| Towards Safer Large Language Models through Machine Unlearning Z Liu, G Dou, Z Tan, Y Tian, M Jiang ACL Findings, 2024 | 72 | 2024 |
| Democratizing large language models via personalized parameter-efficient fine-tuning Z Tan, Q Zeng, Y Tian, Z Liu, B Yin, M Jiang arXiv preprint arXiv:2402.04401, 2024 | 34 | 2024 |
| Fair Graph Representation Learning via Diverse Mixture of Experts Z Liu*, C Zhang*, Y Tian, E Zhang, C Huang, Y Ye, C Zhang The Web Conference (WWW) 2023, 2023 | 29 | 2023 |
| Can we soft prompt LLMs for graph learning tasks? Z Liu*, X He*, Y Tian*, NV Chawla Companion Proceedings of the ACM on Web Conference 2024, 481-484, 2024 | 26 | 2024 |
| Machine unlearning in generative ai: A survey Z Liu, G Dou, Z Tan, Y Tian, M Jiang arXiv preprint arXiv:2407.20516, 2024 | 21 | 2024 |
| Breaking the trilemma of privacy, utility, efficiency via controllable machine unlearning Z Liu*, G Dou*, Y Tian, C Zhang, E Chien, Z Zhu The Web Conference (WWW) 2024, 2023 | 21 | 2023 |
| Chasing all-round graph representation robustness: Model, training, and optimization C Zhang, Y Tian, M Ju, Z Liu, Y Ye, N Chawla, C Zhang The Eleventh International Conference on Learning Representations (ICLR) 2023, 2022 | 21 | 2022 |
| Avoiding copyright infringement via machine unlearning G Dou, Z Liu, Q Lyu, K Ding, E Wong arXiv e-prints, arXiv: 2406.10952, 2024 | 18 | 2024 |
| Graphbert: Bridging graph and text for malicious behavior detection on social media J Wu, C Zhang, Z Liu, E Zhang, S Wilson, C Zhang 2022 IEEE International Conference on Data Mining (ICDM), 548-557, 2022 | 16 | 2022 |
| Personalized pieces: Efficient personalized large language models through collaborative efforts Z Tan, Z Liu, M Jiang arXiv preprint arXiv:2406.10471, 2024 | 12 | 2024 |
| Graph learning for parameter prediction of quantum approximate optimization algorithm Z Liang, G Liu, Z Liu, J Cheng, T Hao, K Liu, H Ren, Z Song, J Liu, F Ye, ... Proceedings of the 61st ACM/IEEE Design Automation Conference, 1-4, 2024 | 6 | 2024 |
| Ugmae: A unified framework for graph masked autoencoders Y Tian, C Zhang, Z Kou, Z Liu, X Zhang, NV Chawla arXiv preprint arXiv:2402.08023, 2024 | 6 | 2024 |
| Protecting privacy in multimodal large language models with mllmu-bench Z Liu, G Dou, M Jia, Z Tan, Q Zeng, Y Yuan, M Jiang arXiv preprint arXiv:2410.22108, 2024 | 5 | 2024 |
| CLIPErase: Efficient Unlearning of Visual-Textual Associations in CLIP T Yang, L Dai, Z Liu, X Wang, M Jiang, Y Tian, X Zhang arXiv preprint arXiv:2410.23330, 2024 | 4 | 2024 |
| Towards Trustworthy Retrieval Augmented Generation for Large Language Models: A Survey B Ni, Z Liu, L Wang, Y Lei, Y Zhao, X Cheng, Q Zeng, L Dong, Y Xia, ... arXiv preprint arXiv:2502.06872, 2025 | 2 | 2025 |
| Can Large Language Models Understand Preferences in Personalized Recommendation? Z Tan, Z Zeng, Q Zeng, Z Wu, Z Liu, F Mo, M Jiang arXiv preprint arXiv:2501.13391, 2025 | 1 | 2025 |
| Superficial Self-Improved Reasoners Benefit from Model Merging X Yuan, C Zhang, Z Liu, D Shi, S Vosoughi, W Lee arXiv preprint arXiv:2503.02103, 2025 | | 2025 |
| Modality-Aware Neuron Pruning for Unlearning in Multimodal Large Language Models Z Liu, G Dou, X Yuan, C Zhang, Z Tan, M Jiang arXiv preprint arXiv:2502.15910, 2025 | | 2025 |
| State-level COVID-19 Trend Forecasting Using Mobility and Policy Data Y Wang, H Peng, L Sha, Z Liu, P Hong medRxiv, 2021.01. 04.21249218, 2021 | | 2021 |
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