8 papers
Why Do Unlearnable Examples Work: A Novel Perspective of Mutual Information
Yifan Zhu, Yibo Miao, Yinpeng Dong +1
The volume of freely scraped data on the Internet has driven the tremendous success of deep learning. Along with this comes the growing concern about data privacy and security. Num…
Red-Teaming Text-to-Image Systems by Rule-based Preference Modeling
Yichuan Cao, Yibo Miao, Xiao-Shan Gao +1
Text-to-image (T2I) models raise ethical and safety concerns due to their potential to generate inappropriate or harmful images. Evaluating these models' security through red-teami…
Provable Robust Overfitting Mitigation in Wasserstein Distributionally Robust Optimization
Shuang Liu, Yihan Wang, Yifan Zhu +2
Wasserstein distributionally robust optimization (WDRO) optimizes against worst-case distributional shifts within a specified uncertainty set, leading to enhanced generalization on…
Generalizability of Neural Networks Minimizing Empirical Risk Based on Expressive Ability
Lijia Yu, Yibo Miao, Yifan Zhu +2
The primary objective of learning methods is generalization. Classic uniform generalization bounds, which rely on VC-dimension or Rademacher complexity, fail to explain the signifi…
3D-Properties: Identifying Challenges in DPO and Charting a Path Forward
Yuzi Yan, Yibo Miao, Jialian Li +4
Aligning large language models (LLMs) with human preferences has gained significant attention, with Proximal Policy Optimization (PPO) as a standard yet computationally expensive m…
PowerMLP: An Efficient Version of KAN
Ruichen Qiu, Yibo Miao, Shiwen Wang +3
The Kolmogorov-Arnold Network (KAN) is a new network architecture known for its high accuracy in several tasks such as function fitting and PDE solving. The superior expressive cap…