Recurrent Memory for Online Interdomain Gaussian Processes

• URL: http://arxiv.org/abs/2502.08736v2
• Date: Sat, 12 Apr 2025 21:46:53 GMT
• Title: Recurrent Memory for Online Interdomain Gaussian Processes
• Authors: Wenlong Chen, Naoki Kiyohara, Harrison Bo Hua Zhu, Yingzhen Li,
• Abstract summary: We propose a novel online Gaussian process (GP) model that is capable of capturing long-term memory in sequential data in an online regression setting.<n>Our model, Online HiPPO Sparse Variational Gaussian Process Regression (OHSGPR), leverages the HiPPO framework, which is popularized in the RNN domain due to its long-range memory modeling capabilities.<n>We show that the HiPPO framework fits naturally into the interdomain GP framework and demonstrate that the kernel matrices can also be updated online in a recurrence form based on the ODE evolution of HiPPO.
• Score: 18.91108540244912
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: We propose a novel online Gaussian process (GP) model that is capable of capturing long-term memory in sequential data in an online regression setting. Our model, Online HiPPO Sparse Variational Gaussian Process Regression (OHSGPR), leverages the HiPPO (High-order Polynomial Projection Operators) framework, which is popularized in the RNN domain due to its long-range memory modeling capabilities. We interpret the HiPPO time-varying orthogonal projections as inducing variables with time-dependent orthogonal polynomial basis functions, which allows the SGPR inducing points to memorize the process history. We show that the HiPPO framework fits naturally into the interdomain GP framework and demonstrate that the kernel matrices can also be updated online in a recurrence form based on the ODE evolution of HiPPO. We evaluate our method on time series regression tasks, showing that it outperforms the existing online GP method in terms of predictive performance and computational efficiency

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