activity
20212026
most citedOn a linear Gromov-Wasserstein distance

26 citations · 57 across the 8 of their papers we have counts for

collaborators

8 papers

math.OC2026

Gromov-Wasserstein Quantization and Clustering: Structure, Rates, and Algorithms

Florian Beier, Stephan Eckstein

Clustering is a fundamental class of data analysis techniques with the most important representatives being centroid-based methods like -means. Such methods are strongly connect…

cs.LG2025

Joint Metric Space Embedding by Unbalanced OT with Gromov-Wasserstein Marginal Penalization

Florian Beier, Moritz Piening, Robert Beinert +1

We propose a new approach for unsupervised alignment of heterogeneous datasets, which maps data from two different domains without any known correspondences to a common metric spac…

math.NA2024

Tangential Fixpoint Iterations for Gromov-Wasserstein Barycenters

Florian Beier, Robert Beinert

The Gromov-Wasserstein (GW) transport problem is a relaxation of classic optimal transport, which seeks a transport between two measures while preserving their internal geometry. D…

stat.ML2024

Transfer Operators from Batches of Unpaired Points via Entropic Transport Kernels

Florian Beier, Hancheng Bi, Clément Sarrazin +2

In this paper, we are concerned with estimating the joint probability of random variables and , given independent observation blocks $(\boldsymbol{x}^i,\boldsymbol{y}^i)…

math.NA2022

Multi-marginal Approximation of the Linear Gromov-Wasserstein Distance

Florian Beier, Robert Beinert

Recently, two concepts from optimal transport theory have successfully been brought to the Gromov--Wasserstein (GW) setting. This introduces a linear version of the GW distance and…

math.OC2022★ 5 cited

Multi-Marginal Gromov-Wasserstein Transport and Barycenters

Florian Beier, Robert Beinert, Gabriele Steidl

Gromov-Wasserstein (GW) distances are combinations of Gromov-Hausdorff and Wasserstein distances that allow the comparison of two different metric measure spaces (mm-spaces). Due t…

math.NA2021★ 26 cited

On a linear Gromov-Wasserstein distance

Florian Beier, Robert Beinert, Gabriele Steidl

Gromov-Wasserstein distances are generalization of Wasserstein distances, which are invariant under distance preserving transformations. Although a simplified version of optimal tr…

math.OC2021★ 26 cited

Unbalanced Multi-Marginal Optimal Transport

Florian Beier, Johannes von Lindheim, Sebastian Neumayer +1

Entropy regularized optimal transport and its multi-marginal generalization have attracted increasing attention in various applications, in particular due to efficient Sinkhorn-lik…