Do We Really Need Contrastive Learning for Graph Representation? - Yulan Hu; Sheng Ouyang; Jingyu Liu; Ge Chen 006; Zhirui Yang; Junchen Wan; Fuzheng Zhang; Zhongyuan Wang 006; Yong Liu 018 (CoRR, 2023)

Advancing Latent Representation Ranking for Masked Graph Autoencoder. - Yulan Hu; Ge Chen 006; Sheng Ouyang; Zhirui Yang; Junchen Wan; Fuzheng Zhang; Zhongyuan Wang 006; Zhao Cao; Shangquan Wu; Yong Liu 018 (DASFAA (6), 2024)

IdmGAE: Importance-Inspired Dynamic Masking for Graph Autoencoders. - Ge Chen 006; Yulan Hu; Sheng Ouyang; Zhirui Yang; Yong Liu 018; Cuicui Luo (SIGIR, 2024)

Exploring Task Unification in Graph Representation Learning via Generative Approach. - Yulan Hu; Sheng Ouyang; Zhirui Yang; Ge Chen 006; Junchen Wan; Xiao Wang; Yong Liu 018 (CoRR, 2024)

Towards Comprehensive Preference Data Collection for Reward Modeling. - Yulan Hu; Qingyang Li 001; Sheng Ouyang; Ge Chen 006; Kaihui Chen; Lijun Mei; Xucheng Ye; Fuzheng Zhang; Yong Liu 018 (CoRR, 2024)

Preserving Node Distinctness in Graph Autoencoders via Similarity Distillation. - Ge Chen 006; Yulan Hu; Sheng Ouyang; Yong Liu 018; Cuicui Luo (CoRR, 2024)

GUNDAM: Aligning Large Language Models with Graph Understanding. - Sheng Ouyang; Yulan Hu; Ge Chen 006; Yong Liu 018 (CoRR, 2024)

Towards Reward Fairness in RLHF: From a Resource Allocation Perspective. - Sheng Ouyang; Yulan Hu; Ge Chen 006; Qingyang Li 001; Fuzheng Zhang; Yong Liu 018 (ACL (1), 2025)