AI RESEARCH
Min Generalized Sliced Gromov Wasserstein: A Scalable Path to Gromov Wasserstein
arXiv CS.LG
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ArXi:2605.13753v1 Announce Type: new We propose min Generalized Sliced Gromo--Wasserstein (min-GSGW), a sliced formulation for the Gromo--Wasserstein (GW) problem using expressive generalized slicers. The key idea is to learn coupled nonlinear slicers that assign compatible push-forward values to both input measures, so that monotone coupling in the projected domain lifts to a transport plan evaluated against the GW objective in the original spaces. The resulting plan induces a GW objective value, and min-GSGW minimizes this cost directly in the original spaces.