Transfer Learning of Stochastic Kriging for Individualized Prediction

Jinwei Yao; Jianguo Wu; Yongxiang Li; Chao Wang

doi:10.1109/TPAMI.2025.3607773

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Transfer Learning of Stochastic Kriging for Individualized Prediction

Journal article

Peer reviewed

Transfer Learning of Stochastic Kriging for Individualized Prediction

Jinwei Yao, Jianguo Wu, Yongxiang Li and Chao Wang

IEEE transactions on pattern analysis and machine intelligence, Vol.48(1), pp.527-541

01/2026

DOI: 10.1109/TPAMI.2025.3607773

PMID: 40924512

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Abstract

Stochastic Kriging (SK) is a generalized variant of Gaussian process regression, and it is developed for dealing with non-i.i.d. noise in functional responses. Although SK has achieved substantial success in various engineering applications, its intrinsic modeling strategy by focusing on the sample mean limits its flexibility and capability of predicting individual functional samples. Moreover, the performance of SK can be impaired under scarce data scenarios, which are commonly encountered in engineering applications, especially for start-up or just deployed systems. In this paper, we propose a novel transfer learning framework to address the challenges of individualization and data scarcity in traditional SK. The proposed framework features a within-process model to facilitate individualized prediction and a between-process model to leverage information from related processes for resolving the issue of data scarcity. The within- and between-process models are integrated through a tailored convolution process, which quantifies interactions within and between processes using a specially designed covariance matrix and corresponding kernel parameters. Statistical properties are investigated on the parameter estimation of the proposed framework, which provide theoretical guarantees for the performance of transfer learning. The proposed method is compared with benchmark methods through various numerical and real case studies, and the results demonstrate the superiority of the proposed method in dealing with individualized prediction of functional responses, especially when limited data are available in the process of interest.

Transfer learning

Correlation

Covariance matrices

Noise

Batteries

Market research

Convolution

Predictive models

Kernel

Data models

Gaussian process

stochastic kriging

transfer learning

non-i.i.d. noise

Details

Title: Subtitle: Transfer Learning of Stochastic Kriging for Individualized Prediction
Creators: Jinwei Yao - University of Iowa
Jianguo Wu - Peking University
Yongxiang Li - Shanghai Jiao Tong University
Chao Wang - University of Iowa
Resource Type: Journal article
Publication Details: IEEE transactions on pattern analysis and machine intelligence, Vol.48(1), pp.527-541
DOI: 10.1109/TPAMI.2025.3607773
PMID: 40924512
NLM abbreviation: IEEE Trans Pattern Anal Mach Intell
ISSN: 0162-8828
eISSN: 2160-9292
Publisher: IEEE; LOS ALAMITOS
Number of pages: 15
Grant note: National Science Foundation (NSF): CMMI-2436025
This work was supported by the National Science Foundation (NSF) under Grant CMMI-2436025.
Language: English
Electronic publication date: 09/09/2025
Date published: 01/2026
Academic Unit: Industrial and Systems Engineering
Record Identifier: 9984962543402771

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