Learning and generalization of one-hidden-layer neural networks, going beyond standard Gaussian data. - Hongkang Li; Shuai Zhang 015; Meng Wang 003 (CISS, 2022)

Generalization Guarantee of Training Graph Convolutional Networks with Graph Topology Sampling. - Hongkang Li; Meng Wang 003; Sijia Liu 001; Pin-Yu Chen; Jinjun Xiong (ICML, 2022)

A Theoretical Understanding of Shallow Vision Transformers: Learning, Generalization, and Sample Complexity. - Hongkang Li; Meng Wang 003; Sijia Liu 001; Pin-Yu Chen (ICLR, 2023)

On the Convergence and Sample Complexity Analysis of Deep Q-Networks with ε-Greedy Exploration. - Shuai Zhang 015; Hongkang Li; Meng Wang 003; Miao Liu 001; Pin-Yu Chen; Songtao Lu; Sijia Liu 001; Keerthiram Murugesan; Subhajit Chaudhury (NeurIPS, 2023)

How Can Context Help? Exploring Joint Retrieval of Passage and Personalized Context. - Hui Wan; Hongkang Li; Songtao Lu; Xiaodong Cui; Marina Danilevsky (CoRR, 2023)

How Can Personalized Context Help? Exploring Joint Retrieval of Passage and Personalized Context. - Hui Wan; Hongkang Li; Songtao Lu; Xiaodong Cui; Marina Danilevsky (ICASSP, 2024)

How Do Nonlinear Transformers Learn and Generalize in In-Context Learning? - Hongkang Li; Meng Wang 003; Songtao Lu; Xiaodong Cui; Pin-Yu Chen (ICML, 2024)

What Improves the Generalization of Graph Transformers? A Theoretical Dive into the Self-attention and Positional Encoding. - Hongkang Li; Meng Wang 003; Tengfei Ma 001; Sijia Liu 001; Zaixi Zhang; Pin-Yu Chen (ICML, 2024)

Learning on Transformers is Provable Low-Rank and Sparse: A One-layer Analysis. - Hongkang Li; Meng Wang 003; Shuai Zhang 015; Sijia Liu 001; Pin-Yu Chen (SAM, 2024)

Enhancing Graph Transformers with Hierarchical Distance Structural Encoding. - Yuankai Luo; Hongkang Li; Lei Shi 002; Xiao-Ming Wu 003 (NeurIPS, 2024)

Training Nonlinear Transformers for Efficient In-Context Learning: A Theoretical Learning and Generalization Analysis. - Hongkang Li; Meng Wang 003; Songtao Lu; Xiaodong Cui; Pin-Yu Chen (CoRR, 2024)

How does promoting the minority fraction affect generalization? A theoretical study of the one-hidden-layer neural network on group imbalance. - Hongkang Li; Shuai Zhang 015; Yihua Zhang; Meng Wang 003; Sijia Liu 001; Pin-Yu Chen (CoRR, 2024)

Training Nonlinear Transformers for Chain-of-Thought Inference: A Theoretical Generalization Analysis. - Hongkang Li; Meng Wang 003; Songtao Lu; Xiaodong Cui; Pin-Yu Chen (CoRR, 2024)

Training Nonlinear Transformers for Chain-of-Thought Inference: A Theoretical Generalization Analysis. - Hongkang Li; Songtao Lu; Pin-Yu Chen; Xiaodong Cui; Meng Wang (ICLR, 2025)

When is Task Vector Provably Effective for Model Editing? A Generalization Analysis of Nonlinear Transformers. - Hongkang Li; Yihua Zhang; Shuai Zhang 015; Pin-Yu Chen; Sijia Liu 001; Meng Wang 003 (ICLR, 2025)

Node Identifiers: Compact, Discrete Representations for Efficient Graph Learning. - Yuankai Luo; Hongkang Li; Qijiong Liu; Lei Shi 002; Xiao-Ming Wu 003 (ICLR, 2025)

Theoretical Learning Performance of Graph Networks: the Impact of Jumping Connections and Layer-wise Sparsification. - Jiawei Sun; Hongkang Li; Meng Wang (Trans. Mach. Learn. Res., 2025)

Merging Smarter, Generalizing Better: Enhancing Model Merging on OOD Data. - Bingjie Zhang 009; Hongkang Li; Changlong Shi; Guowei Rong; He Zhao 001; Dongsheng Wang 003; Dandan Guo; Meng Wang (CoRR, 2025)

Theoretical Learning Performance of Graph Neural Networks: The Impact of Jumping Connections and Layer-wise Sparsification. - Jiawei Sun; Hongkang Li; Meng Wang (CoRR, 2025)