Yger Florian - CV

LAMSADE

Yger Florian

Maître de conférences

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Biographie

Florian Yger est maître de conférences à l'Université Paris-Dauphine et enseigne l'analyse de données et l'apprentissage statistique au sein du département MIDO. Il est membre du laboratoire LAMSADE (Laboratoire d’Analyse et de Modélisation des Systèmes pour l’Aide à la Décision) et plus particulièrement de l'équipe MILES (Machine Intelligence LEarning Systems) qui se concentre sur l'intelligence artificielle responsable et explicable. 

Il a obtenu un doctorant en informatique de l'Université de Rouen en Juin 2013 sous la direction du Pr Alain Rakotomamonjy et du Dr Maxime Berar. Puis, il a ensuite été postdoctorant au LITIS sur des problèmes d'apprentissage multi-tâches du projet ANR LeMOn.
Ensuite, il a obtenu un financement postdoctoral JSPS pour rejoindre le groupe du Pr. Masashi Sugiyama à l'Université de Tokyo de Février 2014 à Septembre 2015.

Il a effectué des contributions à l'apprentissage de représentations (à travers des réprésentations induites par des projections, des noyaux ou des réseaux de neurones) et a appliqué ces travaux aux traitements de signaux (notamment pour des signaux EEG) et d'images (notamment pour la reconnaisannce de styles dans des peintures).
Plus récemment, il se concentre sur l'apprentissage contre-factuel.

Il est membre de Prairie (PaRis Artificial Intelligence Research InstitutE) dont il détient une chaire Tremplin.

Publications

Articles

Lotte F., Bougrain L., Cichocki A., Clerc M., Congedo M., Rakotomamonjy A., Yger F. (2018), A Review of Classification Algorithms for EEG-based Brain-Computer Interfaces: A 10-year Update, Journal of Neural Engineering, vol. 15, n°3

Pauty J., Usuba R., Cheng I., Hespel L., Takahashi H., Kato K., Kobayashi M., Nakajima H., Lee E., Yger F., Soncin F., Matsunaga Y. (2018), A Vascular Endothelial Growth Factor-Dependent Sprouting Angiogenesis Assay Based on an In Vitro Human Blood Vessel Model for the Study of Anti-Angiogenic Drugs, EBioMedicine, vol. 27, p. 225-236

Labernia F., Yger F., Mayag B., Atif J. (2018), Query-based learning of acyclic conditional preference networks from contradictory preferences, EURO Journal on Decision Processes, vol. 6, n°1-2, p. 39-59

Yger F., Berar M., Lotte F. (2017), Riemannian approaches in Brain-Computer Interfaces: a review, IEEE Transactions on Neural System and Rehabilitation Engineering, n°99

Horev I., Yger F., Sugiyama M. (2016), Geometry-aware principal component analysis for symmetric positive definite matrices, Machine Learning, p. 1-30

Spiecker genannt Döhmann I., Tambou O., Bernal P., Hu M., Molinaro C., Negre E., Sarlet I., Schertel Mendes L., Witzleb N., Yger F. (2016), The Regulation of Commercial Profiling – A Comparative Analysis, European Data Protection Law Review, vol. 2, n°4, p. 535-554

Balzi A., Yger F., Sugiyama M. (2015), Importance-weighted covariance estimation for robust common spatial pattern, Pattern Recognition Letters, vol. 68, p. 139-145

Chapitres d'ouvrage

Chevallier S., Kalunga E., Barthélemy Q., Yger F. (2018), Riemannian Classification for SSVEP-Based BCI: Offline versus Online Implementations, in Chang S. Nam, Anton Nijholt, Fabien Lotte, Brain-Computer Interfaces Handbook : Technological and Theoretical Advances, London: CRC Press, Taylor & Francis, p. 372-398

Communications avec actes

Zhang J., Petitjean C., Yger F., Ainouz S. (2020), Explainability for regression CNN in fetal head circumference estimation from ultrasound images, in Jaime Cardoso, Hien Van Nguyen, Nicholas Heller, Workshop on Interpretability of Machine Intelligence in Medical Image Computing at MICCAI 2020, Springer, 73-82 p.

Kumar S., Yger F., Lotte F. (2019), Towards Adaptive Classification using Riemannian Geometry approaches in Brain-Computer Interfaces, in Seong-Whan Lee, Klaus-Robert Müller, 2019 7th International Winter Conference on Brain-Computer Interface (BCI), Piscataway, NJ, IEEE - Institute of Electrical and Electronics Engineers

Yamane I., Yger F., Atif J., Sugiyama M. (2018), Uplift Modeling from Separate Labels, in S. Bengio; H. Wallach; H. Larochelle; K. Grauman; N. Cesa-Bianchi; R. Garnett, Advances in Neural Information Processing Systems 31 (NIPS 2018), Neural Information Processing Systems Foundation, Inc., 9927--9937 p.

