| 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 | 13 | 2022 |
| 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 | 9 | 2023 |
| 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 | 6 | 2022 |
| Towards Safer Large Language Models through Machine Unlearning Z Liu, G Dou, Z Tan, Y Tian, M Jiang arXiv preprint arXiv:2402.10058, 2024 | 5 | 2024 |
| Breaking the trilemma of privacy, utility, efficiency via controllable machine unlearning Z Liu*, G Dou*, Y Tian, C Zhang, E Chien, Z Zhu arXiv preprint arXiv:2310.18574, 2023 | 4 | 2023 |
| 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 | 2 | 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, ... arXiv preprint arXiv:2403.03310, 2024 | | 2024 |
| Can we soft prompt LLMs for graph learning tasks? Z Liu*, X He*, Y Tian*, NV Chawla arXiv preprint arXiv:2402.10359, 2024 | | 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 | | 2024 |
| 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 |
Yijun TianUniversity of Notre DameVerified email at nd.edu
