报告题目：Robust Multi-task Additive Models
腾讯会议ID：229 442 797
摘要：Additive models have attracted much attention for high-dimensional regression estimation and variable selection. However, the existing models are usually limited to the single-task learning framework under the mean squared error (MSE) criterion, where the utilization of variable structure depends heavily on priori knowledge among variables. For high-dimensional observations in real environment, the learning performance of previous methods may be degraded seriously due to the complex non-Gaussian noise and the insufficiency of prior knowledge on variable structure. To tackle this problem, we propose a new class of additive models, called Multi-task Additive Models(MAM), by integrating the mode-induced metric, the structure-based regularizer, and additive hypothesis spaces into a bilevel optimization framework. Our approach does not require priori knowledge of variable structure and suits for high-dimensional data with complex noise. A smooth iterative optimization algorithm with convergence guarantees is provided to implement MAM efficiently. Experiments on simulations and the CMEs analysis demonstrate the competitive performance of our approach for robust estimation and automatic structure discovery.
报告人简介：陈洪，华中农业大学数学与统计学系教授，博士生导师。研究方向为机器学习、统计学习理论。在人工智能顶会NIPS、ICML等发表论文6篇，在Appl. Comput. Harmon. Anal., J. Approx. Theory, IEEE TPAMI/TNNLS/TCYB, Neural Computation, Neural Networks, Bioinformatics等知名期刊发表论文30余篇，主持（含已完成）国家自然科学基金面上项目、青年基金等5项国家级课题。曾在University of Texas at Arlington从事博士后研究，多次受邀赴澳门大学等进行合作研究。