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Testing sufficiency for transfer learning

题目Testing sufficiency for transfer learning
作者Lin, Ziqian; Gao, Yuan; Wang, Feifei; Wang, Hansheng
作者单位Peking Univ, Guanghua Sch Management, Beijing, Peoples R China Renmin Univ China, Ctr Appl Stat, Beijing, Peoples R China Renmin Univ China, Sch Stat, Beijing, Peoples R China
关键词：DIVERGING NUMBER LIKELIHOOD PARAMETERS
时间：2025年3月1日
出版者：COMPUTATIONAL STATISTICS & DATA ANALYSIS
摘要Modern statistical analysis often encounters high dimensional models but with limited sample sizes. This makes it difficult to estimate high-dimensional statistical models based on target data with limited sample size. Then how to borrow information from another large sized source data for more accurate target model estimation becomes an interesting problem. This leads to the useful idea of transfer learning. Various estimation methods in this regard have been developed recently. In this work, we study transfer learning from a different perspective. Specifically, we consider here the problem of testing for transfer learning sufficiency. We denote transfer learning sufficiency to be the null hypothesis. It refers to the situation that, with the help of the source data, the useful information contained in the feature vectors of the target data can be sufficiently extracted for predicting the interested target response. Therefore, the rejection of the null hypothesis implies that information useful for prediction remains in the feature vectors of the target data and thus calls for further exploration. To this end, we develop a novel testing procedure and a centralized and standardized test statistic, whose asymptotic null distribution is analytically derived. Simulation studies are presented to demonstrate the finite sample performance of the proposed method. A deep learning related real data example is presented for illustration purpose.
URLhttp://hdl.handle.net/20.500.11897/725758
ISSN0167-9473
DOI10.1016/j.csda.2024.108075
收录情况SCI（E）
分类人工智能
作者单位 Peking Univ, Guanghua Sch Management, Beijing, Peoples R China Renmin Univ China, Ctr Appl Stat, Beijing, Peoples R China Renmin Univ China, Sch Stat, Beijing, Peoples R China 时间 2025年3月1日
出版者 COMPUTATIONAL STATISTICS & DATA ANALYSIS URL http://hdl.handle.net/20.500.11897/725758
ISSN 0167-9473 DOI 10.1016/j.csda.2024.108075
收录情况 SCI（E） 分类 人工智能
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