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From the 1 of 9 linked papers with an AI index.

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9 papers

math.AP2026

Gluing methods for quantitative stability of optimal transport maps

Cyril Letrouit, Quentin Mérigot

The paper proves that the optimal quadratic transport map from a fixed density to a varying measure depends bi‑Hölder continuously on the target measure, establishing quantitative…

stat.ML2026

Highly Data Parallelizable Estimation of the Sliced-Wasserstein Distance Using Cumulative Distribution Functions

Christophe Vauthier, Quentin Mérigot, Anna Korba

The Sliced Wasserstein (SW) distance has emerged as a computationally attractive alternative to the Wasserstein distance by leveraging one-dimensional optimal transport along rando…

math.OC2026

Semi-discrete convex order and Laguerre tessellation fitting

David P. Bourne, Thomas Gallouët, Quentin Mérigot +1

Laguerre tessellations offer an efficient way to parameterize a large class of convex partitions of Euclidean space using only a set of points and scalar weights. For this reason,…

math.OC2026

Polynomial diagrams for microstructure modelling

David P. Bourne, Maciej Buze, Thomas Gallouët +1

We formulate a framework of polynomial diagrams, which are a generalisation of power diagrams (PDs) and anisotropic power diagrams (APDs) allowing for boundaries between cells to b…

math.OC2026

Particle method for a nonlinear multimarginal optimal transport problem

Adrien Cances, Quentin Mérigot, Luca Nenna

We study a nonlinear multimarginal optimal transport problem arising in risk management, where the objective is to maximize a spectral risk measure of the pushforward of a coupling…

math.ST2025

Sharp comparisons between sliced and standard -Wasserstein distances

Guillaume Carlier, Alessio Figalli, Quentin Mérigot +1

Sliced Wasserstein distances are widely used in practice as a computationally efficient alternative to Wasserstein distances in high dimensions. In this paper, motivated by theoret…