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聚合投影法：组因子模型的新方法AI 解读

Identifying the global factors among grouped data is crucial in the group factor model. In this paper, we propose a novel objective function for the task by maximizing the average of correlations between the latent global factors and group factors, solved through the eigen-decomposition of the aggregated projection matrix. Our method is not only computationally efficient but also robust to strongly correlated local factors. We establish the consistency of the global/local…

函数型分类的自适应迁移学习框架AI 解读

In this article, we study the transfer learning problem in functional classification, aiming to improve the classification accuracy of the target data by leveraging information from related source datasets. To facilitate transfer learning, we propose a novel transferability function tailored for classification problems, enabling a more accurate evaluation of the similarity between source and target dataset distributions. Interestingly, we find that a source dataset can offer…

动态条件分位数处理效应的评估及网约车应用AI 解读

Many modern tech companies, such as Google, Uber, and Didi, utilize online experiments (also known as A/B testing) to evaluate new policies against existing ones. While most studies concentrate on average treatment effects, situations with skewed and heavy-tailed outcome distributions may benefit from alternative criteria, such as quantiles. However, assessing dynamic quantile treatment effects (QTE) remains a challenge, particularly when dealing with data from ride-sourcing…

结合社交网络信息的协同过滤AI 解读

In this article, we present the NetRec method to leverage the social network data of users in collaborative filtering. We formulate two new network-related terms and obtain convex optimization problems that incorporate assumptions regarding users’ social connections and preferences about products. Our theory demonstrates that this procedure leads to a sharper error bound than before, as long as the observed social network is well structured. We point out that the larger the…

异步纵向函数型与标量数据的回归分析AI 解读

Many modern large-scale longitudinal neuroimaging studies, such as the Alzheimer’s Disease Neuroimaging Initiative (ADNI) study, have collected/are collecting asynchronous scalar and functional variables that are measured at distinct time points. The analyses of temporally asynchronous functional and scalar variables pose major technical challenges to many existing statistical approaches. We propose a class of generalized functional partial-linear varying-coefficient models…