Ferdjaoui A, Affeldt S, and Nadif M. WordGraph: a python package for reconstructing interactive causal models from text data. In Proceedings of the 17th ACM International Conference on Web Search and Data Mining. (2024). (WSDM).

Ferdjaoui A, Affeldt S, and Nadif M. Modèles graphiques causaux interactifs pour les données textuelles. EGC '24: Extraction et Gestion des Connaissances: Actes EGC'2023. (2023). (EGC). (Best Demonstration Article Award)

Ferdjaoui A, Tlati A, Affeldt S, and Nadif M. CORPEX: Analyse exploratoire d'un corpus biomédicale à l'aide de la classification croisée. EGC '23: Extraction et Gestion des Connaissances: Actes EGC'2023. (2023). (EGC | pdf).

Falissard, L., Affeldt, S., Nadif, M. (2023). Attentive Perturbation: Extending Prefix Tuning to Large Language Models Inner Representations. In the Proceedings of the 9th International Conference on Machine Learning, Optimization, and Data Science. LOD 2023. (LOD)

Affeldt S, Labiod L, and Nadif M. CAEclust: A consensus of autoencoders representations for clustering.  Image Processing On Line, 12, 590-603. (2022). (IPOL | pdf | source)

Louis Geiler, Affeldt S, and Nadif M. An effective strategy for churn prediction and customer profiling. International Journal of Data Knowledge & Engineering, 142, 102100. (2022).  (DKE)

Louis Geiler, Affeldt S, and Nadif M. A survey on machine learning methods for churn prediction. International Journal of Data Science and Analytics, 1-26. (2022). (JDSA | HAL)

Louis Geiler, Affeldt S, and Nadif M. Apprentissage machine pour la prédiction de l'attrition: une étude comparative. EGC '22: Extraction et Gestion des Connaissances: Actes EGC'2022. (2022). (EGC | pdf)

Affeldt S, Labiod L, and Nadif M. Regularized Dual-PPMI Co-clustering for Text Data. SIGIR '21: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval. (2021) - (SIGIR | pdf)

Affeldt S, Labiod L, and Nadif M. Regularized bi-directional co-clustering. Statistics and Computing, 31(3), 1-17. (2021) - (STCO | pdf | ANR GePhEx)

Affeldt S, Labiod L, and Nadif M. Approche ensemble pour le co-clustering par blocs sur des données textuelles: Application au biomédical. Extraction et Gestion des Connaissances: Actes EGC'2021. (2021) - (EGC | pdf | video | ANR GePhEx)

Affeldt S, Labiod L, and Nadif M. Méthode ensemble de clustering profond. Extraction et Gestion des Connaissances: Actes EGC'2021. (2021) - (EGC | pdf)

Affeldt S, Labiod L, and Nadif M. Ensemble Block Co-clustering: a Unified Framework for Text Data. 29th ACM International Conference on Information and Knowledge Management, CIKM (2020) - (ACM | pdf | video | ANR GePhEx)

Affeldt S, Labiod L, and Nadif M. Spectral clustering via ensemble deep autoencoder learning (SC-EDAE). Pattern Recognition (2020) - (PR | pdf)

Dao M.C, Sokolovska N, Brazeilles R, Affeldt S, Pelloux V, Prifti E, Chilloux J, Verger E.O, Kayser B, Aron-Wisnewsky J, Ichou F, Pujos-Guillot E, Hoyles L, Juste C, Doré J, Dumas M.-E, Rizkalla S.W., Holmes B.A, Zucker J.-D, Clément K. A data integration multi-omics approach to study calorie restriction-induced changes in insulin sensitivity. Frontiers in Physiology (2018) - (Front. Physiol. | pdf)

Sella N, Verny L, Uguzzoni G, Affeldt S, Isambert H: MIIC online: a web server to reconstruct causal or non-causal networks from non-perturbative data. Bioinformatics (2018) - (OUP | pdf | MIIC online server).

Verny* L, Sella* N, Affeldt* S, Singh PP, Isambert H: Learning causal networks with latent variables from multivariate information in genomic data. PLoS Comput Biol 13(10):e1005662 (2017), (*co-first authors) - (PLOS | pdf | supp) [recommended by F1000]

Affeldt S*, Sokolovska N*, Prifti E, and Zucker J-D. Efficient Global Network Learning from Local Reconstructions. International Joint Conference on Neural Network IJCNN17 (2017), (*co-first authors) - (IEEE)

Affeldt S*, Sokolovska N*, Prifti E, Zucker J-D, Spectral consensus strategy for accurate reconstruction of large biological networks, BMC Bioinformatics (2016) 17(S-16):85-97, (*co-first authors) - (BMC)

Affeldt S, Verny L, Isambert H: 3off2: A network reconstruction algorithm based on 2-point and 3-point information statistics. BMC Bioinformatics, 17 Suppl 2:12 (2016) - (BMC | pdf | supp)

Affeldt S, Isambert H: Robust reconstruction of causal graphical models based on conditional 2-point and 3-point information. Proceedings of the 31th conference on Uncertainty in Artificial Intelligence, UAI (2015). - (AUAI | pdf | supp)

