52 citations · 104 across the 23 of their papers we have counts for
4 papers · 1 filter
Unifying Distributionally Robust Optimization via Optimal Transport Theory
Jose Blanchet, Daniel Kuhn, Jiajin Li +1
In recent years, two prominent paradigms have shaped distributionally robust optimization (DRO), modeling distributional ambiguity through -divergences and Wasserstein distances…
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…
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…
Robust Attributed Graph Alignment via Joint Structure Learning and Optimal Transport
Jianheng Tang, Weiqi Zhang, Jiajin Li +3
Graph alignment, which aims at identifying corresponding entities across multiple networks, has been widely applied in various domains. As the graphs to be aligned are usually cons…