On the Dualization of Operator-Valued Kernel Machines. - Pierre Laforgue; Alex Lambert; Luc Motte; Florence d'Alché-Buc (CoRR, 2019)

Duality in RKHSs with Infinite Dimensional Outputs: Application to Robust Losses. - Pierre Laforgue; Alex Lambert; Luc Brogat-Motte; Florence d'Alché-Buc (ICML, 2020)

Learning Output Embeddings in Structured Prediction. - Luc Brogat-Motte; Alessandro Rudi; Céline Brouard; Juho Rousu; Florence d'Alché-Buc (CoRR, 2020)

Learning to Predict Graphs with Fused Gromov-Wasserstein Barycenters. - Luc Brogat-Motte; Rémi Flamary; Céline Brouard; Juho Rousu; Florence d'Alché-Buc (ICML, 2022)

Vector-Valued Least-Squares Regression under Output Regularity Assumptions. - Luc Brogat-Motte; Alessandro Rudi; Céline Brouard; Juho Rousu; Florence d'Alché-Buc (J. Mach. Learn. Res., 2022)

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 (AISTATS, 2024)

Learning Controlled Stochastic Differential Equations. - Luc Brogat-Motte; Riccardo Bonalli; Alessandro Rudi (CoRR, 2024)

Safely Learning Controlled Stochastic Dynamics. - Luc Brogat-Motte; Alessandro Rudi; Riccardo Bonalli (CoRR, 2025)