Christophe Ambroise
Publications
Publications
Epigenetic Variation in Tree Evolution: a case study in black poplar (Populus nigra)Plant Epigenetics (EPIPLANT/SEB 2024), Jul 2024, Clermont-Ferrand, France. , Abstract book of EPIPLANT/SEB 2024, Session VI Chromatin in transcription, replication and repair (Poster #69), pp.131-132 |
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PanGBank: depicting microbial species diversity via PPanGGOLiNJOBIM 2019 Journées Ouvertes Biologie, Informatique et Mathématiques, Jul 2019, Nantes, France |
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Adjacency-constrained hierarchical clustering of a band similarity matrix with application to genomicsStatistical Methods for Post-Genomic Data (SMPGD), Jan 2019, Barcelona, Spain. 2019 |
A greedy great approach to learn with complementary structured datasetsICML 2015 International Conference on Machine Learning, Jul 2015, Lille, France. 2015, Greed Is Great ICML Workshop, Jul 2015, Lille, France |
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Intégration tardive de données multimodales par modèles à blocs stochastiques55e Journées de Statistique de la SFdS, Vincent Couallier et Robin Genuer, May 2024, Bordeaux, France |
An interdisciplinary approach to increase wheat within-field diversity and promote agro-ecosystem services1. European Conference on Crop Diversification 2019, Sep 2019, Budapest, Hungary |
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PPanGGOLiN: Depicting microbial diversity via a Partitioned Pangenome GraphGenome Informatics 2018, Sep 2018, Hinxton, Cambridge, United Kingdom |
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PPanGGOLiN: Depicting microbial diversity via a Partitioned Pangenome GraphJOBIM 2018 Journées Ouvertes Biologie, Informatique et Mathématiques, Jul 2018, Marseille, France |
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Adjacency-constrained hierarchical clustering of a band similarity matrix with application to genomicsJournée Régionale de Bioinformatique et Biostatistique, Génopole Toulouse, Dec 2018, Toulouse, France |
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Quantify Genomic Heritability Through a Prediction Measure46th European Mathematical Genetics Meeting (EMGM), Apr 2018, Cagliari, Italy. pp.2, ⟨10.1159/000488519⟩ |
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A greedy great approach to learn with complementary structured datasetsGreed Is Great ICML Workshop, Jul 2015, Lille, France |
Significance testing for variable selection in high-dimensionConference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), Aug 2015, Niagara Falls, Canada. pp.1-8, ⟨10.1109/CIBCB.2015.7300313⟩ |
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Inférence jointe de la structure de modèles graphiques gaussiensCAp'2010, May 2010, France. pp.217-232 |
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Modèles de graphe aléatoire à classes chevauchantes pour l'analyse des réseaux42èmes Journées de Statistique, 2010, Marseille, France, France |
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Uncovering overlapping clusters in biological networksJournées Ouvertes en Biologie, Informatique et Mathématiques (JOBIM), Jun 2009, Nantes, France. pp.28 |
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A latent logistic model to uncover overlapping clusters in networksAtelier AGS (Apprentissage et Graphes pour les Systèmes complexes), May 2009, Hammamet, Tunisia. pp.3-8 |
Les modèles de mélange pour la classification de données massives en temps réel41èmes Journées de Statistique, SFdS, Bordeaux, 2009, Bordeaux, France, France |
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Pénalisation l1 pour les MAG2005, pp.25.1 |
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Model selection via penalization in the additive Cox modelThe 3rd world conference on Computational Statistics & Data Analysis, 2005, Limassol, Cyprus. pp.45 |
Mixture model approach for acoustic emission control of pressure equipment5th International Conference on Acoustical and Vibratory Surveillance Methods and Diagnostic Techniques, Oct 2004, Senlis, France. pp.1-10 |
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Discriminative Classification vs Modeling Methods in CBIRIEEE International Conference on Advanced Concepts for Intelligent Vision Systems, Sep 2004, Belgium. pp.1 |
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Généralisation du lasso aux modèles additifs2004, pp.83 |
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Penalized additive logistic regression for cardiovascular risk prediction2004, pp.301 |
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Regularization methods for additive models2003, pp.509-520 |
Semi-supervised marginboostNIPS 2001 - 14th International Conference on Neural Information, Dec 2001, Vancouver, BC, Canada. pp.553-560 |
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Boosting mixture models for semi-supervised learning taskICANN 2001 - International Conference on Artificial Neural Networks, Aug 2001, Vienne, Austria. pp.41-48, ⟨10.1007/3-540-44668-0_7⟩ |
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Epigenetic Variation in Tree Evolution: a case study in black poplar (Populus nigra)2023 |
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Linking Allele-Specific Expression And Natural Selection In Wild Populations2019 |
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Beyond Support in Two-Stage Variable Selection2015 |
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Méthodes d'Apprentissage pour la Recherche d'Images par le Contenu.2004 |
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Mixture of stochastic block models for multiview clusteringESANN 2023 - European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, pp.151-156, 2023, ⟨10.14428/esann/2023.ES2023-54⟩ |
Compression structurée de l’information génétique et étude d’association pangénomique par modèles additifsIntégration de données biologiques, ISTE Group, pp.129-163, 2022, ⟨10.51926/ISTE.9030.ch5⟩ |
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Applications in GenomicsFruhwirthSchnatter, S; Celeux, G; Robert, CP. HANDBOOK OF MIXTURE ANALYSIS, CRC PRESS-TAYLOR & FRANCIS GROUP, 2019, Chapman & Hall-CRC Handbooks of Modern Statistical Methods, 978-0-429-50824-0; 978-1-4987-6381-3 |
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Epigenetics in Forest Trees: State of the Art and Potential Implications for Breeding and Management in a Context of Climate ChangePlant Epigenetics Coming of Age for Breeding Applications, 88, Academic press, Elsevier, 454 p., 2018, Advances in Botanical Research, 9780128154038. ⟨10.1016/bs.abr.2018.09.003⟩ |
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Overlapping clustering methods for networksEdoardo M. Airoldi, David Blei, Elena A. Erosheva, Stephen E. Fienberg. Chapman and Hall/CRC. Handbook of Mixed Membership Models and Their Applications, Chapman and Hall/CRC, in press, 2014 |
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Bayesian methods for graph clusteringAndreas Fink, Berthold Lausen, Wilfried Seidel and Alfred Ultsch. Advances in Data Analysis, Data Handling and Business Intelligence, Springer, pp.229-239, 2009, Studies in Classification, Data Analysis, and Knowledge Organization, 978-3-642-01043-9. ⟨10.1007/978-3-642-01044-6⟩ |