Pinot R., Morvan A., Yger F., Gouy-Pailler C., Atif J. (2018), Graph-based Clustering under Differential Privacy, in Amir Globerson; Ricardo Silva, Uncertainty in Artificial Intelligence (UAI) - Proceedings of the Thirty-Fourth Conference (2018), August 6-10, 2018, Monterey, California, USA, AUAI Press, 329-338 p.

Labernia F., Zanuttini B., Mayag B., Yger F., Atif J. (2017), Online learning of acyclic conditional preference networks from noisy data, in George Karypis, Lucio Miele, Proceedings of the IEEE International Conference on Data Mining (ICDM 2017), Piscataway, NJ, IEEE - Institute of Electrical and Electronics Engineers

Vie J-J., Yger F., Lahfa R., Clement B., Cocchi K., Chalumeau T., Kashima H. (2017), Using Posters to Recommend Anime and Mangas in a Cold-Start Scenario, in Jean-Marc Ogier, Utpal Garain, Apostolos Antonacopoulos, 14th IAPR International Conference on Document Analysis and Recognition (ICDAR 2017), 2nd International Workshop on coMics ANalysis, Processing and Understanding (MANPU 2017), Kyoto, IEEE - Institute of Electrical and Electronics Engineers

Lecoutre A., Negrevergne B., Yger F. (2017), Recognizing Art Style Automatically in painting with deep learning, in Yung-Kyun Noh, Min-Ling Zhang, Proceedings of the 9th Asian Conference on Machine Learning (ACML 2017), IEEE - Institute of Electrical and Electronics Engineers, 327-342 p.

Horev I., Yger F., Sugiyama M. (2016), Geometry-aware stationary subspace analysis, in Robert J. Durrant, Kee-Eung Kim, Proceedings of The 8th Asian Conference on Machine Learning (ACML 2016), IEEE - Institute of Electrical and Electronics Engineers, 430-444 p.

Yamane I., Yger F., Berar M., Sugiyama M. (2016), Multitask Principal Component Analysis, in Robert J. Durrant, Kee-Eung Kim, Proceedings of The 8th Asian Conference on Machine Learning (ACML 2016), IEEE - Institute of Electrical and Electronics Engineers, 302-317 p.

Labernia F., Yger F., Mayag B., Atif J. (2016), Query-based learning of acyclic conditional preference networks from noisy data, in Róbert Busa-Fekete, Eyke Hüllermeier, Vincent Mousseau, Karlson Pfannschmidt, From Multiple Criteria Decision Aid to Preference Learning : Proceedings of the DA2PL'2016 EURO Mini Conference, Paderborn, Paderborn University, 6 p.

Yger F., Lotte F., Sugiyama M. (2015), Averaging covariance matrices for EEG signal classification based on the CSP: an empirical study, in , 23rd European Signal Processing Conference (EUSIPCO 2015), IEEE - Institute of Electrical and Electronics Engineers, 2721-2725 p.

Horev I., Yger F., Sugiyama M. (2015), Geometry-Aware Principal Component Analysis for Symmetric Positive Definite Matrices, in Geoffrey Holmes, Tie-Yan Liu, Proceedings of 7th Asian Conference on Machine Learning (ACML2015), IEEE - Institute of Electrical and Electronics Engineers, 1-16 p.

Communications sans actes

Beji C., Benhamou É., Bon M., Yger F., Atif J. (2020), Estimating Individual Treatment Effects throughCausal Populations Identification, 28th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2020), Brugges, Belgique

Riva M., Yger F., Gori P., Cesar R., Bloch I. (2020), Template-Based Graph Clustering, ECML-PKDD, Workshop on Graph Embedding and Minin (GEM), Ghent, Belgique

Yger F., Chevallier S., Barthélemy Q., Sra S. (2020), Geodesically-convex optimization for averaging partially observed covariance matrices, Proceedings of the Asian Conference on Machine Learning (ACML), Bangkok, ThaÏlande

Boria N., Negrevergne B., Yger F. (2020), Fréchet Mean Computation in Graph Space through Projected Block Gradient Descent, 28th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2020), Bruges, France

Pinot R., Yger F., Gouy-Pailler C., Atif J. (2019), A unified view on differential privacy and robustness to adversarial examples, Workshop on Machine Learning for CyberSecurity at ECMLPKDD 2019, Wurzburg, Allemagne

Pinot R., Meunier L., Araujo A., Kashima H., Yger F., Gouy-Pailler C., Atif J. (2019), Theoretical evidence for adversarial robustness through randomization, 33rd Conference on Neural Information Processing Systems (NIPS 2019), Vancouver, Canada

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