Titouan Vayer

According to our database, Titouan Vayer authored at least 27 papers between 2018 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Schur's Positive-Definite Network: Deep Learning in the SPD cone with structure.

Can Pouliquen, Mathurin Massias, Titouan Vayer

CoRR, 2024

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

2023

Controlling Wasserstein Distances by Kernel Norms with Application to Compressive Statistical Learning.

Titouan Vayer, Rémi Gribonval

J. Mach. Learn. Res., 2023

Compressive Recovery of Sparse Precision Matrices.

Titouan Vayer, Etienne Lasalle, Rémi Gribonval, Paulo Gonçalves

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

Optimal Transport with Adaptive Regularisation.

Hugues Van Assel, Titouan Vayer, Rémi Flamary, Nicolas Courty

CoRR, 2023

Implicit Differentiation for Hyperparameter Tuning the Weighted Graphical Lasso.

Can Pouliquen, Paulo Gonçalves, Mathurin Massias, Titouan Vayer

CoRR, 2023

Learning Graphical Factor Models with Riemannian Optimization.

Alexandre Hippert-Ferrer, Florent Bouchard, Ammar Mian, Titouan Vayer, Arnaud Breloy

Proceedings of the Machine Learning and Knowledge Discovery in Databases: Research Track, 2023

SNEkhorn: Dimension Reduction with Symmetric Entropic Affinities.

Hugues Van Assel, Titouan Vayer, Rémi Flamary, Nicolas Courty

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Entropic Wasserstein Component Analysis.

Antoine Collas, Titouan Vayer, Rémi Flamary, Arnaud Breloy

Proceedings of the 33rd IEEE International Workshop on Machine Learning for Signal Processing, 2023

2022

Time Series Alignment with Global Invariances.

Titouan Vayer, Romain Tavenard, Laetitia Chapel, Rémi Flamary, Nicolas Courty, Yann Soullard

Trans. Mach. Learn. Res., 2022

Template based Graph Neural Network with Optimal Transport Distances.

Cédric Vincent-Cuaz, Rémi Flamary, Marco Corneli, Titouan Vayer, Nicolas Courty

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Semi-relaxed Gromov-Wasserstein divergence and applications on graphs.

Cédric Vincent-Cuaz, Rémi Flamary, Marco Corneli, Titouan Vayer, Nicolas Courty

Proceedings of the Tenth International Conference on Learning Representations, 2022

Fast Multiscale Diffusion On Graphs.

Sibylle Marcotte, Amélie Barbe, Rémi Gribonval, Titouan Vayer, Marc Sebban, Pierre Borgnat, Paulo Gonçalves

Proceedings of the IEEE International Conference on Acoustics, 2022

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, Hicham Janati, Alain Rakotomamonjy, Ievgen Redko, Antoine Rolet, Antony Schutz, Vivien Seguy, Danica J. Sutherland, Romain Tavenard, Alexander Tong, Titouan Vayer

J. Mach. Learn. Res., 2021

Semi-relaxed Gromov Wasserstein divergence with applications on graphs.

Cédric Vincent-Cuaz, Rémi Flamary, Marco Corneli, Titouan Vayer, Nicolas Courty

CoRR, 2021

Subspace Detours Meet Gromov-Wasserstein.

Clément Bonet, Titouan Vayer, Nicolas Courty, François Septier, Lucas Drumetz

Algorithms, 2021

Optimization of the Diffusion Time in Graph Diffused-Wasserstein Distances: Application to Domain Adaptation.

Amélie Barbe, Paulo Gonçalves, Marc Sebban, Pierre Borgnat, Rémi Gribonval, Titouan Vayer

Proceedings of the 33rd IEEE International Conference on Tools with Artificial Intelligence, 2021

Online Graph Dictionary Learning.

Cédric Vincent-Cuaz, Titouan Vayer, Rémi Flamary, Marco Corneli, Nicolas Courty

Proceedings of the 38th International Conference on Machine Learning, 2021

2020

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

Titouan Vayer

PhD thesis, 2020

A contribution to Optimal Transport on incomparable spaces.

Titouan Vayer

CoRR, 2020

Fused Gromov-Wasserstein Distance for Structured Objects.

Titouan Vayer, Laetitia Chapel, Rémi Flamary, Romain Tavenard, Nicolas Courty

Algorithms, 2020

CO-Optimal Transport.

Titouan Vayer, Ievgen Redko, Rémi Flamary, Nicolas Courty

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

2019

Sliced Gromov-Wasserstein.

Titouan Vayer, Rémi Flamary, Nicolas Courty, Romain Tavenard, Laetitia Chapel

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Optimal Transport for structured data with application on graphs.

Titouan Vayer, Nicolas Courty, Romain Tavenard, Laetitia Chapel, Rémi Flamary

Proceedings of the 36th International Conference on Machine Learning, 2019

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.

Titouan Vayer, Laetitia Chapel, Rémi Flamary, Romain Tavenard, Nicolas Courty

CoRR, 2018