60 citations · 72 across the 13 of their papers we have counts for
13 papers
Learning on Transformers is Provable Low-Rank and Sparse: A One-layer Analysis
Hongkang Li, Meng Wang, Shuai Zhang +2
Efficient training and inference algorithms, such as low-rank adaption and model pruning, have shown impressive performance for learning Transformer-based large foundation models.…
Hide and Seek: How Does Watermarking Impact Face Recognition?
Yuguang Yao, Steven Grosz, Sijia Liu +1
The recent progress in generative models has revolutionized the synthesis of highly realistic images, including face images. This technological development has undoubtedly helped f…
Advancing the Robustness of Large Language Models through Self-Denoised Smoothing
Jiabao Ji, Bairu Hou, Zhen Zhang +7
Although large language models (LLMs) have achieved significant success, their vulnerability to adversarial perturbations, including recent jailbreak attacks, has raised considerab…
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 +3
Group imbalance has been a known problem in empirical risk minimization (ERM), where the achieved high average accuracy is accompanied by low accuracy in a minority group. Despite…
Selectivity Drives Productivity: Efficient Dataset Pruning for Enhanced Transfer Learning
Yihua Zhang, Yimeng Zhang, Aochuan Chen +6
Massive data is often considered essential for deep learning applications, but it also incurs significant computational and infrastructural costs. Therefore, dataset pruning (DP) h…
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…