Titouan Vayer

Publication Activity (10 Years)

Years Active: 2018-2024
Publications (10 Years): 36

Top Topics

Dimensionality Reduction

Dictionary Learning

Structured Objects

Top Venues

J. Mach. Learn. Res.

Publications

Hugues Van Assel, Cédric Vincent-Cuaz, Nicolas Courty, Rémi Flamary, Pascal Frossard, Titouan Vayer
Distributional Reduction: Unifying Dimensionality Reduction and Clustering with Gromov-Wasserstein Projection. CoRR (2024)
Can Pouliquen, Mathurin Massias, Titouan Vayer
Schur's Positive-Definite Network: Deep Learning in the SPD cone with structure. CoRR (2024)
Hugues Van Assel, Titouan Vayer, Rémi Flamary, Nicolas Courty
Optimal Transport with Adaptive Regularisation. CoRR (2023)
Antoine Collas, Titouan Vayer, Rémi Flamary, Arnaud Breloy
Entropic Wasserstein Component Analysis. MLSP (2023)
Can Pouliquen, Paulo Gonçalves, Mathurin Massias, Titouan Vayer
Implicit Differentiation for Hyperparameter Tuning the Weighted Graphical Lasso. CoRR (2023)
Alexandre Hippert-Ferrer, Florent Bouchard, Ammar Mian, Titouan Vayer, Arnaud Breloy
Learning Graphical Factor Models with Riemannian Optimization. ECML/PKDD (4) (2023)
Antoine Collas, Titouan Vayer, Rémi Flamary, Arnaud Breloy
Entropic Wasserstein Component Analysis. CoRR (2023)
Hugues Van Assel, Titouan Vayer, Rémi Flamary, Nicolas Courty
SNEkhorn: Dimension Reduction with Symmetric Entropic Affinities. NeurIPS (2023)
Titouan Vayer, Etienne Lasalle, Rémi Gribonval, Paulo Gonçalves
Compressive Recovery of Sparse Precision Matrices. CoRR (2023)
Hugues Van Assel, Cédric Vincent-Cuaz, Titouan Vayer, Rémi Flamary, Nicolas Courty
Interpolating between Clustering and Dimensionality Reduction with Gromov-Wasserstein. CoRR (2023)
Hugues Van Assel, Titouan Vayer, Rémi Flamary, Nicolas Courty
SNEkhorn: Dimension Reduction with Symmetric Entropic Affinities. CoRR (2023)
Titouan Vayer, Rémi Gribonval
Controlling Wasserstein Distances by Kernel Norms with Application to Compressive Statistical Learning. J. Mach. Learn. Res. 24 (2023)
Cédric Vincent-Cuaz, Rémi Flamary, Marco Corneli, Titouan Vayer, Nicolas Courty
Semi-relaxed Gromov-Wasserstein divergence and applications on graphs. ICLR (2022)
Sibylle Marcotte, Amélie Barbe, Rémi Gribonval, Titouan Vayer, Marc Sebban, Pierre Borgnat, Paulo Gonçalves
Fast Multiscale Diffusion On Graphs. ICASSP (2022)
Cédric Vincent-Cuaz, Rémi Flamary, Marco Corneli, Titouan Vayer, Nicolas Courty
Template based Graph Neural Network with Optimal Transport Distances. NeurIPS (2022)
Alexandre Hippert-Ferrer, Florent Bouchard, Ammar Mian, Titouan Vayer, Arnaud Breloy
Learning Graphical Factor Models with Riemannian Optimization. CoRR (2022)
Titouan Vayer, Romain Tavenard, Laetitia Chapel, Rémi Flamary, Nicolas Courty, Yann Soullard
Time Series Alignment with Global Invariances. Trans. Mach. Learn. Res. 2022 (2022)
Cédric Vincent-Cuaz, Rémi Flamary, Marco Corneli, Titouan Vayer, Nicolas Courty
Template based Graph Neural Network with Optimal Transport Distances. CoRR (2022)
Clément Bonet, Titouan Vayer, Nicolas Courty, François Septier, Lucas Drumetz
Subspace Detours Meet Gromov-Wasserstein. Algorithms 14 (12) (2021)
Amélie Barbe, Paulo Gonçalves, Marc Sebban, Pierre Borgnat, Rémi Gribonval, Titouan Vayer
Optimization of the Diffusion Time in Graph Diffused-Wasserstein Distances: Application to Domain Adaptation. ICTAI (2021)
Cédric Vincent-Cuaz, Rémi Flamary, Marco Corneli, Titouan Vayer, Nicolas Courty
Semi-relaxed Gromov Wasserstein divergence with applications on graphs. CoRR (2021)
Sibylle Marcotte, Amélie Barbe, Rémi Gribonval, Titouan Vayer, Marc Sebban, Pierre Borgnat, Paulo Gonçalves
Fast Multiscale Diffusion on Graphs. CoRR (2021)
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, Hicham Janati, Alain Rakotomamonjy, Ievgen Redko, Antoine Rolet, Antony Schutz, Vivien Seguy, Danica J. Sutherland, Romain Tavenard, Alexander Tong, Titouan Vayer
POT: Python Optimal Transport. J. Mach. Learn. Res. 22 (2021)
Cédric Vincent-Cuaz, Titouan Vayer, Rémi Flamary, Marco Corneli, Nicolas Courty
Online Graph Dictionary Learning. CoRR (2021)
Titouan Vayer, Rémi Gribonval
Controlling Wasserstein distances by Kernel norms with application to Compressive Statistical Learning. CoRR (2021)
Cédric Vincent-Cuaz, Titouan Vayer, Rémi Flamary, Marco Corneli, Nicolas Courty
Online Graph Dictionary Learning. ICML (2021)
Titouan Vayer, Laetitia Chapel, Nicolas Courty, Rémi Flamary, Yann Soullard, Romain Tavenard
Time Series Alignment with Global Invariances. CoRR (2020)
Titouan Vayer
A contribution to Optimal Transport on incomparable spaces. CoRR (2020)
Titouan Vayer, Laetitia Chapel, Rémi Flamary, Romain Tavenard, Nicolas Courty
Fused Gromov-Wasserstein Distance for Structured Objects. Algorithms 13 (9) (2020)
Titouan Vayer, Ievgen Redko, Rémi Flamary, Nicolas Courty
CO-Optimal Transport. NeurIPS (2020)
Ievgen Redko, Titouan Vayer, Rémi Flamary, Nicolas Courty
CO-Optimal Transport. CoRR (2020)
Titouan Vayer, Nicolas Courty, Romain Tavenard, Laetitia Chapel, Rémi Flamary
Optimal Transport for structured data with application on graphs. ICML (2019)
Titouan Vayer, Rémi Flamary, Romain Tavenard, Laetitia Chapel, Nicolas Courty
Sliced Gromov-Wasserstein. CoRR (2019)
Titouan Vayer, Rémi Flamary, Nicolas Courty, Romain Tavenard, Laetitia Chapel
Sliced Gromov-Wasserstein. NeurIPS (2019)
Titouan Vayer, Laetitia Chapel, Rémi Flamary, Romain Tavenard, Nicolas Courty
Fused Gromov-Wasserstein distance for structured objects: theoretical foundations and mathematical properties. CoRR (2018)
Titouan Vayer, Laetitia Chapel, Rémi Flamary, Romain Tavenard, Nicolas Courty
Optimal Transport for structured data. CoRR (2018)