国家天元数学中部中心高性能计算系列报告 | 陈洪 教授(华中农业大学)

发布时间: 2022-02-21 08:41

报告题目:Multi-task Additive Models for Robust Estimation and Automatic Structure Discovery

报告时间:2022-02-22  10:00 - 11:00

报告人:陈洪 教授 华中农业大学

报告地点:理学院东北楼404报告厅

Abstract: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 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 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, e.g., skewed noise and heavy-tailed 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.