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2020 – today
- 2024
[c8]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: 9991-9995- [c7]Hongkang Li, Meng Wang, Songtao Lu, Xiaodong Cui, Pin-Yu Chen:
How Do Nonlinear Transformers Learn and Generalize in In-Context Learning? ICML 2024 - [c6]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 - [c5]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: 1-5 - [i10]Hongkang Li, Meng Wang, Songtao Lu, Xiaodong Cui, Pin-Yu Chen:
Training Nonlinear Transformers for Efficient In-Context Learning: A Theoretical Learning and Generalization Analysis. CoRR abs/2402.15607 (2024) - [i9]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. CoRR abs/2403.07310 (2024) - [i8]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. CoRR abs/2406.01977 (2024) - [i7]Hongkang Li, Meng Wang, Shuai Zhang, Sijia Liu, Pin-Yu Chen:
Learning on Transformers is Provable Low-Rank and Sparse: A One-layer Analysis. CoRR abs/2406.17167 (2024) - [i6]Hongkang Li, Meng Wang, Songtao Lu, Xiaodong Cui, Pin-Yu Chen:
Training Nonlinear Transformers for Chain-of-Thought Inference: A Theoretical Generalization Analysis. CoRR abs/2410.02167 (2024) - 2023
- [c4]Hongkang Li, Meng Wang, Sijia Liu, Pin-Yu Chen:
A Theoretical Understanding of Shallow Vision Transformers: Learning, Generalization, and Sample Complexity. ICLR 2023 - [c3]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 ε-Greedy Exploration. NeurIPS 2023 - [i5]Hongkang Li, Meng Wang, Sijia Liu, Pin-Yu Chen:
A Theoretical Understanding of shallow Vision Transformers: Learning, Generalization, and Sample Complexity. CoRR abs/2302.06015 (2023) - [i4]Hui Wan, Hongkang Li, Songtao Lu, Xiaodong Cui, Marina Danilevsky:
How Can Context Help? Exploring Joint Retrieval of Passage and Personalized Context. CoRR abs/2308.13760 (2023) - [i3]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 ε-Greedy Exploration. CoRR abs/2310.16173 (2023) - 2022
- [c2]Hongkang Li, Shuai Zhang, Meng Wang:
Learning and generalization of one-hidden-layer neural networks, going beyond standard Gaussian data. CISS 2022: 37-42 - [c1]Hongkang Li, Meng Wang, Sijia Liu, Pin-Yu Chen, Jinjun Xiong:
Generalization Guarantee of Training Graph Convolutional Networks with Graph Topology Sampling. ICML 2022: 13014-13051 - [i2]Hongkang Li, Meng Wang, Sijia Liu, Pin-Yu Chen, Jinjun Xiong
:
Generalization Guarantee of Training Graph Convolutional Networks with Graph Topology Sampling. CoRR abs/2207.03584 (2022) - [i1]Hongkang Li, Shuai Zhang, Meng Wang:
Learning and generalization of one-hidden-layer neural networks, going beyond standard Gaussian data. CoRR abs/2207.03615 (2022)
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