报告名称：Multi-task Additive Models for Robust Estimation and Automatic Structure Discovery

报告专家：陈洪

专家所在单位：华中农业大学

报告时间：2021年11月11日：14：00-18：00

线上腾讯会议：424385948 密码：258369

专家简介：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。在数学期刊ACHA、JAT等发表论文多篇，在人工智能CCFA会议和期刊发表论文7篇，主持国家自然科学基金面上项目等国家级项目5项。

报告摘要：Abstract: Additive models have attracted much attention for high-dimensional regressionestimation 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 a priori knowledge among variables. For high-dimensional observations in real environment, e.g., Coronal Mass Ejections (CMEs) data, the learning performance of previous methods may be degraded seriously due to the complex non-Gaussian noise and the insufficiency of a prior knowledge on variable structure. To tacklethis 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 any priori knowledge of variable structure and suits for high-dimensional data with complex noise, e.g., skewed noise, heavy-tailed noise, and outliers. 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.

邀请人：刘展

(审核：郑大彬）