Publications
Preprints
* represents equal contribution
- Yuankai Luo, Hongkang Li, Lei Shi, Xiao-Ming Wu. Enhancing Graph Transformers with Hierarchical Distance Structural Encoding.
- Yihua Zhang*, Hongkang Li*, Yuguang Yao*, Aochuan Chen, Shuai Zhang, Pin-Yu Chen, Meng Wang, Sijia Liu. “Visual Prompting Reimagined: The Power of Activation Prompts.”
- Jiawei Sun, Hongkang Li, Meng Wang. “How do skip connections affect Graph Convolutional networks with graph sampling? A theoretical analysis on generalization.”
Conference Papers
* represents equal contribution
- Hongkang Li, Meng Wang, Songtao Lu, Xiaodong Cui, Pin-Yu Chen. How Do Nonlinear Transformers Learn and Generalize in In-Context Learning?, ICML 2024.
- Hongkang Li, Meng Wang, Tengfei Ma, Sijia Liu, Zaixi Zhang, Pin-Yu Chen. What Improves the Generalization of Graph Transformers? A Theoretical Dive into the Self-attention and Positional Encoding, ICML 2024.
Hui Wan, Hongkang Li, Songtao Lu, Xiaodong Cui, Marina Danilevsky. How Can Personalized Context Help? Exploring Joint Retrieval of Passage and Personalized Context, ICASSP 2024.
- Shuai Zhang, Hongkang Li, Meng Wang, Miao Liu, Pin-Yu Chen, Songtao Lu, Sijia Liu, Keerthiram Murugesan, Subhajit Chaudhury. On the Convergence and Sample Complexity Analysis of Deep Q-Networks with -Greedy Exploration, Neurips 2023.
Hongkang Li, Meng Wang, Sijia Liu, Pin-Yu Chen. A Theoretical Understanding of shallow Vision Transformers: Learning, Generalization, and Sample Complexity, ICLR 2023. Poster
- Hongkang Li, Meng Wang, Sijia Liu, Pin-Yu Chen, Jinjun Xiong. Generalization Guarantee of Training Graph Convolutional Networks with Graph Topology Sampling, ICML 2022. Poster
- Hongkang Li, Shuai Zhang, Meng Wang. Learning and generalization of one-hidden-layer neural networks, going beyond standard gaussian data, CISS 2022. Poster
Journal Papers
* represents equal contribution
- Hongkang Li, Shuai Zhang, Yihua Zhang, Meng Wang, Sijia Liu, Pin-Yu Chen. How does promoting the minority fraction affect generalization? A theoretical study of the one-hidden-layer neural network on group imbalance, IEEE Journal of Selected Topics in Signal Processing (JSTSP), Special Series on AI in Signal & Data Science - Toward Explainable, Reliable, and Sustainable Machine Learning, March 2024.
Workshop Papers
* represents equal contribution
Hongkang Li, Meng Wang, Songtao Lu, Xiaodong Cui, Pin-Yu Chen. How Do Nonlinear Transformers Acquire Generalization-Guaranteed CoT Ability?, High-dimensional Learning Dynamics Workshop: The Emergence of Structure and Reasoning (HiLD) and Workshop on Theoretical Foundations of Foundation Models (TF2M) at ICML 2024.
Hongkang Li, Meng Wang, Shuai Zhang, Sijia Liu, Pin-Yu Chen. “Learning on Transformers is Provable Low-Rank and Sparse: A One-layer Analysis”, (SAM 2024) 2024 IEEE 13th Sensor Array and Multichannel Signal Processing Workshop.
Hongkang Li, Meng Wang, Songtao Lu, Hui Wan, Xiaodong Cui, Pin-Yu Chen. Transformers as Multi-Task Feature Selectors: Generalization Analysis of In-Context Learning, (M3L 2023) Mathematics of Modern Machine Learning Workshop at NeurIPS 2023.