2 citations · 3 across the 3 of their papers we have counts for
3 papers
cs.CG2023
A Convergent Single-Loop Algorithm for Relaxation of Gromov-Wasserstein in Graph Data
Jiajin Li, Jianheng Tang, Lemin Kong +4
In this work, we present the Bregman Alternating Projected Gradient (BAPG) method, a single-loop algorithm that offers an approximate solution to the Gromov-Wasserstein (GW) distan…
cs.LG2023★ 1 cited
Outlier-Robust Gromov-Wasserstein for Graph Data
Lemin Kong, Jiajin Li, Jianheng Tang +1
Gromov-Wasserstein (GW) distance is a powerful tool for comparing and aligning probability distributions supported on different metric spaces. Recently, GW has become the main mode…
cs.LG2022★ 2 cited
Fast and Provably Convergent Algorithms for Gromov-Wasserstein in Graph Data
Jiajin Li, Jianheng Tang, Lemin Kong +4
In this paper, we study the design and analysis of a class of efficient algorithms for computing the Gromov-Wasserstein (GW) distance tailored to large-scale graph learning tasks.…