and Ambroise, C.
    29th Annual GFKL (Gesellschaft f\"ur Klassifikation)
Thesis
  • Modèles pour l'apprentissage statistique à partir de données complexes, mémoire d'habilitation à diriger des recherches
    Ambroise, C.
    hdr, Université de Technologie de Compiègne
Book chapter
  • Use of microarray data via model-based classification in the study and prediction of survival from lung cancer
    Jones, L. and Ng, S. and Ambroise, C. and Monico, K. and McLachlan, G. pp. 163--173
    Springer
2004
In proceedings
  • A mixture model approach for acoustic emission control of pressure equipment
    Hamdan, H. and Govaert, G. and Ambroise, C. and Hervé, C.
    5th International Conference on Acoustical and Vibratory Surveillance Methods and Diagnostic Techniques
  • Decision tree classifer for vehicle failure isolation
    Charkaoui, N. and Dubuisson, B. and Ambroise, C. and Millemann, S.
    Fifth International Conference on Data Mining, Text Mining and their Business Applications
  • Généralisation du lasso aux modèles additifs.
    Avalos, M. and Grandvalet, Y. and Ambroise, C.
    XXXVIèmes Journées de Statistique
  • Penalized additive logistic regression for cardiovascular risk prediction.
    Avalos, M. and Grandvalet, Y. and Ambroise, C.
    International Conference on Statistics in Health Sciences
Book
2003
Preprint
  • Techniques d'apprentissage pour l'indexation et la recherche d'images par le contenu
    Cord, M. and Ambroise, C.
In proceedings
  • A mixture model approach for binned data clustering
    Same, A. and Ambroise, C. and Govaert, G.
    Advances in Intelligent Data Analysis V, Lecture Notes in Computer Science (LNCS) Vol. 2810 pp. 265--274
  • Comments on Incremental Model Based Clustering for Large Data Sets with Small Clusters by Chris Fraley, Adrian Raftery, Ron Wehrens
    Ambroise, C.
    54th session of the International Statistical Institute
  • De l'importance du biais dans la s\'election de g\`enes discriminants pour la pr\'ediction de type de tumeurs
    Ambroise, C.
    Plateforme AFIA 2003
  • Regularization Methods for Additive Models
    Avalos, M. and Grandvalet, Y. and Ambroise, C.
    Advances in Intelligent Data Analysis V, Lecture Notes in Computer Science (LNCS) Vol. 2810 pp. 509--520
  • Selection bias in gene extraction in tumour classification
    McLachlan, G. and Ambroise, C.
    16th Australian Statistical Conference
Book chapter
  • Analyse de donn\'ees
    Ambroise, C. and Dang, M. pp. 100-121
    Herm\`es
2002
Journal article
  • Selection Bias in Gene Extraction in Tumour Classification on Basis of Microarray Gene Expression Data
    Ambroise, C. and McLachlan, G.J.
    PNAS Vol. 99 No. 10 pp. 6562-6566
In proceedings
  • A Mixture Model Approach to Datacube Clustering (Invited)
    Ambroise, C. and Govaert, G.
    26th Annual GFKL (Gesellschaft f\"ur Klassifikation)
  • Classification de donn\'ees discr\`etis\'ees
    Same, A. and Govaert, G. and Ambroise, C.
    34\`eme journ\'ees de statistiques
  • Semi-supervised marginboost
    d'Alché-Buc, F. and Grandvalet, Y. and Ambroise, C.
    Advances in Neural Information Processing Systems 14 pp. 553--560
2001
Journal article
  • Prediction of ozone peaks by mixture model
    Ambroise, C. and Grandvalet, Y.
    Ecological Modeling Vol. 245 pp. 275--289
In proceedings
  • A mixture model approach for classifying doubtful labeled data
    Ambroise, C. and Govaert, G.
    Mixtures 2001, Recent Developments on Mixture Modelling
  • Boosting Mixture Models for semi-supervised tasks
    Grandvalet, Y. and D'alché-Buc, F. and Ambroise, C.
    ICANN 2001 pp. 41--48
  • Clustering and models
    Ambroise, C. and Govaert, G.
    Classification, Automation and New Media. Proceedings of the 24th Annual Conference of the Gesellshaft für Klassification pp. 1--16
  • Int\'egration de donn\'ees qualitatives et quantitatives par les mod\`eles de m\'elange(Invited)
    Ambroise, C.
    Journ\'ee Didactique IS2 sur les M\'elanges de Lois de Probabilit\'es
  • Learning from an imprecise teacher: probabilistic and evidential approaches
    Ambroise, C. and Denoeux, T. and Govaert, G. and Smets, P.
    Proceeding of ASMDA 2001
  • Mod\`ele de m\'elange et cartes de Kohonen (Invited)
    Ambroise, C. and Govaert, G.
    S\'eminaire M\'ethodes Neuronales organis\'e par la Soci\'et\'e Francaise de Statistique
2000
In proceedings
  • Clustering by Maximizing a Fuzzy Classification Maximum Likelihood Criterion
    Ambroise, C. and Govaert, G.
    Compstat 2000, Prodeedings in Computational Statistics, 14th Symposium held in Utrecht, The Netherlands pp. 186--192
  • EM Algorithm for Partially Known Labels
    Ambroise, C. and Govaert, G.
    Data Analysis, Classification, and Related Methods, Proceedings of the 7th Conference of the International Federation of Classication Societies (IFCS-2000), University of Namur, Belgium pp. 161--166
  • Mixture Models and Clustering (Invited)
    Ambroise, C. and Govaert, G.
    24th Annual GFKL (Gesellschaft f\"ur Klassifikation)
  • Prediction of ozone peaks by mixture models
    Ambroise, C. and Grandvalet, Y.
    International Conference on Applications of Machine Learning to Ecological Modelling
1999
In proceedings
  • Classification spatiale utilisant des \'echantillons partiellement class\'es
    Ambroise, C. and Govaert, G.
    XXXI Journ\'ees de Statistique, R\'esum\'es pp. 407--410
  • Local learning by sparse radial basis functions
    Granvalet, Y. and Ambroise, C. and Canu, S.
    ICANN99 Vol. 1 pp. 233--238
1998
Journal article
  • Convergence Proof of an EM-type Algorithm for Spatial Clustering
    Ambroise, C. and Govaert, G.
    Pattern Recognition Letters Vol. 19 pp. 919--927
  • Hierarchical clustering of self organizing map for cloud classification
    Ambroise, C. and Sèze, G. and Badran, S. and Thiria, S.
    Neurocomputing Vol. 30 pp. 47--52
1997
Preprint
  • Introduction \`a la reconnaissance des formes
    Ambroise, C.
1996
Journal article
  • Constrained Clustering and Kohonen Self-Organizing Maps
    Ambroise, C. and Govaert, G.
    Journal of Classification Vol. 13 No. 2 pp. 299--313
In proceedings
  • Analyzing Dissimilarity Matrices using Kohonen Maps
    Ambroise, C. and Govaert, G.
    Proceeding of IFCS96 Vol. 1 pp. 425--430
Thesis
  • Approche probabiliste en classification automatique et contraintes de voisinage
    Ambroise, C., Universit\'e de Technologie de Compi\`egne
1995
In proceedings
  • Self-organization for Gaussian Parsimonious Clustering
    Ambroise, C. and Govaert, G.
    Proceeding of ICANN1995 Vol. 1 pp. 425--430
by Stat & Génome
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