Hongkang Li

According to our database, Hongkang Li authored at least 19 papers between 2022 and 2025.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2025

Theoretical Learning Performance of Graph Neural Networks: The Impact of Jumping Connections and Layer-wise Sparsification.

Jiawei Sun, Hongkang Li, Meng Wang

CoRR, July, 2025

Merging Smarter, Generalizing Better: Enhancing Model Merging on OOD Data.

Bingjie Zhang, Hongkang Li, Changlong Shi, Guowei Rong, He Zhao, Dongsheng Wang, Dandan Guo, Meng Wang

CoRR, June, 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

Node Identifiers: Compact, Discrete Representations for Efficient Graph Learning.

Yuankai Luo, Hongkang Li, Qijiong Liu, Lei Shi, Xiao-Ming Wu

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

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

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

Training Nonlinear Transformers for Chain-of-Thought Inference: A Theoretical Generalization Analysis.

Hongkang Li, Songtao Lu, Pin-Yu Chen, Xiaodong Cui, Meng Wang

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

2024

Training Nonlinear Transformers for Chain-of-Thought Inference: A Theoretical Generalization Analysis.

Hongkang Li, Meng Wang, 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, Yihua Zhang, Meng Wang, Sijia Liu, Pin-Yu Chen

CoRR, 2024

Training Nonlinear Transformers for Efficient In-Context Learning: A Theoretical Learning and Generalization Analysis.

Hongkang Li, Meng Wang, Songtao Lu, Xiaodong Cui, Pin-Yu Chen

CoRR, 2024

Enhancing Graph Transformers with Hierarchical Distance Structural Encoding.

Yuankai Luo, Hongkang Li, Lei Shi, Xiao-Ming Wu

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

Learning on Transformers is Provable Low-Rank and Sparse: A One-layer Analysis.

Hongkang Li, Meng Wang, Shuai Zhang, Sijia Liu, Pin-Yu Chen

Proceedings of the 13th IEEE Sensor Array and Multichannel Signal Processing Workshop, 2024

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

Proceedings of the Forty-first International Conference on Machine Learning, 2024

How Do Nonlinear Transformers Learn and Generalize in In-Context Learning?

Hongkang Li, Meng Wang, Songtao Lu, Xiaodong Cui, Pin-Yu Chen

Proceedings of the Forty-first International Conference on Machine Learning, 2024

How Can Personalized Context Help? Exploring Joint Retrieval of Passage and Personalized Context.

Hui Wan, Hongkang Li, Songtao Lu, Xiaodong Cui, Marina Danilevsky

Proceedings of the IEEE International Conference on Acoustics, 2024

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

On the Convergence and Sample Complexity Analysis of Deep Q-Networks with ε-Greedy Exploration.

Shuai Zhang, Hongkang Li, Meng Wang, Miao Liu, Pin-Yu Chen, Songtao Lu, Sijia Liu, Keerthiram Murugesan, Subhajit Chaudhury

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

A Theoretical Understanding of Shallow Vision Transformers: Learning, Generalization, and Sample Complexity.

Hongkang Li, Meng Wang, Sijia Liu, Pin-Yu Chen

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

Generalization Guarantee of Training Graph Convolutional Networks with Graph Topology Sampling.

Hongkang Li, Meng Wang, Sijia Liu, Pin-Yu Chen, Jinjun Xiong

Proceedings of the International Conference on Machine Learning, 2022

Learning and generalization of one-hidden-layer neural networks, going beyond standard Gaussian data.

Hongkang Li, Shuai Zhang, Meng Wang

Proceedings of the 56th Annual Conference on Information Sciences and Systems, 2022