Titouan Vayer | DeepAI

AI Chat

Featured Co-authors

Rémi Gribonval
58 publications
Nicolas Courty
46 publications
Rémi Flamary
44 publications
Mathurin Massias
14 publications
Pierre Borgnat
13 publications
Romain Tavenard
11 publications
Marc Sebban
11 publications
Marco Corneli
10 publications
Laetitia Chapel
10 publications
Arnaud Breloy
8 publications
Paulo Gonçalves
6 publications

∙ 07/05/2023

Implicit Differentiation for Hyperparameter Tuning the Weighted Graphical Lasso

We provide a framework and algorithm for tuning the hyperparameters of t...

0 Can Pouliquen, et al. ∙

∙ 05/23/2023

SNEkhorn: Dimension Reduction with Symmetric Entropic Affinities

Many approaches in machine learning rely on a weighted graph to encode t...

0 Hugues van Assel, et al. ∙

∙ 03/09/2023

Entropic Wasserstein Component Analysis

Dimension reduction (DR) methods provide systematic approaches for analy...

0 Antoine Collas, et al. ∙

∙ 10/21/2022

Learning Graphical Factor Models with Riemannian Optimization

Graphical models and factor analysis are well-established tools in multi...

0 Alexandre Hippert-Ferrer, et al. ∙

∙ 05/31/2022

Template based Graph Neural Network with Optimal Transport Distances

Current Graph Neural Networks (GNN) architectures generally rely on two ...

0 Cédric Vincent-Cuaz, et al. ∙

∙ 12/01/2021

Controlling Wasserstein distances by Kernel norms with application to Compressive Statistical Learning

Comparing probability distributions is at the crux of many machine learn...

0 Titouan Vayer, et al. ∙

∙ 10/06/2021

Semi-relaxed Gromov Wasserstein divergence with applications on graphs

Comparing structured objects such as graphs is a fundamental operation i...

0 Cédric Vincent-Cuaz, et al. ∙

∙ 04/29/2021

Fast Multiscale Diffusion on Graphs

Diffusing a graph signal at multiple scales requires computing the actio...

0 Sibylle Marcotte, et al. ∙

∙ 02/12/2021

Online Graph Dictionary Learning

Dictionary learning is a key tool for representation learning, that expl...

0 Cédric Vincent-Cuaz, et al. ∙

∙ 11/09/2020

A contribution to Optimal Transport on incomparable spaces

Optimal Transport is a theory that allows to define geometrical notions ...

0 Titouan Vayer, et al. ∙

∙ 02/10/2020

Time Series Alignment with Global Invariances

In this work we address the problem of comparing time series while takin...

41 Titouan Vayer, et al. ∙

∙ 02/10/2020

CO-Optimal Transport

Optimal transport (OT) is a powerful geometric and probabilistic tool fo...

12 Ivegen Redko, et al. ∙

∙ 05/24/2019

Sliced Gromov-Wasserstein

Recently used in various machine learning contexts, the Gromov-Wasserste...

10 Titouan Vayer, et al. ∙

∙ 11/07/2018

Fused Gromov-Wasserstein distance for structured objects: theoretical foundations and mathematical properties

Optimal transport theory has recently found many applications in machine...

4 Titouan Vayer, et al. ∙

∙ 05/23/2018

Optimal Transport for structured data

Optimal transport has recently gained a lot of interest in the machine l...

4 Titouan Vayer, et al. ∙

Are you a researcher?
Expose your work
to one of the largest
A.I. communities in the world