| Interpretable and Generalizable Graph Learning via Stochastic Attention Mechanism S Miao, M Liu, P Li ICML 2022 (Spotlight), 2022 | 210 | 2022 |
| Interpretable Geometric Deep Learning via Learnable Randomness Injection S Miao, Y Luo, M Liu, P Li ICLR 2023, 2023 | 26 | 2023 |
| High Pileup Particle Tracking with Object Condensation K Lieret, G DeZoort, D Chatterjee, J Park, S Miao, P Li CTD 2023, 2023 | 9 | 2023 |
| What Can We Learn from State Space Models for Machine Learning on Graphs? Y Huang*, S Miao*, P Li arXiv preprint (*Equal contribution, listed in alphabetical order), 2024 | 6 | 2024 |
| Locality-Sensitive Hashing-Based Efficient Point Transformer with Applications in High-Energy Physics S Miao, Z Lu, M Liu, J Duarte, P Li ICML 2024 (Oral), 2024 | 3 | 2024 |
| Simple is Effective: The Roles of Graphs and Large Language Models in Knowledge-Graph-Based Retrieval-Augmented Generation M Li*, S Miao*, P Li arXiv preprint arXiv:2410.20724 (*Equal contribution), 2024 | 2 | 2024 |
| Towards Understanding Sensitive and Decisive Patterns in Explainable AI: A Case Study of Model Interpretation in Geometric Deep Learning J Zhu, S Miao, R Ying, P Li Nature Machine Intelligence, 2024 | 1 | 2024 |
| GeSS: Benchmarking Geometric Deep Learning under Scientific Applications with Distribution Shifts D Zou*, S Liu*, S Miao, V Fung, S Chang, P Li NeurIPS 2024 D&B Track (*Equal contribution), 2024 | | 2024 |
