郭正初 - 浙江大学 - 数学科学学院

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.