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07/19/2023
Properties of Discrete Sliced Wasserstein Losses
The Sliced Wasserstein (SW) distance has become a popular alternative to...
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05/30/2023
Convolutional Monge Mapping Normalization for learning on biosignals
In many machine learning applications on signals and biomedical data, es...
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05/23/2023
SNEkhorn: Dimension Reduction with Symmetric Entropic Affinities
Many approaches in machine learning rely on a weighted graph to encode t...
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03/09/2023
Entropic Wasserstein Component Analysis
Dimension reduction (DR) methods provide systematic approaches for analy...
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06/19/2022
Aligning individual brains with Fused Unbalanced Gromov-Wasserstein
Individual brains vary in both anatomy and functional organization, even...
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05/31/2022
Template based Graph Neural Network with Optimal Transport Distances
Current Graph Neural Networks (GNN) architectures generally rely on two ...
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05/30/2022
Unbalanced CO-Optimal Transport
Optimal transport (OT) compares probability distributions by computing a...
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10/29/2021
Sliding window strategy for convolutional spike sorting with Lasso : Algorithm, theoretical guarantees and complexity
We present a fast algorithm for the resolution of the Lasso for convolut...
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10/06/2021
Semi-relaxed Gromov Wasserstein divergence with applications on graphs
Comparing structured objects such as graphs is a fundamental operation i...
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10/01/2021
Factored couplings in multi-marginal optimal transport via difference of convex programming
Optimal transport (OT) theory underlies many emerging machine learning (...
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06/08/2021
Unbalanced Optimal Transport through Non-negative Penalized Linear Regression
This paper addresses the problem of Unbalanced Optimal Transport (UOT) i...
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03/05/2021
Unbalanced minibatch Optimal Transport; applications to Domain Adaptation
Optimal transport distances have found many applications in machine lear...
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02/12/2021
Online Graph Dictionary Learning
Dictionary learning is a key tool for representation learning, that expl...
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01/05/2021
Minibatch optimal transport distances; analysis and applications
Optimal transport distances have become a classic tool to compare probab...
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09/18/2020
Contextual Semantic Interpretability
Convolutional neural networks (CNN) are known to learn an image represen...
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07/13/2020
Representation Transfer by Optimal Transport
Deep learning currently provides the best representations of complex obj...
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06/24/2020
Provably Convergent Working Set Algorithm for Non-Convex Regularized Regression
Owing to their statistical properties, non-convex sparse regularizers ha...
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06/23/2020
Multi-source Domain Adaptation via Weighted Joint Distributions Optimal Transport
The problem of domain adaptation on an unlabeled target dataset using kn...
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02/10/2020
Time Series Alignment with Global Invariances
In this work we address the problem of comparing time series while takin...
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02/10/2020
CO-Optimal Transport
Optimal transport (OT) is a powerful geometric and probabilistic tool fo...
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10/09/2019
Learning with minibatch Wasserstein : asymptotic and gradient properties
Optimal transport distances are powerful tools to compare probability di...
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06/28/2019
Large scale Lasso with windowed active set for convolutional spike sorting
Spike sorting is a fundamental preprocessing step in neuroscience that i...
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05/24/2019
Concentration bounds for linear Monge mapping estimation and optimal transport domain adaptation
This article investigates the quality of the estimator of the linear Mon...
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05/24/2019
Sliced Gromov-Wasserstein
Recently used in various machine learning contexts, the Gromov-Wasserste...
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04/08/2019
Pushing the right boundaries matters! Wasserstein Adversarial Training for Label Noise
Noisy labels often occur in vision datasets, especially when they are is...
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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...
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10/02/2018
An Entropic Optimal Transport Loss for Learning Deep Neural Networks under Label Noise in Remote Sensing Images
Deep neural networks have established as a powerful tool for large scale...
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05/23/2018
Optimal Transport for structured data
Optimal transport has recently gained a lot of interest in the machine l...
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03/27/2018
DeepJDOT: Deep Joint distribution optimal transport for unsupervised domain adaptation
In computer vision, one is often confronted with problems of domain shif...
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03/13/2018
Optimal Transport for Multi-source Domain Adaptation under Target Shift
In this paper, we propose to tackle the problem of reducing discrepancie...
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03/01/2018
Wasserstein Distance Measure Machines
This paper presents a distance-based discriminative framework for learni...
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11/30/2017
On reducing the communication cost of the diffusion LMS algorithm
The rise of digital and mobile communications has recently made the worl...
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11/07/2017
Large-Scale Optimal Transport and Mapping Estimation
This paper presents a novel two-step approach for the fundamental proble...
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10/20/2017
Learning Wasserstein Embeddings
The Wasserstein distance received a lot of attention recently in the com...
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05/24/2017
Joint Distribution Optimal Transportation for Domain Adaptation
This paper deals with the unsupervised domain adaptation problem, where ...
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03/10/2017
Multi-frequency image reconstruction for radio-interferometry with self-tuned regularization parameters
As the world's largest radio telescope, the Square Kilometer Array (SKA)...
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12/14/2016
Astronomical image reconstruction with convolutional neural networks
State of the art methods in astronomical image reconstruction rely on th...
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09/30/2016
Optimal spectral transportation with application to music transcription
Many spectral unmixing methods rely on the non-negative decomposition of...
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08/29/2016
Wasserstein Discriminant Analysis
Wasserstein Discriminant Analysis (WDA) is a new supervised method that ...
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06/23/2016
Non-convex regularization in remote sensing
In this paper, we study the effect of different regularizers and their i...
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06/23/2016
Multiclass feature learning for hyperspectral image classification: sparse and hierarchical solutions
In this paper, we tackle the question of discovering an effective set of...
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10/22/2015
Generalized conditional gradient: analysis of convergence and applications
The objectives of this technical report is to provide additional results...
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07/02/2015
Distributed image reconstruction for very large arrays in radio astronomy
Current and future radio interferometric arrays such as LOFAR and SKA ar...
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07/02/2015
