62 citations · 153 across the 21 of their papers we have counts for
4 papers · 1 filter
Effective and Efficient Federated Tree Learning on Hybrid Data
Qinbin Li, Chulin Xie, Xiaojun Xu +5
Federated learning has emerged as a promising distributed learning paradigm that facilitates collaborative learning among multiple parties without transferring raw data. However, m…
Ring-A-Bell! How Reliable are Concept Removal Methods for Diffusion Models?
Yu-Lin Tsai, Chia-Yi Hsu, Chulin Xie +6
Diffusion models for text-to-image (T2I) synthesis, such as Stable Diffusion (SD), have recently demonstrated exceptional capabilities for generating high-quality content. However,…
DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Models
Boxin Wang, Weixin Chen, Hengzhi Pei +16
Generative Pre-trained Transformer (GPT) models have exhibited exciting progress in their capabilities, capturing the interest of practitioners and the public alike. Yet, while the…
PerAda: Parameter-Efficient Federated Learning Personalization with Generalization Guarantees
Chulin Xie, De-An Huang, Wenda Chu +4
Personalized Federated Learning (pFL) has emerged as a promising solution to tackle data heterogeneity across clients in FL. However, existing pFL methods either (1) introduce high…