报告题目：Regularizing High Dimensional Models of Dependence over Data Bits
内容摘要：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 the connection, we create a regularization of BELIEF under smoothness interpretations. In particular, we propose to model smooth forms of dependency with a generalized lasso model, which we call the sequency lasso, with larger penalty on less smooth terms. The numerical studies demonstrate that the proposed sequency lasso has advantages in clear interpretability and effectiveness for nonlinear and high dimensional data.
主讲人简介：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，深度学习理论等。她的研究工作发表在《Journal of Business & Economic Statistics》等。
报告时间：2024年11月19日(周二)，16:30-18:00
线下地点：厦门大学经济楼N302
线上地点：腾讯会议 ID：942 539 081
