成电讲堂

　　6月8日，明尼苏达大学Yang Yuhong教授受邀做客学术沙龙，带来题为“Cross-Validation for Optimal and Reproducible Statistical Learning”的讲座。

　　Yang Yuhong首先介绍了交叉验证方法的背景，指出交叉验证方法主要用于评价一种方法的好坏及对模型的选择和预测三个方面。Yang Yuhong通过生动的例子向大家解释了在训练集较大、测试集相对小的情况下，交叉验证方法结果很好，并给大家展示了理论证明过程跟数值计算结果。最后他将CV方法用于选择AIC和BIC准则的分析，还进一步将交叉验证方法用于评估现有模型选择方法的优劣。  

　　报告会后，Yang Yuhong与在场的师生就该方法所涉及的具体数据类型进行了深入交流。他表示，报告所讲方法是应用研究中涉及到的方法，鼓励大家对其做进一步的研究。

　　本次活动由人力资源部教师发展中心主办，数学科学学院承办。

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　　Yuhong Yang received his Ph.D from Yale in statistics in 1996. He then joined the Department of Statistics at Iowa State University and moved to the University of Minnesota in 2004. His research interests include model selection, multi-armed bandit problems, forecasting, high-dimensional data analysis, and machine learning. He has published in journals in several fields, including Annals of Statistics, IEEE Transaction on Information Theory, Journal of Econometrics, Journal of Approximation Theory, Journal of Machine Learning Research, and International Journal of Forecasting.