3 papers
cs.LG2025
A Novel Sliced Fused Gromov-Wasserstein Distance
Moritz Piening, Robert Beinert
The Gromov--Wasserstein (GW) distance and its fused extension (FGW) are powerful tools for comparing heterogeneous data. Their computation is, however, challenging since both dista…
cs.LG2025
Slicing the Gaussian Mixture Wasserstein Distance
Moritz Piening, Robert Beinert
Gaussian mixture models (GMMs) are widely used in machine learning for tasks such as clustering, classification, image reconstruction, and generative modeling. A key challenge in w…
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…