385 citations · 1.5k across the 64 of their papers we have counts for
7 papers · 1 filter
Fused Gromov-Wasserstein distance for structured objects: theoretical foundations and mathematical properties
Titouan Vayer, Laetita Chapel, Rémi Flamary +2
Optimal transport theory has recently found many applications in machine learning thanks to its capacity for comparing various machine learning objects considered as distributions.…
An Entropic Optimal Transport Loss for Learning Deep Neural Networks under Label Noise in Remote Sensing Images
Bharath Bhushan Damodaran, Rémi Flamary, Viven Seguy +1
Deep neural networks have established as a powerful tool for large scale supervised classification tasks. The state-of-the-art performances of deep neural networks are conditioned…
Optimal Transport for structured data with application on graphs
Titouan Vayer, Laetitia Chapel, Rémi Flamary +2
This work considers the problem of computing distances between structured objects such as undirected graphs, seen as probability distributions in a specific metric space. We consid…
DeepJDOT: Deep Joint Distribution Optimal Transport for Unsupervised Domain Adaptation
Bharath Bhushan Damodaran, Benjamin Kellenberger, Rémi Flamary +2
In computer vision, one is often confronted with problems of domain shifts, which occur when one applies a classifier trained on a source dataset to target data sharing similar cha…
Optimal Transport for Multi-source Domain Adaptation under Target Shift
Ievgen Redko, Nicolas Courty, Rémi Flamary +1
In this paper, we propose to tackle the problem of reducing discrepancies between multiple domains referred to as multi-source domain adaptation and consider it under the target sh…
Distance Measure Machines
Alain Rakotomamonjy, Abraham Traoré, Maxime Berar +2
This paper presents a distance-based discriminative framework for learning with probability distributions. Instead of using kernel mean embeddings or generalized radial basis kerne…