2023/10/09 by Fan Wang, Yi Yu, Wang, Fan +1
Computer Science · #Distributed Sensor Networks and Detection Algorithms #Domain Adaptation and Few-Shot Learning #FOS: Computer and information sciences #FOS: Mathematics #Gaussian Processes and Bayesian Inference #Methodology (stat.ME) #Statistics Theory (math.ST)
paper · pdf · doi:10.48550/arxiv.2310.05646
openalex publication_date 2023/10/09 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We study transfer learning for estimating piecewise-constant signals when source data, which may be relevant but disparate, are available in addition to the target data. We first investigate transfer learning estimators that respectively employ ℓ1- and ℓ0-penalties for unisource data scenarios and then generalise these estimators to accommodate multisources. To further reduce estimation errors, especially when some sources significantly differ from the target, we introduce an informative source selection algorithm. We then examine these estimators with multisource selection and establish their minimax optimality. Unlike the common narrative in the transfer learning literature that the performance is enhanced through large source sample sizes, our approaches leverage higher observation frequencies and accommodate diverse frequencies across multiple sources. Our theoretical findings are supported by extensive numerical experiments, with the code available online, see https://github.com/chrisfanwang/transferlearning