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October 15, 2025Open Access

A Framework for Nonstationary Gaussian Processes with Neural Network Parameters

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Framework introduces nonstationary kernels using flexible neural network parameters, improving accuracy in modeling datasets.

The framework enhances gaussian processes by incorporating nonstationary kernels for increased flexibility.
Joint training of the neural network and gaussian process achieves higher accuracy than stationary and hierarchical models.
Utilizing GPyTorch, the proposed method easily adapts to various nonstationary kernels without complicating the optimization process.
Empirical tests show that the nonstationary approach surpasses traditional methods in accuracy and log-score across multiple datasets.

James et al. (2025) studied this question.

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