Singh PP, Affeldt S, Malaguti G, Isambert H: Human dominant disease genes are enriched in paralogs originating from whole genome duplication. PLoS Comput Biol, 10(7):e1003754 (2014) - (PLOS | pdf)

Affeldt S., Singh P.P., Malaguti G. & Isambert H., On the expansion of ‘dangerous’ gene families in vertebrates, BMC Bioinformatics 2014, 15(S-3) : A4 - (article) (short article)

Affeldt S. & Isambert H., Robust inference of structural independencies from finite data, actes du 4th Workshop on Algorithmic issues for Inference in Graphical Models 2014, AIGM14, AgroParisTech - (article) (short article)

Affeldt* S, Singh* PP, Cascone I, Selimoglu R, Camonis J, Isambert H: Evolution and cancer: expansion of dangerous gene repertoire by whole genome duplications. Med Sci, 29(4), 358-61 (2013) [french], (*co-premiers auteurs) - (EDPSciences | pdf)

Singh* PP, Affeldt* S, Cascone I, Selimoglu R, Camonis J, Isambert H: On the expansion of "dangerous" gene repertoires by whole genome duplications in early vertebrates. Cell Rep, 2(5), 1387-1398 (2012), (*co-premiers auteurs) - (CellPress | pdf | supp) [featured by Le Point, Biofutur, CNRS]

Singh P.P., Affeldt S. & Isambert H., Non-adaptive expansion of gene families, actes de la conférence JOBIM (Journées Ouvertes en Biologie, Informatique et Mathématiques) 2012, Université Rennes 1 (short article)

Approche ensemble pour le co-clustering par blocs sur des données textuelles: Application au biomédical. EGC, Extraction et Gestion des Connaissances (2021) - (video, 1:01:56)

Ensemble Block Co-clustering: a Unified Framework for Text Data. 29th ACM International Conference on Information and Knowledge Management, 2020, CIKM (video)

Deep unsupervised ensemble clustering. GdR ISIS - Apprentissage faiblement supervisé ou non supervisé pour l'analyse d'images et de vidéo 2019, CNAM, Paris

Learning causal vs. non-causal networks from non-perturbative data. XXVe rencontres de la société francophone de classification SFC 2018, Université Paris Descartes, Paris

Learning causal networks with latent variables from multivariate information in genomic data. Statistical Methods for Postgenomic Data SMPGD 2017, Imperial College, London

Spectral consensus strategy for accurate reconstruction of large biological networks. Tenth International Workshop on Machine Learning in Systems Biology MLSB 2016, The Hague, Netherlands

Network reconstruction and causal analysis on breast cancer data. Journées Jeunes Chercheurs en Cancérologie de la Fondation ARC 2015, Cite Internationale Universitaire de Paris

Robust reconstruction of causal graphical models from genomic data. Workshop on Statistical Systems Biology 2014, University of Warwick

Robust inference of structural independencies from finite data. 4th Workshop on Algorithmic issues for Inference in Graphical Models 2014, AgroParisTech

Causal network inference from local information-theoretic measures. Paris Biological Physics Community Day 2013, Irish Cultural Center

On the expansion of dangerous gene families in vertebrates. ISMB-Student Council Symposium 2013, ICC Berlin, (BMC Bioinformatics 2014, 15(Suppl3) : A4)

Evolutionary causes of the expansion of ‘dangerous’ gene families in vertebrate genome. Journees GTGC (Groupe de Travail Genomique Comparative) 2012, Université Lille 1

Whole-genome duplication in early vertebrates favored the non-adaptive expansion of ‘dangerous’ gene repertoires. Circle Meeting Biological Physics 2012, Institut Curie Paris

chipQC, a pipeline for batch effect adjustment and quality control of microarray data. Congrès de la Société Québécoise de lipidologie, de nutrition et de métabolisme, SQLNM 2016, Orford

3off2 : a network reconstruction method based on 2-point and 3-point information statistics. Journées Jeunes Chercheurs en Cancérologie de la Fondation ARC 2015, Cité Internationale Universitaire de Paris

Robust reconstruction of causal graphical models based on conditional 2-point and 3-point information. 31st Conference on Uncertainty in Artificial Intelligence 2015, Amsterdam

Robust reconstruction of causal graphical models based on conditional 2-point and 3-point information. Advances in causal inference - UAI2015 Workshop, Amsterdam

3off2 : a network reconstruction method based on 2-point and 3-point information statistics. Clore Center Workshop Biological Physics 2015, Weizmann Institute

Whole-Genome Duplication in Early Vertebrates - The non-adaptive expansion of ‘dangerous’ gene repertoires. QECG Workshop (Quantitative Evolutionary and Comparative Genomics) 2012, OIST (Okinawa Institute of Science and Technology)

The Causal Mediation Analysis in Genomic Data - Going Beyond Simple Correlations. JOBIM (Journées Ouvertes en Biologie, Informatique et Mathematiques) 2012, Université Rennes 1