Hongkang Li - RankMe

29 papers A* 9Misc 2Journal 17Unranked 1

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	A Theoretical Analysis of Mamba's Training Dynamics: Filtering Relevant Features for Generalization in State Space Models. CoRR Mugunthan Shandirasegaran, Hongkang Li, Songyang Zhang, Meng Wang, Shuai Zhang
2025	J	`jnl`	2025 IEEE Signal Processing Society Awards [Society News]. IEEE Signal Process. Mag. Michaël Unser, Dong Yu, Barry D. Van Veen, K. V. S. Hari, John G. Apostolopoulos, Daniel P. W. Ellis, Santiago Segarra, Katherine L. Bouman, Hongkang Li, Nicholas Chimitt, Burak Varici
2025	J	`jnl`	Can Mamba Learn In Context with Outliers? A Theoretical Generalization Analysis. CoRR Hongkang Li, Songtao Lu, Xiaodong Cui, Pin-Yu Chen, Meng Wang
2025	J	`jnl`	Merging Smarter, Generalizing Better: Enhancing Model Merging on OOD Data. CoRR Bingjie Zhang, Hongkang Li, Changlong Shi, Guowei Rong, He Zhao, Dongsheng Wang, Dandan Guo, Meng Wang
2025	A*	`conf`	Node Identifiers: Compact, Discrete Representations for Efficient Graph Learning. ICLR Yuankai Luo, Hongkang Li, Qijiong Liu, Lei Shi, Xiao-Ming Wu
2025	J	`jnl`	Theoretical Learning Performance of Graph Networks: the Impact of Jumping Connections and Layer-wise Sparsification. Trans. Mach. Learn. Res. Jiawei Sun, Hongkang Li, Meng Wang
2025	J	`jnl`	Theoretical Learning Performance of Graph Neural Networks: The Impact of Jumping Connections and Layer-wise Sparsification. CoRR Jiawei Sun, Hongkang Li, Meng Wang
2025	A*	`conf`	Training Nonlinear Transformers for Chain-of-Thought Inference: A Theoretical Generalization Analysis. ICLR Hongkang Li, Songtao Lu, Pin-Yu Chen, Xiaodong Cui, Meng Wang
2025	A*	`conf`	When is Task Vector Provably Effective for Model Editing? A Generalization Analysis of Nonlinear Transformers. ICLR Hongkang Li, Yihua Zhang, Shuai Zhang, Pin-Yu Chen, Sijia Liu, Meng Wang
2025	J	`jnl`	When is Task Vector Provably Effective for Model Editing? A Generalization Analysis of Nonlinear Transformers. CoRR Hongkang Li, Yihua Zhang, Shuai Zhang, Meng Wang, Sijia Liu, Pin-Yu Chen
2024	A*	`conf`	Enhancing Graph Transformers with Hierarchical Distance Structural Encoding. NeurIPS Yuankai Luo, Hongkang Li, Lei Shi, Xiao-Ming Wu
2024	Misc	`conf`	How Can Personalized Context Help? Exploring Joint Retrieval of Passage and Personalized Context. ICASSP Hui Wan, Hongkang Li, Songtao Lu, Xiaodong Cui, Marina Danilevsky
2024	A*	`conf`	How Do Nonlinear Transformers Learn and Generalize in In-Context Learning? ICML Hongkang Li, Meng Wang, Songtao Lu, Xiaodong Cui, Pin-Yu Chen
2024	J	`jnl`	How does promoting the minority fraction affect generalization? A theoretical study of the one-hidden-layer neural network on group imbalance. CoRR Hongkang Li, Shuai Zhang, Yihua Zhang, Meng Wang, Sijia Liu, Pin-Yu Chen
2024	—	`conf`	Learning on Transformers is Provable Low-Rank and Sparse: A One-layer Analysis. SAM Hongkang Li, Meng Wang, Shuai Zhang, Sijia Liu, Pin-Yu Chen
2024	J	`jnl`	Learning on Transformers is Provable Low-Rank and Sparse: A One-layer Analysis. CoRR Hongkang Li, Meng Wang, Shuai Zhang, Sijia Liu, Pin-Yu Chen
2024	J	`jnl`	Training Nonlinear Transformers for Chain-of-Thought Inference: A Theoretical Generalization Analysis. CoRR Hongkang Li, Meng Wang, Songtao Lu, Xiaodong Cui, Pin-Yu Chen
2024	J	`jnl`	Training Nonlinear Transformers for Efficient In-Context Learning: A Theoretical Learning and Generalization Analysis. CoRR Hongkang Li, Meng Wang, Songtao Lu, Xiaodong Cui, Pin-Yu Chen
2024	A*	`conf`	What Improves the Generalization of Graph Transformers? A Theoretical Dive into the Self-attention and Positional Encoding. ICML Hongkang Li, Meng Wang, Tengfei Ma, Sijia Liu, Zaixi Zhang, Pin-Yu Chen
2024	J	`jnl`	What Improves the Generalization of Graph Transformers? A Theoretical Dive into the Self-attention and Positional Encoding. CoRR Hongkang Li, Meng Wang, Tengfei Ma, Sijia Liu, Zaixi Zhang, Pin-Yu Chen
2023	A*	`conf`	A Theoretical Understanding of Shallow Vision Transformers: Learning, Generalization, and Sample Complexity. ICLR Hongkang Li, Meng Wang, Sijia Liu, Pin-Yu Chen
2023	J	`jnl`	A Theoretical Understanding of shallow Vision Transformers: Learning, Generalization, and Sample Complexity. CoRR Hongkang Li, Meng Wang, Sijia Liu, Pin-Yu Chen
2023	J	`jnl`	How Can Context Help? Exploring Joint Retrieval of Passage and Personalized Context. CoRR Hui Wan, Hongkang Li, Songtao Lu, Xiaodong Cui, Marina Danilevsky
2023	A*	`conf`	On the Convergence and Sample Complexity Analysis of Deep Q-Networks with ε-Greedy Exploration. NeurIPS Shuai Zhang, Hongkang Li, Meng Wang, Miao Liu, Pin-Yu Chen, Songtao Lu, Sijia Liu, Keerthiram Murugesan, Subhajit Chaudhury
2023	J	`jnl`	On the Convergence and Sample Complexity Analysis of Deep Q-Networks with ε-Greedy Exploration. CoRR Shuai Zhang, Hongkang Li, Meng Wang, Miao Liu, Pin-Yu Chen, Songtao Lu, Sijia Liu, Keerthiram Murugesan, Subhajit Chaudhury
2022	A*	`conf`	Generalization Guarantee of Training Graph Convolutional Networks with Graph Topology Sampling. ICML Hongkang Li, Meng Wang, Sijia Liu, Pin-Yu Chen, Jinjun Xiong
2022	J	`jnl`	Generalization Guarantee of Training Graph Convolutional Networks with Graph Topology Sampling. CoRR Hongkang Li, Meng Wang, Sijia Liu, Pin-Yu Chen, Jinjun Xiong
2022	Misc	`conf`	Learning and generalization of one-hidden-layer neural networks, going beyond standard Gaussian data. CISS Hongkang Li, Shuai Zhang, Meng Wang
2022	J	`jnl`	Learning and generalization of one-hidden-layer neural networks, going beyond standard Gaussian data. CoRR Hongkang Li, Shuai Zhang, Meng Wang