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Jingni Song

2 accepted papers

2026

Achieving Structurally Robust Gromov Wasserstein Distance via Adaptive Dual-Mask

Kangke Cheng, Jiawei Huang, Jingni Song, Wanlin Zhang, Bangxian Han, Hu Ding
ICML 2026poster

The Gromov-Wasserstein (GW) distance enables comparison across different spaces but remains fragile to structural noise due to its global quadratic coupling. Existing robust extensions primarily rely on node-centric mass relaxation. However, we argue that this strategy is far from sufficient: it onl…

Cited by 0Source ↗Scholar ↗
2026

LoBCD-GW: A Fast and Data-Dependent Algorithm for Computing Gromov-Wasserstein Distance via Localized Block Coordinate Descent

Jingni Song, Jiawei Huang, Kangke Cheng, Bangxian Han, Hu Ding
ICML 2026poster

The Gromov-Wasserstein (GW) distance provides a powerful framework for aligning structured data by comparing the intrinsic geometries of metric measure spaces, and has become a fundamental tool in machine learning. Most existing methods leverage entropy regularization to reduce the computational com…

Cited by 0Source ↗Scholar ↗