报告名称：Sparse Modal Additive Model

主办单位：英国立博官网中文版

报告专家：陈 洪

专家所在单位：华中农业大学

报告时间：2018年12月12日8：00-10：00

报告地点：英国立博官网中文版203报告厅

专家简介：陈洪，华中农业大学教授，博士生导师。研究方向为机器学习、学习理论、逼近论。2009.6博士毕业于湖北大学基础数学专业，湖北省优秀博士论文获得者。2016.3-2017.8在University of Texas at Arlington从事博士后研究，多次受邀请赴澳门大学、香港城市大学等进行合作研究。主持国家自然科学基金面上项目、青年基金等多项科研课题，在Appl. Comput. Harmon. Anal., J. Approx. Theory, IEEE TPAMI, IEEE TNNLS, IEEE T. Cybernetics, Neural Networks, Neural Computation, Bioinformatics等知名期刊发表SCI论文20余篇，在机器学习顶级会议NIPS发表论文3篇。

报告摘要：Sparse additive models have been successfully applied to high dimensional data analysis due to their representation flexibility and interpretability. However, existing methods are often formulated with the least squares loss under the mean square error (MSE) criterion, which is sensitive to data with the non-Gaussian noise, e.g., the skewed noise, the heavy-tailed noise, and outliers. To cure this problem, we propose a new sparse method, called sparse modal additive model (SpMAM), by integrating the mode-induced loss, the data dependent hypothesis space, and the weighted \ell_{q,1}-norm regularizer (q

1) into additive models. In contrast to existing methods that aim to learning the conditional mean, the proposed method approximates the intrinsic mode and is robust to the complex noise. Theoretical properties of SpMAM are characterized including generalization bound and variable selection consistency. Experimental results on simulated and benchmark datasets confirm the effectiveness and robustness of the proposed model.