时 间：2025年8月2日（星期六）10:00 - 11:00
地 点：普陀校区理科大楼A1514
报告人：张婉 中国科学院数学与系统科学研究院助理研究员
主持人：周勇 华东师范大学教授
摘 要：
As the complexity of models and the volume of data increase, interpretable methods for modeling complicated dependence are in great need. A recent framework of binary expansion linear effect (BELIEF) provides a “divide and conquer'” approach to decompose any complex form of dependency into small linear regressions over data bits. Although BELIEF can be used to approximate any relationship, it faces an important challenge of high dimensionality. To overcome this obstacle, we propose a novel definition of smoothness for binary interactions through an interesting connection to the sequency of Walsh functions. We investigate this connection and study related theory and algorithms. Based on this connection, we develop a regularization of BELIEF under smoothness interpretations. Specifically, we propose the sequency Lasso, a generalized Lasso model that imposes larger penalties on less smooth terms to model smooth form of dependency. The proposed method yields a highly competitive yet interpretable machine learning tool. Numerical studies demonstrate that the sequency Lasso has advantages in clear interpretability and effectiveness for nonlinear and high dimensional data.
报告人简介：
张婉，中国科学院数学与系统科学研究院预测科学研究中心助理研究员。2024年于北卡罗来纳大学教堂山分校获得统计学博士学位。主要研究方向为非参数统计，高维数据分析与特征选择，可解释机器学习等。她的研究工作发表在Journal of Business & Economic Statistics、The Annals of Applied Statistics等。