Learning Image-Specific Attributes by Hyperbolic Neighborhood Graph Propagation. - Xiaofeng Xu; Ivor W. Tsang; Xiaofeng Cao 002; Ruiheng Zhang 001; Chuancai Liu (IJCAI, 2019)

BorderShift: toward optimal MeanShift vector for cluster boundary detection in high-dimensional data. - Xiaofeng Cao 002; Baozhi Qiu; Guandong Xu (Pattern Anal. Appl., 2019)

Multidimensional Balance-Based Cluster Boundary Detection for High-Dimensional Data. - Xiaofeng Cao 002; Baozhi Qiu; Xiangli Li; Zenglin Shi; Guandong Xu; Jianliang Xu (IEEE Trans. Neural Networks Learn. Syst., 2019)

A divide-and-conquer approach to geometric sampling for active learning. - Xiaofeng Cao 002 (Expert Syst. Appl., 2020)

A structured perspective of volumes on active learning. - Xiaofeng Cao 002 (Neurocomputing, 2020)

Bayesian Active Learning by Disagreements: A Geometric Perspective. - Xiaofeng Cao 002; Ivor W. Tsang (CoRR, 2021)

High-dimensional cluster boundary detection using directed Markov tree. - Xiaofeng Cao 002 (Pattern Anal. Appl., 2021)

Distribution-based Active Learning - Xiaofeng Cao 002 (2021)

When an Active Learner Meets a Black-box Teacher. - Xiaofeng Cao 002; Yaming Guo; Tieru Wu; Ivor W. Tsang (CoRR, 2022)

A Survey of Learning on Small Data. - Xiaofeng Cao 002; Weixin Bu; Shengjun Huang; Ying-Peng Tang; Yaming Guo; Yi Chang 001; Ivor W. Tsang (CoRR, 2022)

One-shot Machine Teaching: Cost Very Few Examples to Converge Faster. - Chen Zhang; Xiaofeng Cao 002; Yi Chang 001; Ivor W. Tsang (CoRR, 2022)

Robust active representation via ℓ2, p-norm constraints. - Jiaoyan Zhao; Shuangyan Yi; Yongsheng Liang 001; Wei Liu 065; Xiaofeng Cao 002 (Knowl. Based Syst., 2022)

Distribution Disagreement via Lorentzian Focal Representation. - Xiaofeng Cao 002; Ivor W. Tsang (IEEE Trans. Pattern Anal. Mach. Intell., 2022)

Cold-Start Active Sampling Via γ-Tube. - Xiaofeng Cao 002; Ivor W. Tsang; Jianliang Xu (IEEE Trans. Cybern., 2022)

Shattering Distribution for Active Learning. - Xiaofeng Cao 002; Ivor W. Tsang (IEEE Trans. Neural Networks Learn. Syst., 2022)

Out-of-Distribution Generalization of Federated Learning via Implicit Invariant Relationships. - Yaming Guo; Kai Guo 003; Xiaofeng Cao 002; Tieru Wu; Yi Chang 001 (ICML, 2023)

Nonparametric Iterative Machine Teaching. - Chen Zhang; Xiaofeng Cao 002; Weiyang Liu; Ivor W. Tsang; James T. Kwok (ICML, 2023)

Nonparametric Teaching for Multiple Learners. - Chen Zhang; Xiaofeng Cao 002; Weiyang Liu; Ivor W. Tsang; James T. Kwok (NeurIPS, 2023)

Aggregation Weighting of Federated Learning via Generalization Bound Estimation. - Mingwei Xu; Xiaofeng Cao 002; Ivor W. Tsang; James T. Kwok (CoRR, 2023)

AdaNS: Adaptive negative sampling for unsupervised graph representation learning. - Yu Wang 152; Liang Hu 001; Wanfu Gao; Xiaofeng Cao 002; Yi Chang 001 (Pattern Recognit., 2023)

Improving generalization of double low-rank representation using Schatten-p norm. - Jiaoyan Zhao; Yongsheng Liang 001; Shuangyan Yi; Qiangqiang Shen; Xiaofeng Cao 002 (Pattern Recognit., 2023)

Taming over-smoothing representation on heterophilic graphs. - Kai Guo 003; Xiaofeng Cao 002; Zhining Liu 002; Yi Chang 001 (Inf. Sci., 2023)

Towards fidelity of graph data augmentation via equivariance. - Bai Zhang; Yixing Gao; Feng Ji; Linbo Xie; Xiaofeng Cao 002; Yixiang Shan; Jielong Yang (Knowl. Based Syst., 2023)

