Florent Bouchard

Curriculum Vitae

Experience

Publications

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Supervision

PhD Students

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Principal Investigator

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Publications

My Google Scholar might be more up-to-date.

International ML conferences

1. Bouchard, F., Mian, A., Tiomoko, M., Ginolhac, G., & Pascal, F. (2024). Random matrix theory improved Fréchet mean of symmetric positive definite matrices. In International Conference on Machine Learning (ICML).
2. Hippert-Ferrer, A., Bouchard, F., Mian, A., Vayer, T., & Breloy, A. (2023). Learning graphical factor models with Riemannian optimization. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases (ECML-PKDD).
3. Tiomoko, M., Couillet, R., Bouchard, F., & Ginolhac, G. (2019). Random matrix improved covariance estimation for a large class of metrics. In International Conference on Machine Learning (ICML).

Journal papers

1. Mian, A., Ginolhac, G., Bouchard, F., & Breloy, A. (2024). Online change detection in SAR time-series with Kronecker product structured scaled Gaussian models. Signal Processing.
2. Collas, A., Bouchard, F., Breloy, A., Ginolhac, G., Ren, C., & Ovarlez, J. P. (2021). Probabilistic PCA from heteroscedastic signals: geometric framework and application to clustering. IEEE Transactions on Signal Processing (TSP).
3. Bouchard, F., Breloy, A., Ginolhac, G., Renaux, A., & Pascal, F. (2021). A Riemannian framework for low-rank structured elliptical models. IEEE Transactions on Signal Processing (TSP).
4. Bouchard, F., Mian, A., Zhou, J., Said, S., Ginolhac, G., & Berthoumieu, Y. (2020). Riemannian geometry for compound Gaussian distributions: Application to recursive change detection. Signal Processing.
5. Bouchard, F., Afsari, B., Malick, J., & Congedo, M. (2020). Approximate joint diagonalization with Riemannian optimization on the general linear group. SIAM Journal on Matrix Analysis and Applications (SIMAX).
6. Breloy, A., Ginolhac, G., Renaux, A., & Bouchard, F. (2018). Intrinsic Cramér–Rao bounds for scatter and shape matrices estimation in CES distributions. IEEE Signal Processing Letters (SPL).
7. Bouchard, F., Malick, J., & Congedo, M. (2018). Riemannian optimization and approximate joint diagonalization for blind source separation. IEEE Transactions on Signal Processing (TSP).

Book Chapters

1. Bouchard, F., Breloy, A., Collas, A., Renaux, A., & Ginolhac, G. (2024). The Fisher-Rao geometry of CES distributions. Springer. Elliptical Distributions in Signal Processing.

