1 citations · 2 across the 5 of their papers we have counts for
5 papers
How Can Mamba Learn In Context with Outliers and Generalize Provably?
Hongkang Li, Songtao Lu, Xiaodong Cui +2
The Mamba model has gained significant attention for its computational advantages over Transformer-based models, while achieving comparable performance across a wide range of langu…
Training Nonlinear Transformers for Chain-of-Thought Inference: A Theoretical Generalization Analysis
Hongkang Li, Songtao Lu, Pin-Yu Chen +2
Chain-of-Thought (CoT) is an efficient prompting method that enables the reasoning ability of large language models by augmenting the query using multiple examples with multiple in…
SF-DQN: Provable Knowledge Transfer using Successor Feature for Deep Reinforcement Learning
Shuai Zhang, Heshan Devaka Fernando, Miao Liu +5
This paper studies the transfer reinforcement learning (RL) problem where multiple RL problems have different reward functions but share the same underlying transition dynamics. In…
How Do Nonlinear Transformers Learn and Generalize in In-Context Learning?
Hongkang Li, Meng Wang, Songtao Lu +2
Transformer-based large language models have displayed impressive in-context learning capabilities, where a pre-trained model can handle new tasks without fine-tuning by simply aug…
On the Convergence and Sample Complexity Analysis of Deep Q-Networks with -Greedy Exploration
Shuai Zhang, Hongkang Li, Meng Wang +6
This paper provides a theoretical understanding of Deep Q-Network (DQN) with the -greedy exploration in deep reinforcement learning. Despite the tremendous empirical a…