1 citations · 1 across the 4 of their papers we have counts for
4 papers
Controllable Concept Bottleneck Models
Hongbin Lin, Chenyang Ren, Juangui Xu +7
Concept Bottleneck Models (CBMs) have garnered much attention for their ability to elucidate the prediction process through a human-understandable concept layer. However, most prev…
Towards Lifecycle Unlearning Commitment Management: Measuring Sample-level Unlearning Completeness
Cheng-Long Wang, Qi Li, Zihang Xiang +2
Growing concerns over data privacy and security highlight the importance of machine unlearning--removing specific data influences from trained models without full retraining. Techn…
Data Lineage Inference: Uncovering Privacy Vulnerabilities of Dataset Pruning
Qi Li, Cheng-Long Wang, Yinzhi Cao +1
In this work, we systematically explore the data privacy issues of dataset pruning in machine learning systems. Our findings reveal, for the first time, that even if data in the re…
Towards Lifecycle Unlearning Commitment Management: Measuring Sample-level Approximate Unlearning Completeness
Cheng-Long Wang, Qi Li, Zihang Xiang +2
By adopting a more flexible definition of unlearning and adjusting the model distribution to simulate training without the targeted data, approximate machine unlearning provides a…