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07/26/2023
Simulation-based Inference for Cardiovascular Models
Over the past decades, hemodynamics simulators have steadily evolved and...
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06/20/2023
Learning Costs for Structured Monge Displacements
Optimal transport theory has provided machine learning with several tool...
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05/31/2023
Unbalanced Low-rank Optimal Transport Solvers
The relevance of optimal transport methods to machine learning has long ...
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02/09/2023
The Monge Gap: A Regularizer to Learn All Transport Maps
Optimal transport (OT) theory has been been used in machine learning to ...
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02/08/2023
Monge, Bregman and Occam: Interpretable Optimal Transport in High-Dimensions with Feature-Sparse Maps
Optimal transport (OT) theory focuses, among all maps T:ℝ^d→ℝ^d that can...
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06/28/2022
Supervised Training of Conditional Monge Maps
Optimal transport (OT) theory describes general principles to define and...
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06/15/2022
Rethinking Initialization of the Sinkhorn Algorithm
Computing an optimal transport (OT) coupling between distributions plays...
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05/24/2022
Low-rank Optimal Transport: Approximation, Statistics and Debiasing
The matching principles behind optimal transport (OT) play an increasing...
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04/18/2022
Simultaneous Multiple-Prompt Guided Generation Using Differentiable Optimal Transport
Recent advances in deep learning, such as powerful generative models and...
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03/11/2022
Averaging Spatio-temporal Signals using Optimal Transport and Soft Alignments
Several fields in science, from genomics to neuroimaging, require monito...
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02/17/2022
Debiaser Beware: Pitfalls of Centering Regularized Transport Maps
Estimating optimal transport (OT) maps (a.k.a. Monge maps) between two m...
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02/11/2022
Recovering Stochastic Dynamics via Gaussian Schrödinger Bridges
We propose a new framework to reconstruct a stochastic process {ℙ_t: t ∈...
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01/28/2022
Optimal Transport Tools (OTT): A JAX Toolbox for all things Wasserstein
Optimal transport tools (OTT-JAX) is a Python toolbox that can solve opt...
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11/25/2021
Randomized Stochastic Gradient Descent Ascent
An increasing number of machine learning problems, such as robust or adv...
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06/11/2021
JKOnet: Proximal Optimal Transport Modeling of Population Dynamics
Consider a heterogeneous population of points evolving with time. While ...
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06/02/2021
Linear-Time Gromov Wasserstein Distances using Low Rank Couplings and Costs
The ability to compare and align related datasets living in heterogeneou...
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05/31/2021
Efficient and Modular Implicit Differentiation
Automatic differentiation (autodiff) has revolutionized machine learning...
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03/08/2021
Low-Rank Sinkhorn Factorization
Several recent applications of optimal transport (OT) theory to machine ...
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06/22/2020
On Projection Robust Optimal Transport: Sample Complexity and Model Misspecification
Optimal transport (OT) distances are increasingly used as loss functions...
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06/12/2020
Projection Robust Wasserstein Distance and Riemannian Optimization
Projection robust Wasserstein (PRW) distance, or Wasserstein projection ...
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06/12/2020
Handling Multiple Costs in Optimal Transport: Strong Duality and Efficient Computation
We introduce an extension of the optimal transportation (OT) problem whe...
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06/12/2020
Linear Time Sinkhorn Divergences using Positive Features
Although Sinkhorn divergences are now routinely used in data sciences to...
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06/03/2020
Debiased Sinkhorn barycenters
Entropy regularization in optimal transport (OT) has been the driver of ...
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04/26/2020
Noisy Adaptive Group Testing using Bayesian Sequential Experimental Design
When test resources are scarce, a viable alternative to test for the pre...
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02/20/2020
Learning with Differentiable Perturbed Optimizers
Machine learning pipelines often rely on optimization procedures to make...
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02/12/2020
Revisiting Fixed Support Wasserstein Barycenter: Computational Hardness and Efficient Algorithms
We study the fixed-support Wasserstein barycenter problem (FS-WBP), whic...
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02/12/2020
Fixed-Support Wasserstein Barycenters: Computational Hardness and Fast Algorithm
We study the fixed-support Wasserstein barycenter problem (FS-WBP), whic...
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02/12/2020
Computational Hardness and Fast Algorithm for Fixed-Support Wasserstein Barycenter
We study in this paper the fixed-support Wasserstein barycenter problem ...
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02/10/2020
Regularized Optimal Transport is Ground Cost Adversarial
Regularizing Wasserstein distances has proved to be the key in the recen...
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02/10/2020
Missing Data Imputation using Optimal Transport
Missing data is a crucial issue when applying machine learning algorithm...
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02/08/2020
Supervised Quantile Normalization for Low-rank Matrix Approximation
Low rank matrix factorization is a fundamental building block in machine...
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02/05/2020
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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11/08/2019
Ground Metric Learning on Graphs
Optimal transport (OT) distances between probability distributions are p...
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10/09/2019
Spatio-Temporal Alignments: Optimal transport through space and time
Comparing data defined over space and time is notoriously hard, because ...
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10/03/2019
Multi-subject MEG/EEG source imaging with sparse multi-task regression
Magnetoencephalography and electroencephalography (M/EEG) are non-invasi...
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09/30/2019
On the Complexity of Approximating Multimarginal Optimal Transport
We study the complexity of approximating the multimarginal optimal trans...
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05/30/2019
Deep multi-class learning from label proportions
We propose a learning algorithm capable of learning from label proportio...
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05/28/2019
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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05/26/2019
Regularity as Regularization: Smooth and Strongly Convex Brenier Potentials in Optimal Transport
The problem of estimating Wasserstein distances in high-dimensional spac...
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05/26/2019
Evaluating Generative Models Using Divergence Frontiers
Despite the tremendous progress in the estimation of generative models, ...
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05/24/2019
Subspace Detours: Building Transport Plans that are Optimal on Subspace Projections
Sliced Wasserstein metrics between probability measures solve the optima...
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02/13/2019
Group level MEG/EEG source imaging via optimal transport: minimum Wasserstein estimates
Magnetoencephalography (MEG) and electroencephalogra-phy (EEG) are non-i...
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02/01/2019
Tree-Sliced Approximation of Wasserstein Distances
Optimal transport () theory provides a useful set of tools to compare pr...
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01/25/2019
Subspace Robust Wasserstein distances
Making sense of Wasserstein distances between discrete measures in high-...
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11/19/2018
Stochastic Deep Networks
Machine learning is increasingly targeting areas where input data cannot...
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11/13/2018
Semi-dual Regularized Optimal Transport
Variational problems that involve Wasserstein distances and more general...
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11/02/2018
Unsupervised Hyperalignment for Multilingual Word Embeddings
We consider the problem of aligning continuous word representations, lea...
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10/05/2018
Sample Complexity of Sinkhorn divergences
Optimal transport (OT) and maximum mean discrepancies (MMD) are now rout...
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05/22/2018
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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05/20/2018