Clément Bonet

I am an associate professor at Ecole Polytechnique. My researchs lie at the interface of Optimal Transport, Optimization, Computational Statistics and Deep Learning.

Previously, I was a postdoctoral researcher at ENSAE/CREST, working with Anna Korba on Wasserstein Gradient Flows. I defended my PhD in November 2023 and I was supervised by François Septier, Nicolas Courty and Lucas Drumetz. Before that, I graduated from Telecom Paris and from the MVA ("Mathématiques, Vision, Apprentissage") of Ecole Normale Superieure Paris-Saclay. For more informations, you can find my resume here.

Publications

Bonet*, C., Vauthier*, C. & Korba, A. (2025). Flowing Datasets with Wasserstein over Wasserstein Gradient Flows. In Proceedings of International Conference on Machine Learning (ICML). PMLR.
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Bonet, C., Drumetz, L., & Courty, N. (2025). Sliced-Wasserstein Distances and Flows on Cartan-Hadamard Manifolds. Journal of Machine Learning Research (JMLR).
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Geuter, J., Bonet, C., Korba, A., & Alvarez-Melis, D. (2025). DDEQs: Distributional Deep Equilibrium Models through Wasserstein Gradient Flows. In The 28th International Conference on Artificial Intelligence and Statistics (AISTATS).
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Bonet*, C., Nadjahi*, K., Séjourné*, T., Fatras, K., & Courty, N. (2025). Slicing Unbalanced Optimal Transport. Transactions on Machine Learning Research (TMLR).
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Bonet, C., Uscidda, T., David, A., Aubin-Frankowski, P.C., & Korba, A. (2024). Mirror and Preconditioned Gradient Descent in Wasserstein Space. In Thirty-eight Conference on Neural Information Processing Systems (NeurIPS).
Abstract Bibtex Code Slides Poster Spotlight
Mahey, G., Chapel, L., Gasso, G., Bonet, C., & Courty, N. (2023). Fast Optimal Transport through Sliced Wasserstein Generalized Geodesics. In Thirty-seventh Conference on Neural Information Processing Systems (NeurIPS).
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Bonet*, C., Malézieux*, B., Rakotomamonjy, A., Drumetz, L., Moreau, T., Kowalski, M. & Courty, N. (2023). Sliced-Wasserstein on Symmetric Positive Definite Matrices for M/EEG Signals. In Proceedings of International Conference on Machine Learning (ICML). PMLR.
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Bonet, C., Chapel, L., Drumetz, L., & Courty, N. (2023). Hyperbolic Sliced-Wasserstein via Geodesic and Horospherical Projections. In Proceedings of 2nd Annual Workshop on Topology, Algebra, and Geometry in Machine Learning (TAG-ML). PMLR.
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Bonet, C., Berg, P., Courty, N., Septier, F., Drumetz, L., & Pham, M. T. (2023). Spherical Sliced-Wasserstein. International Conference on Learning Representations (ICLR).
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Bonet, C., Courty, N., Septier, F., & Drumetz, L. (2022). Efficient Gradient Flows in Sliced-Wasserstein Space. Transactions on Machine Learning Research (TMLR).
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Bonet, C., Vayer, T., Courty, N., Septier, F., & Drumetz, L. Subspace Detours Meet Gromov-Wasserstein. Algorithms 2021, 14, 366.
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PhD Thesis

Bonet, C. (2023). Leveraging Optimal Transport via Projections on Subspaces for Machine Learning Applications (Doctoral dissertation, Université de Bretagne Sud).
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Talks/Workshops

Divers

Teaching

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