报 告 人：胡庆培 研究员

报告题目：数据与机理结合的数据恢复与建模方法

报告时间：2023年12月7日（周四）下午14:30

报告地点：分析测试中心102会议室

主办单位：数学研究院、bat365中文官方网站、科学技术研究院

报告人简介：

       胡庆培，中国科学院数学与系统科学研究院研究员，现任系统所统计室主任、质量与数据科学中心常务副主任、航天产品可靠性技术与质量科学联合实验室副主任。长期从事工业统计与系统可靠性的研究工作，在系统可靠性综合评估、加速退化试验评估与设计、系统可靠性增长建模与推断、复杂数据分析等方向研究成果发表于IISE、ITR、RESS、JQT、NeurIPS等，曾获关肇直青年科学奖、IISE最佳年度论文提名奖等。目前担任系统科学与数学、IISE、QTQM、QREI学术期刊的副编辑或编委。

报告摘要：

       Because of the widespread existence of noise and data corruption, recovering the true regression parameters with a certain proportion of corrupted response variables is an essential task. Methods to overcome this problem often involve robust least-squares regression, but few methods perform well when confronted with severeadaptive adversarial attacks. In many applications, prior knowledge is often available from historical data or engineering experience, and by incorporating prior information into a robust regression method, we develop an effective robust regression method that can resist adaptive adversarial attacks. First, we propose the novel TRIP (hard Thresholding approach to Robust regression with sImple Prior) algorithm, which improves the breakdown point when facing adaptive adversarial attacks. Then, to improve the robustness and reduce the estimation error caused by the inclusion of priors, we use the idea of Bayesian reweighting to construct the more robust BRHT (robust Bayesian Reweighting regression via Hard Thresholding) algorithm. We prove the theoretical convergence of the proposed algorithms under mild conditions, and extensive experiments show that under different types of dataset attacks, our algorithms outperform other benchmark ones. Finally, we apply our methods to a data-recovery problem in a real-world application involving a space solar array, demonstrating their good applicability.