6 papers · 1 filter
BridgePure: Limited Protection Leakage Can Break Black-Box Data Protection
Yihan Wang, Yiwei Lu, Xiao-Shan Gao +2
Availability attacks, or unlearnable examples, are defensive techniques that allow data owners to modify their datasets in ways that prevent unauthorized machine learning models fr…
Evaluating Zero-Shot Long-Context LLM Compression
Chenyu Wang, Yihan Wang, Kai Li
This study evaluates the effectiveness of zero-shot compression techniques on large language models (LLMs) under long-context. We identify the tendency for computational errors to…
MUC: Machine Unlearning for Contrastive Learning with Black-box Evaluation
Yihan Wang, Yiwei Lu, Guojun Zhang +4
Machine unlearning offers effective solutions for revoking the influence of specific training data on pre-trained model parameters. While existing approaches address unlearning for…
Efficient Availability Attacks against Supervised and Contrastive Learning Simultaneously
Yihan Wang, Yifan Zhu, Xiao-Shan Gao
Availability attacks can prevent the unauthorized use of private data and commercial datasets by generating imperceptible noise and making unlearnable examples before release. Idea…
Game-Theoretic Unlearnable Example Generator
Shuang Liu, Yihan Wang, Xiao-Shan Gao
Unlearnable example attacks are data poisoning attacks aiming to degrade the clean test accuracy of deep learning by adding imperceptible perturbations to the training samples, whi…
Data-Dependent Stability Analysis of Adversarial Training
Yihan Wang, Shuang Liu, Xiao-Shan Gao
Stability analysis is an essential aspect of studying the generalization ability of deep learning, as it involves deriving generalization bounds for stochastic gradient descent-bas…