Article de journal
Taminiau, T., Nunes Grapiglia, G., & Massart, E. (2026). Enhancing finite-difference-based derivative-free optimization with machine learning. Optimization Letters, 20(6), 1159-1177. https://doi.org/10.1007/s11590-026-02281-1 (Original work published 2026)
Oswald, A., & Massart, E. (2026). Expressivity of congruence-based architectures for DNNs on positive-definite matrices. Submitted. (Original work published 2026)
Cartis, C., Liang, X., Massart, E., & Otemissov, A. (2026). Learning the subspace of variation for global optimization of functions with low effective dimension. SIAM Journal on Optimization. Submitted. (Original work published 2026)
Papier de conférence
Massion, B., Makhlouf, R., & Massart, E. (2026, May 29). The role of class encoding in neural collapse. EUSIPCO 2026, Bruges, Belgium.
Tansley, E., Massart, E., & Cartis, C. (2026, May 2). On the Neural Feature Ansatz for Deep Neural Networks. AISTATS 2026, Tangier, Marocco.
Rapport
Adel, T., Agarwal, A., Chrétien, S., Massart, E., Mokeev, D., Rungger, I., & Thompson, A. (2025). A Langevin sampler for quantum tomography.
Papier de conférence
Massion, B., & Massart, E. (2025). Grassmannian Frame Computation via Accelerated Alternating Projections. Proceedings of the International Conference on Sampling Theory and Applications. Published. Sampling Theory and Applications, Vienna. https://doi.org/10.1109/SampTA64769.2025.11133550 (Original work published 2025)
Papier de conférence
Massart, E., & Abrol, V. (2023). Coordinate descent on the Stiefel manifold for deep neural network training. ESANN 2023 proceedings. Published. 31st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning., Bruges, Belgium.
Papier de conférence
Massart, E., & Abrol, V. (2022). Coordinate Descent on the Orthogonal Group for Recurrent Neural Network Training. Proceedings of the AAAI Conference on Artificial Intelligence, 36(7), 7744-7751. https://doi.org/10.1609/aaai.v36i7.20742 (Original work published 2022)
Massart, E. (2022). Improving weight clipping in Wasserstein GANs. Proceedings of the IEEE International Conference on Pattern Recognition (ICPR). Published. 2022 26th International Conference on Pattern Recognition (ICPR), Montreal, QC, Canada. https://doi.org/10.1109/icpr56361.2022.9956056
Massart, E. (2022). Orthogonal regularizers in deep learning: how to handle rectangular matrices? Proceedings of the IEEE International Conference on Pattern Recognition (ICPR). Published. 2022 26th International Conference on Pattern Recognition (ICPR), Montreal, QC, Canada. https://doi.org/10.1109/icpr56361.2022.9956205
Article de journal
Cartis, C., Massart, E., & Otemissov, A. (2022). Bound-constrained global optimization of functions with low effective dimensionality using multiple random embeddings. Mathematical Programming, 198(1), 997-1058. https://doi.org/10.1007/s10107-022-01812-9 (Original work published 2022)
Cartis, C., Massart, E., & Otemissov, A. (2022). Global optimization using random embeddings. Mathematical Programming, 200(2), 781-829. https://doi.org/10.1007/s10107-022-01871-y (Original work published 2022)
Article de journal
Musolas, A., Massart, E., Hendrickx, J., Absil, P.-A., & Marzouk, Y. (2021). Low-rank multi-parametric covariance identification. Bit (Lisse) : numerical mathematics, 62, 221-249. https://doi.org/10.1007/s10543-021-00867-y (Original work published 2021)
Article de journal
Massart, E., & Absil, P.-A. (2020). Quotient Geometry with Simple Geodesics for the Manifold of Fixed-Rank Positive-Semidefinite Matrices. SIAM Journal on Matrix Analysis and Applications, 41(1), 171-198. https://doi.org/10.1137/18m1231389 (Original work published 2020)
Papier de conférence
Nguyen, T. S., Gousenbourger, P.-Y., Massart, E., & Absil, P.-A. (2019). Online balanced truncation for linear time-varying systems using continuously differentiable interpolation on Grassmann manifold. 2019 6th International Conference on Control, Decision and Information Technologies (CoDIT), p. 165-170. https://doi.org/10.1109/codit.2019.8820675
Massart, E., Hendrickx, J., & Absil, P.-A. (2019). Curvature of the Manifold of Fixed-Rank Positive-Semidefinite Matrices Endowed with the Bures–Wasserstein Metric. Lecture Notes in Computer Science : Geometric Science of Information, p. 739-748. https://doi.org/10.1007/978-3-030-26980-7_77
Massart, E., Gousenbourger, P.-Y., Nguyen, T. S., Stykel, T., & Absil, P.-A. (2019). Interpolation on the manifold of fixed-rank positive-semidefinite matrices for parametric model order reduction: preliminary results. ESANN 2019 Proceedings, p. 281-286.
Szczapa, B., Daoudi, M., Berretti, S., Del Bimbo, A., Pala, P., & Massart, E. (2019). Fitting, Comparison, and Alignment of Trajectories on Positive Semi-Definite Matrices with Application to Action Recognition. Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV). Published. ICCV Human Behavior Understanding workshop, 2019, Seoul, Korea.
Thèse
Massart, E. (2019). Data fitting on positive-semidefinite matrix manifolds.
Article de journal
Gousenbourger, P.-Y., Massart, E., & Absil, P.-A. (2018). Data Fitting on Manifolds with Composite Bézier-Like Curves and Blended Cubic Splines. Journal of Mathematical Imaging and Vision, 61(5), 645-671. https://doi.org/10.1007/s10851-018-0865-2 (Original work published 2018)
Article de journal
Massart, E., Hendrickx, J., & Absil, P.-A. (2017). Matrix geometric means based on shuffled inductive sequences. Linear Algebra and Its Applications, 252, 334-359. https://doi.org/10.1016/j.laa.2017.05.036 (Original work published 2018)
Papier de conférence
Gousenbourger, P.-Y., Massart, E., Musolas, A., Absil, P.-A., Hendrickx, J., Jacques, L., & Marzouk, Y. (2017). Piecewise-Bezier C1 smoothing on manifolds with application to wind field estimation. 25th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2017), 305-3010.
Massart, E., & Chevallier, S. (2017). Inductive Means and Sequences Applied to Online Classification of EEG. Lecture Notes in Computer Science : Geometric Science of Information, p. 763-770. https://doi.org/10.1007/978-3-319-68445-1_88
Papier de conférence
Massart, E., Hendrickx, J., & Absil, P.-A. (2016). Extending a two-variable mean to a multi-variable mean. ESANN 2016, Bruges, Belgium, 26-28 April 2016.
