17 citations · 26 across the 14 of their papers we have counts for
9 papers · 1 filter
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…
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…
Generalizability of Memorization Neural Networks
Lijia Yu, Xiao-Shan Gao, Lijun Zhang +1
The neural network memorization problem is to study the expressive power of neural networks to interpolate a finite dataset. Although memorization is widely believed to have a clos…
Generalization Bound and New Algorithm for Clean-Label Backdoor Attack
Lijia Yu, Shuang Liu, Yibo Miao +2
The generalization bound is a crucial theoretical tool for assessing the generalizability of learning methods and there exist vast literatures on generalizability of normal learnin…
Efficient Black-box Adversarial Attacks via Bayesian Optimization Guided by a Function Prior
Shuyu Cheng, Yibo Miao, Yinpeng Dong +3
This paper studies the challenging black-box adversarial attack that aims to generate adversarial examples against a black-box model by only using output feedback of the model to i…
Efficient Detection of LLM-generated Texts with a Bayesian Surrogate Model
Yibo Miao, Hongcheng Gao, Hao Zhang +1
The detection of machine-generated text, especially from large language models (LLMs), is crucial in preventing serious social problems resulting from their misuse. Some methods tr…