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个人简介

郭正初,理学博士,现为浙江大学数学科学学院教授,主要研究方向为学习理论与机器学习理论。2011年于中山大学和香港城市大学取得博士学位(联合培养),毕业后在香港城市大学和英国埃克塞特大学从事博士后研究工作,于2013年8月加入浙江大学数学科学学院。 科研 目前主持一项浙江省杰出青年基金,参与一项国家自然科学基金重点项目。 相关链接 浙江大学数学科学学院 香港城市大学数学系 香港城市大学数据科学学院 中山大学数学学院

研究领域

学习理论与机器学习

近期论文

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[20] Zheng-Chu Guo, Lei Shi and Shao-Bo Lin, Realizing data features by deep nets, IEEE Transactions on Neural Networks and Learning Systems, 31(2020): 4036-4048. [19] Zhiying Fang, Zheng-Chu Guo and Ding-Xuan Zhou, Optimal learning rates for distribution regression. Journal of Complexity, 56 (2020): 1-15. [18] Zheng-Chu Guo and Lei Shi, Fast and Strong Convergence of Online Learning Algorithms. Advances in Computational Mathematics, 26(2019): 1-26. [17] Zheng-Chu Guo, Shaobo Lin and Lei Shi, Distributed learning with multi-penalty regularization, Applied and Computational Harmonic Analysis, 46:478-499, 2019. [16] Zheng-Chu Guo, Ting Hu and Lei Shi, Gradient descent for robust kernel based regression. Inverse Problems, 34, 065009(29pp),2018. [15] Yunwen Lei, Lei Shi,and Zheng-Chu Guo, Convergence of Unregularized Online Learning Algorithms, Journal of Machine Learning Research, 18(171), 1-33, 2018. [14]Yuyi Wang, Zheng-Chu Guo and Jan Ramon, Learning from Networked Examples, Proceedings of Machine Learning Research, 76:1–26, 2017. [13] Zheng-Chu Guo, Lei Shi, Qiang Wu; Learning Theory of Distributed Regression with Bias Corrected Regularization Kernel Network, Journal of Machine Learning Research, 18(118):1--25, 2017. [12] Zheng-Chu Guo and Lei Shi, Optimal Rates for Coefficient-based Regularized Regression, Applied and Computational Harmonic Analysis, to appear, 1-41, 2017. [11] Zheng-Chu Guo, Shaobo Lin and Ding-Xuan Zhou, Learning Theory of Distributed Spectral Algorithms, Inverse Problems, 33(7) , 074009(29pp),2017. [10] Zheng-Chu Guo, Dao-Hong Xiang, Xin Guo and Ding-Xuan Zhou, Thresholded Spectral Algorithms for Sparse Approximations, Analysis and Applications,15( 2017):433-455. [9] Zheng-Chu Guo Yiming Ying and Ding-Xuan Zhou, Online Regularized Learning with Pairwise Loss Functions. Advances in Computational Mathematics, 43(2017):127--150. [8] Qiong Cao, Zheng-Chu Guo and Yiming Ying, Generalization Bounds for Metric and Similarity Learning. Machine Learning, 102(2016):115--132. [7] Zheng-Chu Guo and Yiming Ying, Guaranteed Classification via Regularised Similarity Learning. Neural Computation, 26(2014): 497-- 522. [6] Yuyi Wang, Jan Ramon and Zheng-Chu Guo, Learning from Networked Examples in a k-partite Graph. French Conference on Machine Learning, 2013. [5] Zheng-Chu Guo and Lei Shi, Learning with Coefficient-based Regularization and l^1-penalty. Advances in Computational Mathematics. 39(2013): 493--510. [4] Zheng-Chu Guo and Ding-Xuan Zhou, Concentration Estimates for Learning with Unbounded Sampling. Advances in Computational Mathematics. 38(2013): 207--223. [3] Cheng Wang and Zheng-Chu Guo, ERM Learning Algorithm for Multi-class Classification. Applicable Analysis. 7(2012): 2339--1349. [2] Zheng-Chu Guo and Cheng Wang, Online Regression with Unbounded Sampling. International Journal of Computer Mathematics. 88(2011): 2936--2941. [1] Zheng-Chu Guo and Lei Shi, Classification with Non-i.i.d Sampling. Mathematical and Computer Modelling. 54(2011): 1347--1364.

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