
Ground Metric Learning on Graphs
Optimal transport (OT) distances between probability distributions are p...
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Learning with Differentiable Perturbed Optimizers
Machine learning pipelines often rely on optimization procedures to make...
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Missing Data Imputation using Optimal Transport
Missing data is a crucial issue when applying machine learning algorithm...
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On the Complexity of Approximating Multimarginal Optimal Transport
We study the complexity of approximating the multimarginal optimal trans...
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SpatioTemporal Alignments: Optimal transport through space and time
Comparing data defined over space and time is notoriously hard, because ...
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Stochastic Deep Networks
Machine learning is increasingly targeting areas where input data cannot...
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TreeSliced Approximation of Wasserstein Distances
Optimal transport () theory provides a useful set of tools to compare pr...
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Multisubject MEG/EEG source imaging with sparse multitask regression
Magnetoencephalography and electroencephalography (M/EEG) are noninvasi...
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Semidual Regularized Optimal Transport
Variational problems that involve Wasserstein distances and more general...
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Group level MEG/EEG source imaging via optimal transport: minimum Wasserstein estimates
Magnetoencephalography (MEG) and electroencephalography (EEG) are noni...
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Revisiting Fixed Support Wasserstein Barycenter: Computational Hardness and Efficient Algorithms
We study the fixedsupport Wasserstein barycenter problem (FSWBP), whic...
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Fast and Robust Comparison of Probability Measures in Heterogeneous Spaces
The problem of comparing distributions endowed with their own geometry a...
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GAN and VAE from an Optimal Transport Point of View
This short article revisits some of the ideas introduced in arXiv:1701.0...
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Learning Generative Models with Sinkhorn Divergences
The ability to compare two degenerate probability distributions (i.e. tw...
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SoftDTW: a Differentiable Loss Function for TimeSeries
We propose in this paper a differentiable learning loss between time ser...
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Wasserstein Discriminant Analysis
Wasserstein Discriminant Analysis (WDA) is a new supervised method that ...
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On Wasserstein Two Sample Testing and Related Families of Nonparametric Tests
Nonparametric two sample or homogeneity testing is a decision theoretic ...
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Wasserstein Training of Boltzmann Machines
The Boltzmann machine provides a useful framework to learn highly comple...
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Principal Geodesic Analysis for Probability Measures under the Optimal Transport Metric
Given a family of probability measures in P(X), the space of probability...
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A Smoothed Dual Approach for Variational Wasserstein Problems
Variational problems that involve Wasserstein distances have been recent...
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Sinkhorn Distances: Lightspeed Computation of Optimal Transportation Distances
Optimal transportation distances are a fundamental family of parameteriz...
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Fast Optimal Transport Averaging of Neuroimaging Data
Knowing how the Human brain is anatomically and functionally organized a...
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Positivity and Transportation
We prove in this paper that the weighted volume of the set of integral t...
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Autoregressive Kernels For Time Series
We propose in this work a new family of kernels for variablelength time...
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Positive Definite Kernels in Machine Learning
This survey is an introduction to positive definite kernels and the set ...
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Kernels for Measures Defined on the Gram Matrix of their Support
We present in this work a new family of kernels to compare positive meas...
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Ground Metric Learning
Transportation distances have been used for more than a decade now in ma...
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A kernel for time series based on global alignments
We propose in this paper a new family of kernels to handle times series,...
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Wasserstein Dictionary Learning: Optimal Transportbased unsupervised nonlinear dictionary learning
This article introduces a new nonlinear dictionary learning method for ...
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Sliced Wasserstein Kernel for Persistence Diagrams
Persistence diagrams (PDs) play a key role in topological data analysis ...
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Computational Optimal Transport
Optimal Transport (OT) is a mathematical gem at the interface between pr...
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Wasserstein regularization for sparse multitask regression
Two important elements have driven recent innovation in the field of reg...
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Generalizing Point Embeddings using the Wasserstein Space of Elliptical Distributions
Embedding complex objects as vectors in low dimensional spaces is a long...
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Large Scale computation of Means and Clusters for Persistence Diagrams using Optimal Transport
Persistence diagrams (PDs) are now routinely used to summarize the under...
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Unsupervised Hyperalignment for Multilingual Word Embeddings
We consider the problem of aligning continuous word representations, lea...
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Sample Complexity of Sinkhorn divergences
Optimal transport (OT) and maximum mean discrepancies (MMD) are now rout...
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Subspace Robust Wasserstein distances
Making sense of Wasserstein distances between discrete measures in high...
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Subspace Detours: Building Transport Plans that are Optimal on Subspace Projections
Sliced Wasserstein metrics between probability measures solve the optima...
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Regularity as Regularization: Smooth and Strongly Convex Brenier Potentials in Optimal Transport
The problem of estimating Wasserstein distances in highdimensional spac...
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Evaluating Generative Models Using Divergence Frontiers
Despite the tremendous progress in the estimation of generative models, ...
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Deep multiclass learning from label proportions
We propose a learning algorithm capable of learning from label proportio...
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Differentiable Sorting using Optimal Transport:The Sinkhorn CDF and Quantile Operator
Sorting an array is a fundamental routine in machine learning, one that ...
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Supervised Quantile Normalization for Lowrank Matrix Approximation
Low rank matrix factorization is a fundamental building block in machine...
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Regularized Optimal Transport is Ground Cost Adversarial
Regularizing Wasserstein distances has proved to be the key in the recen...
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Marco Cuturi
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Professor of Statistics in CREST  ENSAE at Université ParisSaclay, Ph.D. under the supervision of JeanPhilippe Vert in 11/2005 from the Ecole des Mines de Paris, Postdoctoral researcher at the Institute of Statistical Mathematics, Tokyo, between 11/2005 and 03/2007. ORFE department of Princeton University between 02/2009 and 08/2010 as a lecturer, I was at the Graduate School of Informatics of Kyoto University between 09/2010 and 09/2016 as an associate professor (tenured in 11/2013).