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Luc Brogat-Motte I am a postdoctoral researcher under the supervision of Riccardo Bonalli (L2S, CentraleSupélec), and Alessandro Rudi (SIERRA team, INRIA Paris). I study the estimation of controlled stochastic differential equations. Previously, I completed my PhD at Télécom Paris, where I studied kernel methods and statistical learning theory under the supervision of Florence d'Alché-Buc (Télécom Paris) and Juho Rousu (Aalto University). You can reach me at: luc [dot] brogat [dot] motte [at] gmail [dot] com. |
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Publications and Preprints |
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Learning Controlled Stochastic Differential Equations
Luc Brogat-Motte, Riccardo Bonalli, Alessandro Rudi Preprint |
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Sketch In, Sketch Out: Accelerating both Learning and Inference for Structured Prediction with Kernels
Tamim El Ahmad, Luc Brogat-Motte, Pierre Laforgue, Florence d’Alché-Buc International Conference on Artificial Intelligence and Statistics (AISTATS), 2024 |
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Learning to Predict Graphs with Fused Gromov-Wasserstein Barycenters
Luc Brogat-Motte, Rémi Flamary, Céline Brouard, Juho Rousu, Florence d’Alché-Buc International Conference on Machine Learning (ICML), 2022 pdf / github 1 / github 2 |
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Vector-Valued Least-Squares Regression under Output Regularity Assumptions
Luc Brogat-Motte, Alessandro Rudi, Céline Brouard, Juho Rousu, Florence d'Alché-Buc Journal of Machine Learning Research (JMLR), 2022 |
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Duality in RKHSs with infinite dimensional outputs: Application to robust losses
Pierre Laforgue, Alex Lambert, Luc Brogat-Motte, Florence d’Alché-Buc International Conference on Machine Learning (ICML), 2020 |
PhD thesis |
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Structured Prediction with Output Regularization : Improving Statistical and Computational Efficiency
Luc Brogat-Motte 2023 manuscript |
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Thanks, Jon Barron, for the template. |