Fast and Provably Convergent Algorithms for Gromov-Wasserstein in Graph Learning. - Jiajin Li; Jianheng Tang; Lemin Kong; Huikang Liu; Jia Li 009; Anthony Man-Cho So; Jose H. Blanchet (CoRR, 2022)

A Convergent Single-Loop Algorithm for Relaxation of Gromov-Wasserstein in Graph Data. - Jiajin Li; Jianheng Tang; Lemin Kong; Huikang Liu; Jia Li 009; Anthony Man-Cho So; Jose H. Blanchet (ICLR, 2023)

Outlier-Robust Gromov-Wasserstein for Graph Data. - Lemin Kong; Jiajin Li; Jianheng Tang; Anthony Man-Cho So (NeurIPS, 2023)

Outlier-Robust Gromov Wasserstein for Graph Data. - Lemin Kong; Jiajin Li; Anthony Man-Cho So (CoRR, 2023)

Common Learning Constraints Alter Interpretations of Direct Preference Optimization. - Lemin Kong; Xiangkun Hu; Tong He 002; David Wipf (AISTATS, 2025)

Explicit Preference Optimization: No Need for an Implicit Reward Model. - Xiangkun Hu; Lemin Kong; Tong He 002; David Wipf (CoRR, 2025)