Research paper on scalable Gromov-Wasserstein learning withdrawn

A research paper titled "Distance-Matrix Wasserstein Statistics for Scalable Gromov--Wasserstein Learning" has been withdrawn by its author, Ao Xu.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claimed theoretical guarantees and demonstrated its effectiveness on various benchmarks, but the paper was later withdrawn.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IMPACT This withdrawn research paper does not have a direct impact on AI operations.

RANK_REASON The cluster contains a withdrawn academic paper.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]

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• Ao Xu
• arXiv
• Distance-Matrix Wasserstein
• Gromov--Wasserstein

• paper
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