专职教师

统计学院副教授,博士生导师,20117月博士毕业,获得中国科学技术大学理学博士及香港城市大学应用数学博士(后者为Part Time)20119月至今在西南财经大学统计学院工作,同时20149月至20159月受邀为日本统计数理研究所访问副教授。主要研究方向包括:统计学习与数据挖掘,高维非参推断以及机器学习在金融工程中的应用等。已经在SCI收录的杂志上发表学术论文10余篇。

  1. 教授课程
  2. 学术成果
  3. 主持项目
  4. 学术兼职
  •   《数据挖掘》,《统计学习》,《随机过程》
  • 1.Shaogao Lv, Xin He and Junhui Wang. (2016). A unified penalized method for sparse additive quantile
    models: a RKHS approach. (2016). Annals of the Institute of Statistical Mathematics, Accepted.

    2.Lei Yang, Shaogao Lv and Junhui Wang*. (2016). Model-free variable selection in reproducing kernel Hilbert space. Journal of Machine Learning Research. 171—24.

    3.Yunlong Feng, Shaogao Lv*, Hanyuan Han, Johan A.K. Suykens. (2016). Kernelized elastic net regularization: generalization bounds and sparse recovery. Neural Computation, 28, 525–562 .

    4..ShaogaoLv*. (2015). Refined generalization bounds of gradient learning overreproducing Kernel Hilbert spaces . Neural Computation, (27), 1294–1320.

    5. Shaogao Lv* and Fanyin Zhou. (2015) Optimal learning rates of L^p-type multiple kernel learning under general conditions. Information Science, (10) 255–268.

    6. Yue Liu, Bingjie Wang* and Shaogao Lv. (2014) Using multi-class adaboost tree for prediction frequency of auto insurance. Journal of Applied Finance & Banking, (4), 45-53.

    7. Shao-Gao Lv*, Dai-Min Shi, Quan Wu Xiao and Ming Shan Zhang. (2013) Sharp learning rates of coefficient-based l^p-regularized regression with indefinite kernels. Science China Mathematics, 56(8), 1557-1574 (SCI).

    8.Shao-Gao Lv*, Tie-Feng Ma, Liu Liu and Yun-Long Feng. (2013) . Fast learning rates for sparse quantile regression problem. Neurocomputing, 108, 13-22 (SCI).

    9.Shao-Gao Lv*and Yun-Long Feng. (2013). Consistency of coefficient-based spectral clustering with l^1-regularizer. Mathematics and Computer Modeling. 57, 469--482 (SCI).

    10.Shao-Gao Lv *and Yun-Long Feng. (2012). Integral operator approaches to learning theory with unbounded sampling. Complex Analysis and Operator Theory. 6, 533--548.(SCI).

    11.Shao-Gao Lv* and Yun-Long Feng. (2012). Semi-supervised learning with the help of Parzen windows. Journal of Mathematics Analysis and Applications. 386, 205--212. (SCI).

    12.Shao-Gao Lv *and Jinde Zhu. (2012). Error bounds for lp-norm multiple kernel learning with least square loss. Abstract and Applied Analysis, Article ID 915920, 18 pages.doi: 10.1155/2012/915920.

    13.Yun-Long Feng* and Shao-Gao Lv. (2011). Unified approach to coefficient-based regularized regression.  Computer and Mathematic With Application. 62, 506--515. (SCI).

    14.Shao-Gao Lv* and Lei Shi. (2010). Learning theory viewpoint of approximation by positive linear operators. Computer and Mathematics with Application. 60, 3177--3186. (SCI).

  • 1、国家自然科学基金青年项目:高维数据框架内的非参与半参分位数回归模型的研究,No.11301421, (2014-2016)

    2、中央高校专项资金-交叉创新项目:不依赖于模型的可大规模计算的变量选择方法. No.JBK140210.(2014-2015).

    3、中央高校基本科研业务费专项资金--交叉创新项目:有关稀疏型的非参分位数回归模型的研究,No.JBK130219, (2013-2014)

    4、国家自然科学基金天元专项基金:基于凸正则化项的多核学习算法的理论研究,No.11226111,(2012-2013)。

    5、中央高校基本科研业务费专项资金--年度培育项目:一类多核学习算法的统计特性研究,No. JBK120940,2012-2013)。

    6、西南财经大学“211工程三期青年教师成长项目:非正定核学习算法的理论基础与应用研究,No. 211QN2011028, (2011-2012)