1 citations · 3 across the 3 of their papers we have counts for
3 papers
cs.LG2025★ 1 cited
Concept-Based Unsupervised Domain Adaptation
Xinyue Xu, Yueying Hu, Hui Tang +4
Concept Bottleneck Models (CBMs) enhance interpretability by explaining predictions through human-understandable concepts but typically assume that training and test data share the…
cs.CV2024★ 1 cited
Energy-Based Concept Bottleneck Models: Unifying Prediction, Concept Intervention, and Probabilistic Interpretations
Xinyue Xu, Yi Qin, Lu Mi +2
Existing methods, such as concept bottleneck models (CBMs), have been successful in providing concept-based interpretations for black-box deep learning models. They typically work…
cs.CL2023★ 1 cited
LatticeGen: A Cooperative Framework which Hides Generated Text in a Lattice for Privacy-Aware Generation on Cloud
Mengke Zhang, Tianxing He, Tianle Wang +5
In the current user-server interaction paradigm of prompted generation with large language models (LLM) on cloud, the server fully controls the generation process, which leaves zer…