| 2025 | ICLR | Training Nonlinear Transformers for Chain-of-Thought Inference: A Theoretical Generalization Analysis. | Hongkang Li, Songtao Lu, Pin-Yu Chen, Xiaodong Cui, Meng Wang |
| 2025 | ICLR | When is Task Vector Provably Effective for Model Editing? A Generalization Analysis of Nonlinear Transformers. | Hongkang Li, Yihua Zhang, Shuai Zhang, Pin-Yu Chen, Sijia Liu, Meng Wang |
| 2025 | ICLR | Node Identifiers: Compact, Discrete Representations for Efficient Graph Learning. | Yuankai Luo, Hongkang Li, Qijiong Liu, Lei Shi, Xiao-Ming Wu |
| 2024 | ICASSP | How Can Personalized Context Help? Exploring Joint Retrieval of Passage and Personalized Context. | Hui Wan, Hongkang Li, Songtao Lu, Xiaodong Cui, Marina Danilevsky |
| 2024 | ICML | How Do Nonlinear Transformers Learn and Generalize in In-Context Learning? | Hongkang Li, Meng Wang, Songtao Lu, Xiaodong Cui, Pin-Yu Chen |
| 2024 | ICML | What Improves the Generalization of Graph Transformers? A Theoretical Dive into the Self-attention and Positional Encoding. | Hongkang Li, Meng Wang, Tengfei Ma, Sijia Liu, Zaixi Zhang, Pin-Yu Chen |
| 2023 | ICLR | A Theoretical Understanding of Shallow Vision Transformers: Learning, Generalization, and Sample Complexity. | Hongkang Li, Meng Wang, Sijia Liu, Pin-Yu Chen |
| 2022 | CISS | Learning and generalization of one-hidden-layer neural networks, going beyond standard Gaussian data. | Hongkang Li, Shuai Zhang, Meng Wang |
| 2022 | ICML | Generalization Guarantee of Training Graph Convolutional Networks with Graph Topology Sampling. | Hongkang Li, Meng Wang, Sijia Liu, Pin-Yu Chen, Jinjun Xiong |