SST/EPL Louvain School of Engineering (EPL)
SST/ICTM Institute of Information and Communication Technologies, Electronics and Applied Mathematics (ICTEAM)
SST/ICTM/INMA Pôle en ingénierie mathématique (INMA)
2025
Conference paper
Massion, B., & Massart, E. (2025). Grassmannian Frame Computation via Accelerated Alternating Projections. Published. Sampling Theory and Applications, Vienna. https://doi.org/10.1109/SampTA64769.2025.11133550
Report
Adel, T., Agarwal, A., Chrétien, S., Massart, E., Mokeev, D., Rungger, I., & Thompson, A. (2025). A Langevin sampler for quantum tomography.
2023
Conference paper
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.
2022
Conference paper
Massart, E., & Abrol, V. (2022). Coordinate Descent on the Orthogonal Group for Recurrent Neural Network Training. Published. 36th AAAI conference on artificial intelligence (online), 2022., online. https://doi.org/10.1609/aaai.v36i7.20742
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
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
Journal article
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)
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)
2021
Journal article
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)
2020
Journal article
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)
2019
Conference paper
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.
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
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
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.
Dissertation
Massart, E. (2019). Data fitting on positive-semidefinite matrix manifolds.
2018
Journal article
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)
2017
Conference paper
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
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.
Journal article
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)
2016
Conference paper
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.