
Ground Metric Learning on Graphs
Optimal transport (OT) distances between probability distributions are p...
11/08/2019 ∙ by Matthieu Heitz, et al. ∙ 83 ∙ shareread it

Universal Invariant and Equivariant Graph Neural Networks
Graph Neural Networks (GNN) come in many flavors, but should always be e...
05/13/2019 ∙ by Nicolas Keriven, et al. ∙ 13 ∙ shareread it

Stochastic Deep Networks
Machine learning is increasingly targeting areas where input data cannot...
11/19/2018 ∙ by Gwendoline de Bie, et al. ∙ 12 ∙ shareread it

Geometric Losses for Distributional Learning
Building upon recent advances in entropyregularized optimal transport, ...
05/15/2019 ∙ by Arthur Mensch, et al. ∙ 11 ∙ shareread it

Semidual Regularized Optimal Transport
Variational problems that involve Wasserstein distances and more general...
11/13/2018 ∙ by Marco Cuturi, et al. ∙ 6 ∙ shareread it

Sinkhorn Divergences for Unbalanced Optimal Transport
This paper extends the formulation of Sinkhorn divergences to the unbala...
10/28/2019 ∙ by Thibault Séjourné, et al. ∙ 6 ∙ shareread it

Degrees of freedom for offthegrid sparse estimation
A central question in modern machine learning and imaging sciences is to...
11/08/2019 ∙ by Clarice Poon, et al. ∙ 6 ∙ shareread it

GAN and VAE from an Optimal Transport Point of View
This short article revisits some of the ideas introduced in arXiv:1701.0...
06/06/2017 ∙ by Aude Genevay, et al. ∙ 0 ∙ shareread it

Learning Generative Models with Sinkhorn Divergences
The ability to compare two degenerate probability distributions (i.e. tw...
06/01/2017 ∙ by Aude Genevay, et al. ∙ 0 ∙ shareread it

Sensitivity Analysis for MirrorStratifiable Convex Functions
This paper provides a set of sensitivity analysis and activity identific...
07/11/2017 ∙ by Jalal Fadili, et al. ∙ 0 ∙ shareread it

Bayesian Modeling of Motion Perception using Dynamical Stochastic Textures
A common practice to account for psychophysical biases in vision is to f...
11/02/2016 ∙ by Jonathan Vacher, et al. ∙ 0 ∙ shareread it

A Smoothed Dual Approach for Variational Wasserstein Problems
Variational problems that involve Wasserstein distances have been recent...
03/09/2015 ∙ by Marco Cuturi, et al. ∙ 0 ∙ shareread it

Low Complexity Regularization of Linear Inverse Problems
Inverse problems and regularization theory is a central theme in contemp...
07/07/2014 ∙ by Samuel Vaiter, et al. ∙ 0 ∙ shareread it

Model Consistency of Partly Smooth Regularizers
This paper studies leastsquare regression penalized with partly smooth ...
05/05/2014 ∙ by Samuel Vaiter, et al. ∙ 0 ∙ shareread it

Biologically Inspired Dynamic Textures for Probing Motion Perception
Perception is often described as a predictive process based on an optima...
11/09/2015 ∙ by Jonathan Vacher, et al. ∙ 0 ∙ shareread it

Fast Optimal Transport Averaging of Neuroimaging Data
Knowing how the Human brain is anatomically and functionally organized a...
03/30/2015 ∙ by Alexandre Gramfort, et al. ∙ 0 ∙ shareread it

Risk estimation for matrix recovery with spectral regularization
In this paper, we develop an approach to recursively estimate the quadra...
05/07/2012 ∙ by CharlesAlban Deledalle, et al. ∙ 0 ∙ shareread it

Regularized Discrete Optimal Transport
This article introduces a generalization of the discrete optimal transpo...
07/21/2013 ∙ by Sira Ferradans, et al. ∙ 0 ∙ shareread it

A Panorama on Multiscale Geometric Representations, Intertwining Spatial, Directional and Frequency Selectivity
The richness of natural images makes the quest for optimal representatio...
01/27/2011 ∙ by Laurent Jacques, et al. ∙ 0 ∙ shareread it

Wasserstein Dictionary Learning: Optimal Transportbased unsupervised nonlinear dictionary learning
This article introduces a new nonlinear dictionary learning method for ...
08/07/2017 ∙ by Morgan A. Schmitz, et al. ∙ 0 ∙ shareread it

A LowRank Approach to OffTheGrid Sparse Deconvolution
We propose a new solver for the sparse spikes deconvolution problem over...
12/23/2017 ∙ by Paul Catala, et al. ∙ 0 ∙ shareread it

Quantum Optimal Transport for Tensor Field Processing
This article introduces a new notion of optimal transport (OT) between t...
12/20/2016 ∙ by Gabriel Peyré, et al. ∙ 0 ∙ shareread it

Computational Optimal Transport
Optimal Transport (OT) is a mathematical gem at the interface between pr...
03/01/2018 ∙ by Gabriel Peyré, et al. ∙ 0 ∙ shareread it

A Dual Certificates Analysis of Compressive OfftheGrid Recovery
Many problems in machine learning and imaging can be framed as an infini...
02/23/2018 ∙ by Clarice Poon, et al. ∙ 0 ∙ shareread it

Support Localization and the Fisher Metric for offthegrid Sparse Regularization
Sparse regularization is a central technique for both machine learning (...
10/08/2018 ∙ by Clarice Poon, et al. ∙ 0 ∙ shareread it

Sample Complexity of Sinkhorn divergences
Optimal transport (OT) and maximum mean discrepancies (MMD) are now rout...
10/05/2018 ∙ by Aude Genevay, et al. ∙ 0 ∙ shareread it

Interpolating between Optimal Transport and MMD using Sinkhorn Divergences
Comparing probability distributions is a fundamental problem in data sci...
10/18/2018 ∙ by Jean Feydy, et al. ∙ 0 ∙ shareread it
Gabriel Peyré
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CNRS senior researcher (DR2), working at the DMA, École Normale Supérieure. I am also an associate member of the Mokaplan INRIA/CNRS/ParisDauphine research group.