“ Black-box Generalization of Machine Teaching,”
X. Cao, Y. Guo, I. W. Tsang, and James T. Kwok,
Journal of Machine Learning Research, revision.
“A Survey of Learning on Small Data,”
X. Cao, W. Bu, J. Huang, Y. Chang, and I. W. Tsang,
ACM Computing Surveys, review.
“Transductive Reward Inference on Graph，”
Bohao Qu, Xiaofeng Cao, Qing Guo, Chang Yi, Ivor W.Tsang, and Chengqi Zhang
IEEE Transactions on Knowledge and Data Engineering
Refining Euclidean Obfuscatory Nodes Helps: A Joint-Space Graph Learning Method for Graph Neural Networks
Zhaogeng Liu, Feng Ji, Jielong Yang, Xiaofeng Cao, Muhan Zhang, Hechang Chen, and Yi Chang.
IEEE Transactions on Neural Networks and Learning Systems
“Mitigating Privacy Risk in Membership Inference by Convex-Concave Loss,”
Zhenlong Liu, Lei Feng, Huiping Zhuang, Xiaofeng Cao, Hongxin Wei
International Conference on Machine Learning (ICML 2024)
“Deep Hierarchical Graph Alignment Kernels，”
S. Tang, H. Tian, X. Cao, W. Ye
The 33rd International Joint Conference on Artificial Intelligence, IJCAI 2024.
“Dual Expert Distillation Network for Generalized Zero-Shot Learning,”
Z. Rao, J. Guo, X. Lu, J. Liang, J. Zhang, H. Wang, K. Wei, X. Cao,
The 33rd International Joint Conference on Artificial Intelligence, IJCAI 2024.
“IRAD: Implicit Representation-driven Image Resampling against Adversarial Attacks,”
Yue Cao, Tianlin Li, Xiaofeng Cao, Ivor Tsang, Yang Liu, Qing Guo
Twelfth International Conference on Learning Representations, ICLR 2024.
“Nonparametric Teaching for Multiple Learners,”
C. Zhang, X. Cao, W. Liu, and I. W. Tsang, J. Kwok,
Thirty-seventh Conference on Neural Information Processing Systems, NeurIPS 2023.
“Hyperbolic Uncertainty Aware Semantic Segmentation,”
Bike Chen, Wei Peng, Xiaofeng Cao, Röning Juha,
IEEE Transactions on Intelligent Transportation Systems, 2023.
“Enhancing Locally Adaptive Smoothing of Graph Neural Networks via Laplacian Node Disagreement,”
Yu Wang, Liang Hu, Xiaofeng Cao*, Yi Chang,Ivor W. Tsang,
IEEE Transactions on Knowledge and Data Engineering, 2023.
“Data-Efficient Learning via Minimizing Hyperspherical Energy,”
X. Cao, Weiyang Liu, and I. W. Tsang,
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023.
“DualMatch: Robust Semi-Supervised Learning with Dual-Level Interaction，”
Cong Wang#, Xiaofeng Cao#*, Lanzhe Guo, Zenglin Shi,
Joint European Conference on Machine Learning 2023.
“Nonparametric Iterative Machine Teaching,”
Chen Zhang, Xiaofeng Cao*, Weiyang Liu, Ivor Tsang, James Kwok,
The 40th International Conference on Machine Learning (ICML 2023).
“Out-of-Distribution Generalization of Federated Learning via Implicit Invariant Relationships,”
Yaming Guo#, Kai Guo#, Xiaofeng Cao*, Tieru Wu*, Yi Chang,
The 40th International Conference on Machine Learning (ICML 2023).
“Distribution Matching for Machine Teaching,”
Xiaofeng Cao, Ivor W. Tsang.
IEEE Transactions on Neural Networks and Learning Systems, 2023.
“Distribution Disagreement via Lorentzian Focal Representation,”
X. Cao, and I. W. Tsang,
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021, [PDF][Code].
“Shattering Distribution for Active Learning,”
X. Cao, and I. W. Tsang,
IEEE transactions on neural networks and learning systems, [PDF][Code].
“Cold-Start Active Sampling via γ-Tube,”
X. Cao, and I. W. Tsang,
IEEE Transactions on Cybernetics, 2021, [PDF][Code].
“Multidimensional balance-based cluster boundary detection for high-dimensional data,”
X. Cao, B. Qiu, X. Li, Z. Shi, G. Xu, J. Xu,
IEEE transactions on neural networks and learning systems, 2018, [PDF][Code].
“Crowd counting with deep negative correlation learning,”
Z. Shi, L. Zhang, Y. Liu, X. Cao, Y. Ye, M. M. Cheng, G. Zheng,
Proceedings of the IEEE conference on computer vision and pattern recognition, 2018, [PDF][Code].
“Learning Image-Specific Attributes by Hyperbolic Neighborhood Graph Propagation”,
X. Xu, I. W. Tsang, X. Cao, R. Zhang, C. Liu,
Proceedings of the 28th International Joint Conference on Artificial Intelligence, 2020, [PDF][Code].
