6 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…
Revisiting Differentially Private Hyper-parameter Tuning
Zihang Xiang, Tianhao Wang, Chenglong Wang +1
We study the application of differential privacy in hyper-parameter tuning, a crucial process in machine learning involving selecting the best hyper-parameter from several candidat…
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…
Visual Agents as Fast and Slow Thinkers
Guangyan Sun, Mingyu Jin, Zhenting Wang +7
Achieving human-level intelligence requires refining cognitive distinctions between System 1 and System 2 thinking. While contemporary AI, driven by large language models, demonstr…
Editable Concept Bottleneck Models
Lijie Hu, Chenyang Ren, Zhengyu Hu +5
Concept Bottleneck Models (CBMs) have garnered much attention for their ability to elucidate the prediction process through a humanunderstandable concept layer. However, most previ…
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…