arXiv Machine Learning June 2, 2026 By Junhyoung Chung, Euijong Song, Won Hwa Kim, Gunwoong Park

Convex Distance Operator Transport: A Convex and Geometry-Preserving Formulation

Read the original on arXiv Machine Learning →

arXiv:2606. 02047v1 Announce Type: cross Abstract: We introduce Convex Distance Operator Transport (CDOT), the first convex optimal transport framework that aligns distributions across heterogeneous domains by jointly preserving feature correspondence and intrinsic geometric structure.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

benchmarks

arXiv Machine Learning

Jul 28

Distance-Matrix Wasserstein Statistics for Scalable Gromov--Wasserstein Learning

arXiv:2605. 14981v2 Announce Type: replace Abstract: Gromov--Wasserstein (GW) distances compare graphs, shapes, and point clouds through internal distances, without requiring a common coordinate system.

By Ao Xu, Tieru Wu

benchmarks safety

arXiv Machine Learning

1d ago

The Observable Wasserstein Distance

arXiv:2605. 09916v2 Announce Type: replace-cross Abstract: We introduce the observable Wasserstein distance, a framework for deriving lower bounds on the Wasserstein distance between probability measures on Polish metric spaces, designed to bypass the computational intractability of exact optimal transport in large-scale, non-Euclidean datasets.

By Edivaldo Lopes dos Santos, Leandro Vicente Mauri, Washington Mio, Tom Needham

arXiv Machine Learning

Jun 29

Difference of Convex Programming in the Wasserstein Space with Applications to MMD Optimization

arXiv:2606. 27767v1 Announce Type: new Abstract: Optimizing functionals over the space of probability measures is now ubiquitous in machine learning.

By Cl\'ement Bonet, Pierre-Cyril Aubin-Frankowski, Youssef Mroueh

arXiv Machine Learning

Jun 4

Cone-Compatible Monge Geometry for High-Dimensional Ordered Optimal Transport

arXiv:2606. 04695v1 Announce Type: new Abstract: High-dimensional optimal transport is seldom available in closed form.

By Lei Luo, Hongliang Zhang, Jian Yang

arXiv Machine Learning

Jul 17

Distributionally Robust Optimization via Iterative Algorithms in Continuous Probability Spaces

arXiv:2412. 20556v2 Announce Type: replace-cross Abstract: We study distributionally robust optimization (DRO) for robust inference when the worst-case distribution is continuous, leading to significant computational challenges due to the infinite-dimensional nature of the optimization problem.

By Linglingzhi Zhu, Yunqin Zhu, Yao Xie

arXiv Machine Learning

Jul 28

On a linear fused Gromov-Wasserstein distance for graph structured data

arXiv:2203. 04711v2 Announce Type: replace Abstract: We present a framework for embedding graph structured data into a vector space, taking into account node features and topology of a graph into the optimal transport (OT) problem.

By Dai Hai Nguyen, Koji Tsuda

rag

Related stories

Distance-Matrix Wasserstein Statistics for Scalable Gromov--Wasserstein Learning

The Observable Wasserstein Distance

Difference of Convex Programming in the Wasserstein Space with Applications to MMD Optimization

Cone-Compatible Monge Geometry for High-Dimensional Ordered Optimal Transport

Distributionally Robust Optimization via Iterative Algorithms in Continuous Probability Spaces

On a linear fused Gromov-Wasserstein distance for graph structured data