Parameter-Free Auto-Weighted Multiple Graph Learning: A Framework for Multiview Clustering and Semi-Supervised Classification. - Feiping Nie 001; Jing Li 009; Xuelong Li 001 (IJCAI, 2016)

Multi-label Learning with Label-Specific Feature Selection. - Yan Yan 025; ShiNing Li; Zhe Yang 008; Xiao Zhang 037; Jing Li 009; Anyi Wang; Jingyu Zhang (ICONIP (1), 2017)

Self-weighted Multiview Clustering with Multiple Graphs. - Feiping Nie 001; Jing Li 009; Xuelong Li 001 (IJCAI, 2017)

Convex Multiview Semi-Supervised Classification. - Feiping Nie 001; Jing Li 009; Xuelong Li 001 (IEEE Trans. Image Process., 2017)

Directly Solving the Original Ratiocut Problem for Effective Data Clustering. - Jing Li 009; Feiping Nie 001; Xuelong Li 001 (ICASSP, 2018)

Auto-Weighted Multi-View Learning for Image Clustering and Semi-Supervised Classification. - Feiping Nie 001; Guohao Cai; Jing Li 009; Xuelong Li 001 (IEEE Trans. Image Process., 2018)

Solid State Drives in modern computing systems. - Jing Li 009 (2018)

Intrinsic Weight Learning Approach for Multi-view Clustering. - Feiping Nie 001; Jing Li 009; Xuelong Li 001 (CoRR, 2019)

Fairness Constraints in Semi-supervised Learning. - Tao Zhang 055; Tianqing Zhu; Mengde Han; Jing Li 009; Wanlei Zhou 001; Philip S. Yu (CoRR, 2020)

Secure Metric Learning via Differential Pairwise Privacy. - Jing Li 009; Yuangang Pan; Yulei Sui; Ivor W. Tsang (IEEE Trans. Inf. Forensics Secur., 2020)

Fairness in Semi-Supervised Learning: Unlabeled Data Help to Reduce Discrimination. - Tao Zhang 055; Tianqing Zhu; Jing Li 009; Mengde Han; Wanlei Zhou 001; Philip S. Yu (IEEE Trans. Knowl. Data Eng., 2022)

Fairness in graph-based semi-supervised learning. - Tao Zhang 055; Tianqing Zhu; Mengde Han; Fengwen Chen; Jing Li 009; Wanlei Zhou 001; Philip S. Yu (Knowl. Inf. Syst., 2023)

Implicit Weight Learning for Multi-View Clustering. - Feiping Nie 001; Shaojun Shi; Jing Li 009; Xuelong Li 001 (IEEE Trans. Neural Networks Learn. Syst., 2023)

Revisiting model fairness via adversarial examples. - Tao Zhang 055; Tianqing Zhu; Jing Li 009; Wanlei Zhou 001; Philip S. Yu (Knowl. Based Syst., 2023)

Earning Extra Performance From Restrictive Feedbacks. - Jing Li 009; Yuangang Pan; Yueming Lyu; Yinghua Yao; Yulei Sui; Ivor W. Tsang (IEEE Trans. Pattern Anal. Mach. Intell., 2023)

Towards Harmless Rawlsian Fairness Regardless of Demographic Prior. - Xuanqian Wang; Jing Li 009; Ivor W. Tsang; Yew Soon Ong (NeurIPS, 2024)

Alpha and Prejudice: Improving α-sized Worst-case Fairness via Intrinsic Reweighting. - Jing Li 009; Yinghua Yao; Yuangang Pan; Xuanqian Wang; Ivor W. Tsang; Xiuju Fu (CoRR, 2024)

Generative Adversarial Ranking Nets. - Yinghua Yao; Yuangang Pan; Jing Li 009; Ivor W. Tsang; Xin Yao 001 (J. Mach. Learn. Res., 2024)

Sanitized clustering against confounding bias. - Yinghua Yao; Yuangang Pan; Jing Li 009; Ivor W. Tsang; Xin Yao 001 (Mach. Learn., 2024)

Historical Information-Aided Monitoring of Few-Sample Modes in Industrial Processes With Orthogonal Transferred Projection. - Kai Wang 024; Xiang Lei; Wenxuan Zhou; Saige Cheng; Jing Li 009 (IEEE Trans. Ind. Informatics, 2024)

PROUD: PaRetO-gUided diffusion model for multi-objective generation. - Yinghua Yao; Yuangang Pan; Jing Li 009; Ivor W. Tsang; Xin Yao 001 (Mach. Learn., 2024)

Taming Overconfident Prediction on Unlabeled Data From Hindsight. - Jing Li 009; Yuangang Pan; Ivor W. Tsang (IEEE Trans. Neural Networks Learn. Syst., 2024)