科学研究

Dr. Jiancheng Jiang is Professor of Statistics in the Department of Mathematics and Statistics & the School of Data Science at the University of North Carolina (UNC) at Charlotte. He has published over 70 refereed research papers in (bio)statistics and financial econometrics and been awarded several NSF/NIH/NSFC grants, in addition to serving as AE for several professional journals and as statistics program coordinator of his department for many years. His research interests include but not limited to AI-driven mathematics, financial time series, statistical inference for high-dimensional models, survival analysis, and nonparametric smoothing. Currently he serves as co-PI of the Charlotte Center for Trustworthy AI through Model Management (TAIM2) at UNC Charlotte.

There are many supervised learning algorithms for modeling a given dataset. Implementing one of the algorithms usually reduces to minimizing an objective function of the data over the parameters of model indexed by some tuning parameters. Success of the algorithm depends on how to choose the tuning parameters. In this lecture we introduce some universal techniques for the choice of tuning parameters, in addition to some easy to use but nonuniversal methods. Examples will be employed to implement theses techniques.