International SP conferences

1. Phi, T. H., Hippert-Ferrer, A., Bouchard, F., & Breloy, A. (2024). Robust low-rank correlation fitting. In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
2. Ayadi, I., Bouchard, F., & Pascal, F. (2023). t-WDA: A novel discriminant analysis applied to EEG classification. In European Signal Processing Conference (EUSIPCO).
3. Ayadi, I., Bouchard, F., & Pascal, F. (2023). Elliptical Wishart distribution: maximum likelihood estimator from information geometry. In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
4. Hippert-Ferrer, A., Mian, A., Bouchard, F., & Pascal, F. (2022). Riemannian classification of EEG signals with missing values. In European Signal Processing Conference (EUSIPCO).
5. Collas, A., Bouchard, F., Ginolhac, G., Breloy, A., Ren, C., & Ovarlez, J. P. (2022). On the use of geodesic triangles between Gaussian distributions for classification problems. In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
6. Bouchard, F., Breloy, A., Mian, A., & Ginolhac, G. (2021). On-line Kronecker product structured covariance estimation with Riemannian geometry for t-distributed data. In European Signal Processing Conference (EUSIPCO).
7. Collas, A., Bouchard, F., Breloy, A., Ren, C., Ginolhac, G., & Ovarlez, J. P. (2021). A Tyler-type estimator of location and scatter leveraging Riemannian optimization. In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
8. Bouchard, F., Breloy, A., Ginolhac, G., & Renaux, A. (2021). A Riemannian approach to blind separation of t-distributed sources. In European Signal Processing Conference (EUSIPCO).
9. Lefèvre, J., Bouchard, F., Said, S., Le Bihan, N., & Manton, J. H. (2021). On Riemannian and non-Riemannian Optimisation, and Optimisation Geometry. International Symposium on Mathematical Theory of Networks and Systems (MTNS).
10. Bouchard, F., Breloy, A., Ginolhac, G., & Pascal, F. (2020). Riemannian framework for robust covariance matrix estimation in spiked models. In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
11. Bouchard, F., Breloy, A., Renaux, A., & Ginolhac, G. (2020). Riemannian geometry and Cramér-Rao bound for blind separation of Gaussian sources. In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
12. Rodrigues, P. L. C., Bouchard, F., Congedo, M., & Jutten, C. (2017). Dimensionality Reduction for BCI classification using Riemannian geometry. In International Brain-Computer Interface Conference.
13. Congedo, M., Rodrigues, P. L. C., Bouchard, F., Barachant, A., & Jutten, C. (2017). A closed-form unsupervised geometry-aware dimensionality reduction method in the Riemannian Manifold of SPD matrices. In International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).
14. Bouchard, F., Malick, J., & Congedo, M. (2017). Approximate joint diagonalization according to the natural Riemannian distance. In International Conference of Latent Variable Analysis and Signal Separation (LVA/ICA).
15. Bouchard, F., Korczowski, L., Malick, J., & Congedo, M. (2016). Approximate joint diagonalization within the Riemannian geometry framework. In European Signal Processing Conference (EUSIPCO).
16. Korczowski, L., Bouchard, F., Jutten, C., & Congedo, M. (2016). Mining the bilinear structure of data with approximate joint diagonalization. In European Signal Processing Conference (EUSIPCO).

French national SP conferences

1. Hippert-Ferrer, A., Bouchard, F., Mian, A., Vayer, T., & Breloy, A. (2023). Optimisation Riemannienne pour l'apprentissage de graphes structurés. In Colloque Francophone de Traitement du Signal et des Images (GRETSI).
2. Laurent, N., Bouchard, F., Said, S., & Le Bihan, N. (2023). Estimation de barycentres sur variétés de Stiefel: une approche par projection. In Colloque Francophone de Traitement du Signal et des Images (GRETSI).
3. Ayadi, I., Bouchard, F., & Pascal, F. (2023). Distribution matricielle t-Wishart: géométrie d'information, estimation et application pour la classification de signaux EEG. In Colloque Francophone de Traitement du Signal et des Images (GRETSI).
4. Bouchard, F., Breloy, A., Renaux, A., & Ginolhac, G. (2019). Bornes de Cramér-Rao Intrinsèques pour l'estimation de la matrice de dispersion normalisée dans les distributions elliptiques. In Colloque Francophone de Traitement du Signal et des Images (GRETSI).
5. Rodrigues, P. L. C., Bouchard, F., Congedo, M., & Jutten, C. (2017). Géométrie Riemannienne appliquée à la réduction de la dimension de signaux EEG pour les interfaces cerveau-machine. In Colloque Francophone de Traitement du Signal et des Images (GRETSI).
6. Bouchard, F., Rodrigues, P. L. C., Malick, J., & Congedo, M. (2017). Réduction de dimension pour la Séparation Aveugle de Sources. In Colloque Francophone de Traitement du Signal et des Images (GRETSI).
7. Breloy, A., Renaux, A., Ginolhac, G., & Bouchard, F. (2017). Borne de Cramér-Rao intrinsèque pour la matrice de covariance des distributions elliptiques complexes. In Colloque Francophone de Traitement du Signal et des Images (GRETSI).

Contact

I am always open for collaborations on machine learning, statistical signal processing, Riemannian geometry and optimization.

florent.bouchard@cnrs.fr
florent.bouchard@centralesupelec.fr

CentraleSupélec, 3 rue Joliot Curie, 91190 Gif-sur-Yvette.

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Elements

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