A Survey about application based on RF signals. - Jiaren Xiao (CoRR, 2022)

Co-evolving Vector Quantization for ID-based Recommendation. - Qijiong Liu; Jiaren Xiao; Lu Fan; Jieming Zhu; Xiao-Ming Wu 003 (CoRR, 2023)

Adversarially regularized graph attention networks for inductive learning on partially labeled graphs. - Jiaren Xiao; Quanyu Dai; Xiaochen Xie; James Lam; Ka-Wai Kwok (Knowl. Based Syst., 2023)

Domain Adaptive Graph Infomax via Conditional Adversarial Networks. - Jiaren Xiao; Quanyu Dai; Xiaochen Xie; Qi Dou 001; Ka-Wai Kwok; James Lam (IEEE Trans. Netw. Sci. Eng., 2023)

Graph Transfer Learning via Adversarial Domain Adaptation With Graph Convolution. - Quanyu Dai; Xiao-Ming Wu 003; Jiaren Xiao; Xiao Shen 001; Dan Wang 002 (IEEE Trans. Knowl. Data Eng., 2023)

Automatic Multiparametric Magnetic Resonance Imaging-Based Prostate Lesions Assessment with Unsupervised Domain Adaptation. - Jing Dai; Xiaomei Wang; Yingqi Li; Zhiyu Liu; Yui-Lun Ng; Jiaren Xiao; Joe King Man Fan; James Lam; Qi Dou 001; Varut Vardhanabhuti; Ka-Wai Kwok (Adv. Intell. Syst., 2023)

Learning Category Trees for ID-Based Recommendation: Exploring the Power of Differentiable Vector Quantization. - Qijiong Liu; Jiaren Xiao; Lu Fan; Jieming Zhu; Xiao-Ming Wu 003 (WWW, 2024)

Vector Quantization for Recommender Systems: A Review and Outlook. - Qijiong Liu; Xiaoyu Dong; Jiaren Xiao; Nuo Chen 004; Hengchang Hu; Jieming Zhu; Chenxu Zhu; Tetsuya Sakai; Xiao-Ming Wu 003 (CoRR, 2024)

Semi-supervised domain adaptation on graphs with contrastive learning and minimax entropy. - Jiaren Xiao; Quanyu Dai; Xiao Shen 001; Xiaochen Xie; Jing Dai; James Lam; Ka-Wai Kwok (Neurocomputing, 2024)

A survey on application in RF signal. - Jiaren Xiao; Bing Luo; Li Xu; Bo Li; Zhiguo Chen (Multim. Tools Appl., 2024)

MCNet: Monotonic Calibration Networks for Expressive Uncertainty Calibration in Online Advertising. - Quanyu Dai; Jiaren Xiao; Zhaocheng Du; Jieming Zhu; Chengxiao Luo; Xiao-Ming Wu 003; Zhenhua Dong (WWW, 2025)