Model positive and unlabeled data with a generalized additive density ratio model

主讲人：桑培俊

桑培俊, 加拿大滑铁卢大学统计与精算系担任副教授。在Annals of Statistics, Biometrika, JASA, Journal of Machine Learning Research, Journal of Econometrics等杂志发表文章。主要研究方向是函数型数据和正样本和无标记数据的半监督学习（PU Learning），尤其是函数型数据分析回归模型中的统计推断问题以及 PU Learning 的半参模型。 目前担任The American Statistician 和 Statistics and Computing 的副主编。

1  报告信息

时间 

2026年7月6日(周一)

16:30

地点 

中国人民大学中关村校区明德主楼1016

2  报告摘要

This paper focuses on learning from positive and unlabeled (PU) data, where only some positives are labeled and the rest are mixed with negatives. Classical exponential tilting models guarantee identifiability by imposing a linear structure, but they can be severely misspecified when the true relationships are nonlinear. We propose a generalized additive density-ratio framework that retains identifiability while allowing nonlinear and feature-specific effects. The approach comes with a practical fitting algorithm and supporting theory that enable estimation and inference for the mixture proportion and other quantities of interest. In simulations and analyses of real datasets, the proposed method matches the standard exponential tilting method when the linear model is correct and delivers clear gains when it is not. Overall, the framework strikes a useful balance between flexibility and interpretability for PU data and provides principled tools for estimation, prediction, and uncertainty assessment.