GraphBERT: Bridging Graph and Text for Malicious Behavior Detection on Social Media. - Jiele Wu; Chunhui Zhang; Zheyuan Liu 010; Erchi Zhang; Steven Wilson; Chuxu Zhang (ICDM, 2022)

Chasing All-Round Graph Representation Robustness: Model, Training, and Optimization. - Chunhui Zhang; Yijun Tian 001; Mingxuan Ju; Zheyuan Liu 010; Yanfang Ye 001; Nitesh V. Chawla; Chuxu Zhang (ICLR, 2023)

Fair Graph Representation Learning via Diverse Mixture-of-Experts. - Zheyuan Liu 010; Chunhui Zhang; Yijun Tian 001; Erchi Zhang; Chao Huang 001; Yanfang Ye 001; Chuxu Zhang (WWW, 2023)

Breaking the Trilemma of Privacy, Utility, Efficiency via Controllable Machine Unlearning. - Zheyuan Liu 010; Guangyao Dou; Yijun Tian 001; Chunhui Zhang; Eli Chien; Ziwei Zhu 001 (CoRR, 2023)

Towards Safer Large Language Models through Machine Unlearning. - Zheyuan Liu 010; Guangyao Dou; Zhaoxuan Tan; Yijun Tian 001; Meng Jiang 001 (ACL (Findings), 2024)

Invited: Graph Learning for Parameter Prediction of Quantum Approximate Optimization Algorithm. - Zhiding Liang; Gang Liu 025; Zheyuan Liu 010; Jinglei Cheng; Tianyi Hao 003; Kecheng Liu; Hang Ren; Zhixin Song; Ji Liu 007; Fanny Ye; Yiyu Shi 001 (DAC, 2024)

Democratizing Large Language Models via Personalized Parameter-Efficient Fine-tuning. - Zhaoxuan Tan; Qingkai Zeng 001; Yijun Tian 001; Zheyuan Liu 010; Bing Yin; Meng Jiang 001 (EMNLP, 2024)

Personalized Pieces: Efficient Personalized Large Language Models through Collaborative Efforts. - Zhaoxuan Tan; Zheyuan Liu 010; Meng Jiang 001 (EMNLP, 2024)

Breaking the Trilemma of Privacy, Utility, and Efficiency via Controllable Machine Unlearning. - Zheyuan Liu 010; Guangyao Dou; Eli Chien; Chunhui Zhang; Yijun Tian 001; Ziwei Zhu 001 (WWW, 2024)

Can we Soft Prompt LLMs for Graph Learning Tasks? - Zheyuan Liu 010; Xiaoxin He; Yijun Tian 001; Nitesh V. Chawla (WWW (Companion Volume), 2024)

UGMAE: A Unified Framework for Graph Masked Autoencoders. - Yijun Tian 001; Chuxu Zhang; Ziyi Kou; Zheyuan Liu 010; Xiangliang Zhang 001; Nitesh V. Chawla (CoRR, 2024)

Graph Learning for Parameter Prediction of Quantum Approximate Optimization Algorithm. - Zhiding Liang; Gang Liu 025; Zheyuan Liu 010; Jinglei Cheng; Tianyi Hao 003; Kecheng Liu; Hang Ren; Zhixin Song; Ji Liu 007; Fanny Ye; Yiyu Shi 001 (CoRR, 2024)

Avoiding Copyright Infringement via Machine Unlearning. - Guangyao Dou; Zheyuan Liu 010; Qing Lyu 001; Kaize Ding; Eric Wong 001 (CoRR, 2024)

Machine Unlearning in Generative AI: A Survey. - Zheyuan Liu 010; Guangyao Dou; Zhaoxuan Tan; Yijun Tian 001; Meng Jiang 001 (CoRR, 2024)

CLIPErase: Efficient Unlearning of Visual-Textual Associations in CLIP. - Tianyu Yang; Lisen Dai; Zheyuan Liu 010; Xiangqi Wang; Meng Jiang 001; Yapeng Tian; Xiangliang Zhang 001 (CoRR, 2024)

Modality-Aware Neuron Pruning for Unlearning in Multimodal Large Language Models. - Zheyuan Liu 010; Guangyao Dou; Xiangchi Yuan; Chunhui Zhang; Zhaoxuan Tan; Meng Jiang 001 (ACL (1), 2025)

Disentangling Biased Knowledge from Reasoning in Large Language Models via Machine Unlearning. - Zheyuan Liu 010; Suraj Maharjan; Fanyou Wu; Rahil Parikh; Belhassen Bayar; Srinivasan H. Sengamedu; Meng Jiang 001 (ACL (1), 2025)

Avoiding Copyright Infringement via Large Language Model Unlearning. - Guangyao Dou; Zheyuan Liu 010; Qing Lyu 001; Kaize Ding; Eric Wong 001 (NAACL (Findings), 2025)

Protecting Privacy in Multimodal Large Language Models with MLLMU-Bench. - Zheyuan Liu 010; Guangyao Dou; Mengzhao Jia; Zhaoxuan Tan; Qingkai Zeng 001; Yongle Yuan; Meng Jiang 001 (NAACL (Long Papers), 2025)

Can Large Language Models Understand Preferences in Personalized Recommendation? - Zhaoxuan Tan; Zinan Zeng; Qingkai Zeng 001; Zhenyu Wu 004; Zheyuan Liu 010; Fengran Mo; Meng Jiang 001 (CoRR, 2025)

Towards Trustworthy Retrieval Augmented Generation for Large Language Models: A Survey. - Bo Ni; Zheyuan Liu 010; Leyao Wang; Yongjia Lei; Yuying Zhao; Xueqi Cheng; Qingkai Zeng 001; Luna Dong; Yinglong Xia; Krishnaram Kenthapadi; Ryan A. Rossi; Franck Dernoncourt; et al. (CoRR, 2025)

Superficial Self-Improved Reasoners Benefit from Model Merging. - Xiangchi Yuan; Chunhui Zhang; Zheyuan Liu 010; Dachuan Shi; Soroush Vosoughi; Wenke Lee (CoRR, 2025)

Graph Foundation Models: A Comprehensive Survey. - Zehong Wang; Zheyuan Liu 010; Tianyi Ma; Jiazheng Li 012; Zheyuan Zhang; Xingbo Fu; Yiyang Li; Zhengqing Yuan; Wei Song; Yijun Ma; Qingkai Zeng 001; Xiusi Chen; et al. (CoRR, 2025)

Incorporating Rather Than Eliminating: Achieving Fairness for Skin Disease Diagnosis Through Group-Specific Expert. - Gelei Xu; Yuying Duan; Zheyuan Liu 010; Xueyang Li; Meng Jiang 001; Michael Lemmon 001; Wei Jin 009; Yiyu Shi 001 (CoRR, 2025)