报告人：陈洪 (华中农业大学)

报告题目：Regularized Modal Regression with Applications in Cognitive Impairment Prediction

报告人简介：陈洪，教授，博士生导师，湖北省优秀博士论文获得者。2009年6月在湖北大学获基础数学博士学位。2016.2-2017.8在University of Texas at Arlington 从事博士后研究，多次受邀赴澳门大学、香港城市大学从事合作研究。主持国家自然科学基金面上项目1项、青年基金1项，主持中央高校创新团队培育项目1项、优秀人才培育项目1项。在国际期刊发表论文30余篇，在机器学习顶级会议NIPS发表论文3篇。

报告摘要:Linear regression models have been successfully used to function estimation and model selection in high-dimensional data analysis. However, most existing methods are built on least squares with the mean square error (MSE) criterion. In this talk, we go beyond this criterion by investigating the regularized modal regression from a statistical learning viewpoint. A new regularized modal regression model is proposed for estimation and variable selection, which is robust to outliers, heavy-tailed noise, and skewed noise. On the theoretical side, we establish the approximation estimate for learning the conditional mode function, the sparsity analysis for variable selection, and the robustness characterization. On the application side, we applied our model to improve the cognitive impairment prediction using the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort data.

报告时间:2018年5月16日（星期三）上午9：00-10：00

报告地点:科技楼南楼602室