4 citations · 11 across the 11 of their papers we have counts for
11 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…
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…
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…
MoRAL: MoE Augmented LoRA for LLMs' Lifelong Learning
Shu Yang, Muhammad Asif Ali, Cheng-Long Wang +2
Adapting large language models (LLMs) to new domains/tasks and enabling them to be efficient lifelong learners is a pivotal challenge. In this paper, we propose MoRAL, i.e., Mixtur…
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…
Communication Efficient and Provable Federated Unlearning
Youming Tao, Cheng-Long Wang, Miao Pan +3
We study federated unlearning, a novel problem to eliminate the impact of specific clients or data points on the global model learned via federated learning (FL). This problem is d…
Differentially Private Non-convex Learning for Multi-layer Neural Networks
Hanpu Shen, Cheng-Long Wang, Zihang Xiang +2
This paper focuses on the problem of Differentially Private Stochastic Optimization for (multi-layer) fully connected neural networks with a single output node. In the first part,…
Inductive Graph Unlearning
Cheng-Long Wang, Mengdi Huai, Di Wang
As a way to implement the "right to be forgotten" in machine learning, \textit{machine unlearning} aims to completely remove the contributions and information of the samples to be…