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This research aims to develop a Bayesian hierarchical framework for enhancing multi-output Gaussian processes in transfer learning contexts.

A Bayesian Hierarchical Framework for Non-linear and Iterative Transfer Learning of Gaussian Process

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This research aims to develop a Bayesian hierarchical framework for enhancing multi-output Gaussian processes in transfer learning contexts.
Proposes a three-layer Bayesian hierarchical architecture for Gaussian processes.
Models shared hyper-parameters as random variables to update asymmetrically.
Evaluates performance through synthetic data and real-world case studies in battery diagnostics and nano-sensor design.
The proposed method outperforms single-output and hierarchical Gaussian process baselines in accuracy and computational efficiency.
Demonstrated significant improvements under data-scarce and heterogeneous conditions.

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Randomized trial demonstrates improved accuracy and efficiency in transfer learning for complex data scenarios, suggesting a scalable solution.

Hu et al. (2026) studied this question.

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Randomized trial demonstrates improved accuracy and efficiency in transfer learning for complex data scenarios, suggesting a scalable solution.