Optimal Transport for structured data. - Titouan Vayer; Laetitia Chapel; Rémi Flamary; Romain Tavenard; Nicolas Courty (CoRR, 2018)

Fused Gromov-Wasserstein distance for structured objects: theoretical foundations and mathematical properties. - Titouan Vayer; Laetitia Chapel; Rémi Flamary; Romain Tavenard; Nicolas Courty (CoRR, 2018)

Optimal Transport for structured data with application on graphs. - Titouan Vayer; Nicolas Courty; Romain Tavenard; Laetitia Chapel; Rémi Flamary (ICML, 2019)

Sliced Gromov-Wasserstein. - Titouan Vayer; Rémi Flamary; Nicolas Courty; Romain Tavenard; Laetitia Chapel (NeurIPS, 2019)

CO-Optimal Transport. - Titouan Vayer; Ievgen Redko; Rémi Flamary; Nicolas Courty (NeurIPS, 2020)

Fused Gromov-Wasserstein Distance for Structured Objects. - Titouan Vayer; Laetitia Chapel; Rémi Flamary; Romain Tavenard; Nicolas Courty (Algorithms, 2020)

A contribution to Optimal Transport on incomparable spaces. - Titouan Vayer (CoRR, 2020)

A contribution to Optimal Transport on incomparable spaces. (Une contribution au Transport Optimal sur des espaces incomparables). - Titouan Vayer (2020)

Online Graph Dictionary Learning. - Cédric Vincent-Cuaz; Titouan Vayer; Rémi Flamary; Marco Corneli; Nicolas Courty (ICML, 2021)

Optimization of the Diffusion Time in Graph Diffused-Wasserstein Distances: Application to Domain Adaptation. - Amélie Barbe; Paulo Gonçalves 001; Marc Sebban; Pierre Borgnat; Rémi Gribonval; Titouan Vayer (ICTAI, 2021)

Subspace Detours Meet Gromov-Wasserstein. - Clément Bonet; Titouan Vayer; Nicolas Courty; François Septier; Lucas Drumetz (Algorithms, 2021)

Semi-relaxed Gromov Wasserstein divergence with applications on graphs. - Cédric Vincent-Cuaz; Rémi Flamary; Marco Corneli; Titouan Vayer; Nicolas Courty (CoRR, 2021)

POT: Python Optimal Transport. - Rémi Flamary; Nicolas Courty; Alexandre Gramfort; Mokhtar Z. Alaya; Aurélie Boisbunon; Stanislas Chambon; Laetitia Chapel; Adrien Corenflos; Kilian Fatras; Nemo Fournier; Léo Gautheron; Nathalie T. H. Gayraud; et al. (J. Mach. Learn. Res., 2021)

Fast Multiscale Diffusion On Graphs. - Sibylle Marcotte; Amélie Barbe; Rémi Gribonval; Titouan Vayer; Marc Sebban; Pierre Borgnat; Paulo Gonçalves 001 (ICASSP, 2022)

Semi-relaxed Gromov-Wasserstein divergence and applications on graphs. - Cédric Vincent-Cuaz; Rémi Flamary; Marco Corneli; Titouan Vayer; Nicolas Courty (ICLR, 2022)

Template based Graph Neural Network with Optimal Transport Distances. - Cédric Vincent-Cuaz; Rémi Flamary; Marco Corneli; Titouan Vayer; Nicolas Courty (NeurIPS, 2022)

Time Series Alignment with Global Invariances. - Titouan Vayer; Romain Tavenard; Laetitia Chapel; Rémi Flamary; Nicolas Courty; Yann Soullard (Trans. Mach. Learn. Res., 2022)

Entropic Wasserstein Component Analysis. - Antoine Collas; Titouan Vayer; Rémi Flamary; Arnaud Breloy (MLSP, 2023)

SNEkhorn: Dimension Reduction with Symmetric Entropic Affinities. - Hugues Van Assel; Titouan Vayer; Rémi Flamary; Nicolas Courty (NeurIPS, 2023)

Learning Graphical Factor Models with Riemannian Optimization. - Alexandre Hippert-Ferrer; Florent Bouchard; Ammar Mian; Titouan Vayer; Arnaud Breloy (ECML/PKDD (4), 2023)

Implicit Differentiation for Hyperparameter Tuning the Weighted Graphical Lasso. - Can Pouliquen; Paulo Gonçalves 001; Mathurin Massias; Titouan Vayer (CoRR, 2023)

Optimal Transport with Adaptive Regularisation. - Hugues Van Assel; Titouan Vayer; Rémi Flamary; Nicolas Courty (CoRR, 2023)

Interpolating between Clustering and Dimensionality Reduction with Gromov-Wasserstein. - Hugues Van Assel; Cédric Vincent-Cuaz; Titouan Vayer; Rémi Flamary; Nicolas Courty (CoRR, 2023)

Compressive Recovery of Sparse Precision Matrices. - Titouan Vayer; Etienne Lasalle; Rémi Gribonval; Paulo Gonçalves 001 (CoRR, 2023)

Controlling Wasserstein Distances by Kernel Norms with Application to Compressive Statistical Learning. - Titouan Vayer; Rémi Gribonval (J. Mach. Learn. Res., 2023)

Distributional Reduction: Unifying Dimensionality Reduction and Clustering with Gromov-Wasserstein Projection. - Hugues Van Assel; Cédric Vincent-Cuaz; Nicolas Courty; Rémi Flamary; Pascal Frossard; Titouan Vayer (CoRR, 2024)

PASCO (PArallel Structured COarsening): an overlay to speed up graph clustering algorithms. - Etienne Lasalle; Rémi Vaudaine; Titouan Vayer; Pierre Borgnat; Rémi Gribonval; Paulo Gonçalves 001; Márton Karsai (CoRR, 2024)

Schur's Positive-Definite Network: Deep Learning in the SPD cone with structure. - Can Pouliquen; Mathurin Massias; Titouan Vayer (ICLR, 2025)

Distributional Reduction: Unifying Dimensionality Reduction and Clustering with Gromov-Wasserstein. - Hugues Van Assel; Cédric Vincent-Cuaz; Nicolas Courty; Rémi Flamary; Pascal Frossard; Titouan Vayer (Trans. Mach. Learn. Res., 2025)

A note on the relations between mixture models, maximum-likelihood and entropic optimal transport. - Titouan Vayer; Etienne Lasalle (CoRR, 2025)

Bridging Arbitrary and Tree Metrics via Differentiable Gromov Hyperbolicity. - Pierre Houedry; Nicolas Courty; Florestan Martin-Baillon; Laetitia Chapel; Titouan Vayer (CoRR, 2025)