Data-Efficient Learning via Minimizing Hyperspherical Energy. - Xiaofeng Cao 002; Weiyang Liu; Ivor W. Tsang (IEEE Trans. Pattern Anal. Mach. Intell., 2023)

A First-Order Multi-Gradient Algorithm for Multi-Objective Bi-Level Optimization. - Feiyang Ye 001; Baijiong Lin; Xiaofeng Cao 002; Yu Zhang 006; Ivor W. Tsang (ECAI, 2024)

IRAD: Implicit Representation-driven Image Resampling against Adversarial Attacks. - Yue Cao; Tianlin Li; Xiaofeng Cao 002; Ivor W. Tsang; Yang Liu 003; Qing Guo 005 (ICLR, 2024)

MetaRepair: Learning to Repair Deep Neural Networks from Repairing Experiences. - Yun Xing; Qing Guo 005; Xiaofeng Cao 002; Ivor W. Tsang; Lei Ma 003 (ACM Multimedia, 2024)

Sharpness-Aware Minimization Activates the Interactive Teaching's Understanding and Optimization. - Mingwei Xu; Xiaofeng Cao 002; Ivor W. Tsang (NeurIPS, 2024)

Breaking the curse of dimensional collapse in graph contrastive learning: A whitening perspective. - Yang Tao; Kai Guo 003; Yizhen Zheng; Shirui Pan; Xiaofeng Cao 002; Yi Chang 001 (Inf. Sci., 2024)

Hyperbolic Uncertainty Aware Semantic Segmentation. - Bike Chen; Wei Peng 009; Xiaofeng Cao 002; Juha Röning (IEEE Trans. Intell. Transp. Syst., 2024)

Enhancing Locally Adaptive Smoothing of Graph Neural Networks Via Laplacian Node Disagreement. - Yu Wang 152; Liang Hu 001; Xiaofeng Cao 002; Yi Chang 001; Ivor W. Tsang (IEEE Trans. Knowl. Data Eng., 2024)

Improving Augmentation Consistency for Graph Contrastive Learning. - Weixin Bu; Xiaofeng Cao 002; Yizhen Zheng; Shirui Pan (Pattern Recognit., 2024)

Distribution Matching for Machine Teaching. - Xiaofeng Cao 002; Ivor W. Tsang (IEEE Trans. Neural Networks Learn. Syst., 2024)

Refining Euclidean Obfuscatory Nodes Helps: A Joint-Space Graph Learning Method for Graph Neural Networks. - Zhaogeng Liu; Feng Ji; Jielong Yang; Xiaofeng Cao 002; Muhan Zhang; Hechang Chen; Yi Chang 001 (IEEE Trans. Neural Networks Learn. Syst., 2024)

Transductive Reward Inference on Graph. - Bohao Qu; Xiaofeng Cao 002; Qing Guo 005; Yi Chang 001; Ivor W. Tsang; Chengqi Zhang (IEEE Trans. Knowl. Data Eng., 2024)

Diversifying Policies With Non-Markov Dispersion to Expand the Solution Space. - Bohao Qu; Xiaofeng Cao 002; Yi Chang 001; Ivor W. Tsang; Yew-Soon Ong (IEEE Trans. Pattern Anal. Mach. Intell., 2024)

Towards a structured analysis of domain perturbations in Tubular Geometry. - Yang Tao; Xiaofeng Cao 002; Yi Chang 001 (Pattern Recognit., 2025)

Lighting is Unreliable: Adversarial Video Relighting Against rPPG Heart Rate Measurement. - Menglin Zhang; Xiaoxin Guo; Xiaofeng Cao 002; Shuifa Sun; Huazhu Fu; Qing Guo 005 (IEEE Trans. Inf. Forensics Secur., 2025)

Time-variant Image Inpainting via Interactive Distribution Transition Estimation. - Yun Xing; Qing Guo 005; Xiaohong Li 001; Yihao Huang 001; Xiaofeng Cao 002; Di Lin 002; Ivor W. Tsang; Lei Ma 003 (CoRR, 2025)

FOCUS: Frequency-Optimized Conditioning of DiffUSion Models for mitigating catastrophic forgetting during Test-Time Adaptation. - Gabriel Tjio; Jie Zhang 002; Xulei Yang; Yun Xing; Nhat Chung; Xiaofeng Cao 002; Ivor W. Tsang; Chee Keong Kwoh; Qing Guo 005 (CoRR, 2025)