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Research paper on scalable Gromov-Wasserstein learning withdrawn

By PulseAugur Editorial · [1 sources] ·

A research paper titled "Distance-Matrix Wasserstein Statistics for Scalable Gromov--Wasserstein Learning" has been withdrawn by its author, Ao Xu. The paper proposed a new method called Distance-Matrix Wasserstein (DMW) as a scalable approximation and lower bound for Gromov--Wasserstein (GW) distances, which are used to compare graphs and shapes. DMW works by comparing laws of random distance matrices rather than optimizing global point-level alignment. The authors claimed theoretical guarantees and demonstrated its effectiveness on various benchmarks, but the paper was later withdrawn. AI

IMPACT This withdrawn research paper does not have a direct impact on AI operations.

RANK_REASON The cluster contains a withdrawn academic paper. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Research paper on scalable Gromov-Wasserstein learning withdrawn

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The cluster contains a withdrawn academic paper. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Ao Xu, Tieru Wu ·

    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. This invariance is powerful, but discrete GW is a nonconvex quadratic optimal …

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