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曹晓锋

发布时间:2021-04-16点击:

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基本情况

姓名:

曹晓锋

 





 

 

 

性别:

职称:

准聘副教授

是否博导:

最高学历:

研究生

最高学位:

博士

电话:

0431-85168503

Email

xiaofeng.cao.uts@gmail.com

 

详细情况

所在学科专业:

计算机科学

所研究方向:

机器学习,具体包括PAC Learning TheoryAgnostic Learning AlgorithmGeneralization   Analysis以及Hyperbolic   Geometry, Riemannian Manifold等。

讲授课程:

暂无

教育经历:

20179-20211月:博士,澳大利亚人工智能研究院|悉尼科技大学,计算机科学

20109-20176月:本科、硕士,郑州大学,计算机科学与技术

工作经历:

20211-至今:研究助理,悉尼科技大学

科研项目

学术论文:

注: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=17.861)

[2] Xiaofeng Cao, Ivor W. Tsang. Learning Hyperbolic Fréchet Mean   for Deep Representations. Review in IEEE TNNLS. 神经学习汇刊.

[3] Xiaofeng Xu, Ivor W. Tsang, Xiaofeng Cao, et al. Learning   image-specific attributes by hyperbolic neighborhood graph propagation. IJCAI   2019.

[4] Yang Tao and Xiaofeng Cao. Perturbation   Elimination via Homeomorphic Manifold Tubes, in progress.

§ Machine   Teaching/ Black-box/Bayesian Optimization (机器教学/黑盒/贝叶斯优化)

[1] Xiaofeng   Cao, Ivor W. Tsang. Distribution-based Machine Teaching for a Black-box,   Artificial Intelligence, to be accepted.

[2] Xiaofeng Cao, Ivor W. Tsang. On the Geometry of Deep Bayesian   Active Learning, T-PAMI, under review.

[3] Chen Zhang, Xiaofeng Cao. Pseudo-Iterative Machine Teaching. Pseudo-Iterative   Machine Teaching, in progress.

[4] Xiaofeng Cao# and Yaming Guo#. Black-box Teaching an Active   Learner, in progress.

§ Active   Learning Theory and Its Generalization Analysis (主动学习理论与其泛化分析)

[1] Xiaofeng Cao, Ivor W. Tsang. Shattering distribution for active   learning, TNNLS, 2020.

[2] Xiaofeng Cao, Ivor W. Tsang, Jianliang Xu. Cold-start Active   Sampling via $\gamma$-Tube, T-CYB, 2021.

[3] Xiaofeng   Cao. A structured perspective of volumes on active learning. Neurocomputing,   377: 200-212, 2020. (唯一作者)

[4] Xiaofeng Cao. A divide-and-conquer approach to geometric   sampling for active learning, Expert Systems with Applications, 140, 2020. (唯一作者)

   § Learning on Small Data (新主题:Small data is the future of AI)

[1] Xiaofeng Cao, Ivor W. Tsang.  Learning on Small data via Minimizing   Hyperspherical Energy. Review in T-PAMI.

[2] Learning on Small Data: Transfer the Future of Artificial   Intelligence to Now, Survey work, in progress.

   § Applications of Data Mining and Image Analysis (涵盖部分硕士研究生工作)

[1] Xiaofeng Cao et al. Multidimensional Balance-Based Cluster   Boundary Detection for High-Dimensional Data, TNNLS, 30(6): 1867-1880, 2019.

[2] Xiaofeng Cao et al. BorderShift: toward optimal MeanShift   vector for cluster boundary detection in high-dimensional data, Pattern   Analysis and Applications 22(3): 1015-1027, 2019.

[3] Xiaofeng Cao. High-dimensional cluster boundary detection using   directed Markov tree, Pattern Analysis and Applications, 2020. (唯一作者)

[4] Baozhi Qiu, Xiaofeng Cao*. Clustering boundary detection for   high dimensional space based on space inversion and Hopkins statistics.   Knowledge-Based Systems 98 (2016): 216-225. (通讯作者,硕士研究生工作)

[5] Zenglin Shi, Le Zhang, Yun Liu, Xiaofeng Cao et al. Crowd   counting with deep negative correlation learning. CVPR 2018.

[6] Xiangli Li, Xiaofeng Cao*, Baozhi   Qiu. Clustering boundary pattern discovery for high dimensional space base on   matrix model. Acta Automatica Sinica, 43(11), pp.1962-1972, 2017. (通讯作者,硕士研究生工作)

 

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-LearningDistribution   Optimization, 等。你可能与我、Ivor   Tsang一起工作。Ivor Tsang是国际知名机器学者,他是NeurIPS 2021 Exp Chair, ACML 2021 Co-Chair, ICML 2021 Senior   Area Chair,同时也是JMLRMLJT-PAMIJAIR等机器学习和人工智能旗舰期刊的Editor/Associate Editor

 

著作教材:

获奖情况:

社会兼职:

Conference Reviewer/Program Committee: ACML 2021, ICML 2021,   NeurIPS 2021.

Journal Reviewer: T-PAMI, MLJ, TNNLS.

 

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