你可能对以下一个或多个主题感兴趣，部分主题可能涉及机器学习理论、黑盒优化等较为数学化的内容：
§ Hyperbolic Geometry (双曲空间和黎曼几何的建模与优化)
[1] Xiaofeng Cao, Ivor W. Tsang. Distribution Disagreement via Lorentzian Focal Representation, IEEE Transactions on Pattern Analysis and Machine Intelligence. (T-PAMI为模式分析旗舰期刊，IF=24.314, CCF A类期刊)
[2] Xiaofeng Cao, Ivor W. Tsang. Hyperbolic Fréchet Mean. Pattern Recognition. IF=8.518. (独著工作,研究非欧空间下的Fréchet Mean优化与求解)
[3] Xiaofeng Xu, Ivor W. Tsang, Xiaofeng Cao, et al. Learning image-specific attributes by hyperbolic neighborhood graph propagation. International Joint Conference on Artificial Intelligence 2019.(非欧树结构， CCF A 类会议)
[4] Yang Tao and Xiaofeng Cao. Perturbation Elimination via Homeomorphic Manifold Tubes, IEEE Transactions on Neural Networks and Learning Systems, review. （黎曼通管优化）
[5] Bike Chen, Wei Peng, Xiaofeng Cao, Röning Juha, Hyperbolic Uncertainty Aware Semantic Segmentation. IEEE Transactions on Intelligent Transportation Systems, 2023.(智能交通顶级刊物)
§ Machine Teaching/Black-Box Solving/Convex Optimization/Non-Convex Approximation (机器教学/黑盒求解/凸优化/非凸近似)
[1] Xiaofeng Cao, Ivor W. Tsang. Distribution-based Machine Teaching for a Black-box, IEEE Transactions on Neural Networks and Learning Systems, IF=14.255.（黑盒机器教学优化理论）
[2] Xiaofeng Cao, Ivor W. Tsang. On the Geometry of Deep Bayesian Active Learning, IEEE Transactions on Emerging Topics in Computational Intelligence, revision, IF=4.851.
[3] Chen Zhang, Xiaofeng Cao. Pseudo-Iterative Machine Teaching. Pseudo-Iterative Machine Teaching, Artificial Intelligence (人工智能旗舰期刊，国际人工智能会刊), review. IF=14.05.（封闭梯度优化，CCF A类会议）
[4] Xiaofeng Cao# and Yaming Guo#. Black-box Teaching an Active Learner, Journal of Machine Learning Research, revision (机器学习领域旗舰期刊). 这可能是黑盒泛化理论重要的研究成果！
[5] Chen Zhang, Xiaofeng Cao*, Weiyang Liu, Ivor Tsang, James Kwok, Nonparametric Iterative Machine Teaching, ICML 2023. (CCF A类会议).（泛函梯度优化理论，CCF A类会议）
[6] Cong Wang#, Xiaofeng Cao#*, Lanzhe Guo, Zenglin Shi. DualMatch: Robust Semi-Supervised Learning with Dual-Level Interaction. ECML 2023.
§ Learning Theory and Generalization Analysis (学习理论与泛化分析)
[1] Xiaofeng Cao, Ivor W. Tsang. Shattering distribution for active learning, IEEE Transactions on Neural Networks and Learning Systems, 2020, IF=14.255. （分布破碎）
[2] Xiaofeng Cao, Ivor W. Tsang, Jianliang Xu. Cold-start Active Sampling via $\gamma$-Tube, IEEE Transactions on Cybernetics, 2021. IF=19.118.（通管采样）
[3]Yaming Guo#, Kai Guo#, Xiaofeng Cao*, Tieru Wu*, Yi Chang, Out-of-Distribution Generalization of Federated Learning via Implicit Invariant Relationships, ICML 2023. (CCF A类会议)
§ Learning on Small Data (新主题：Small data is the future of AI)
[1] Xiaofeng Cao, Weiyang Liu, Ivor W. Tsang. Data-Efficient Learning via Minimizing Hyperspherical Energy.IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023.（最小能量球优化问题， CCF A类期刊）
[2] Learning on Small Data: Transfer the Future of Artificial Intelligence to Now, Survey work, IEEE Transactions on Pattern Analysis and Machine Intelligence, review. （小数据学习）
§ General AI Applications: Data, Image, Graph.
[1] Xiaofeng Cao et al. Multidimensional Balance-Based Cluster Boundary Detection for High-Dimensional Data, IEEE Transactions on Neural Networks and Learning Systems, 30(6): 1867-1880, 2019. IF=14.255. (边界检测)
[2]Yu Wang, Liang Hu, Xiaofeng Cao*, Yi Chang,Ivor W. Tsang,Enhancing Locally Adaptive Smoothing of Graph Neural Networks via Laplacian Node Disagreement, IEEE Transactions on Knowledge and Data Engineering, 2023. (CCF A类期刊)
[3] Zenglin Shi, Le Zhang, Yun Liu, Xiaofeng Cao et al. Crowd counting with deep negative correlation learning. Computer Vision and Pattern Recognition Conference (CVPR) 2018. （CCF A类会议）
[4]Yu Wang, Liang Hu, Wanfu Gao*, Xiaofeng Cao*, Yi Chang, AdaNS: Adaptive Negative Sampling for Unsupervised Graph Representation Learning, Pattern Recognition, 2022.IF=8.518. （图表征）
[5]Kai Guo, Xiaofeng Cao*, Zhining Liu, Yi Chang*, Taming Over-Smoothing Representation on Heterophilic Graphs, Information Sciences, 2023. (IF=8.1)
注：如果你有意与我一同工作，并且拟攻读硕士研究生，你可能会直接参与以上课题。如果你有意攻读博士，我们将一起探索更为基础和前沿的机器学习内容，可能包括Meta-Learning、Distribution Optimization, 等。你可能与我、Ivor Tsang、James Kwok等权威教授一起工作。他们是是国际知名人工智能/机器学习领域权威学者，是各大人工智能旗舰期刊/会议的编辑/主席，如JMLR、MLJ、T-PAMI、JAIR、NeurIPS、 IJCAI，等。课题组已经与剑桥大学、斯坦福大学的一流同行建立合作，欢迎有志青年加入我们！
