Robust, Online, and Adaptive Decentralized Gaussian Processes

• URL: http://arxiv.org/abs/2509.18011v1
• Date: Mon, 22 Sep 2025 16:49:49 GMT
• Title: Robust, Online, and Adaptive Decentralized Gaussian Processes
• Authors: Fernando Llorente, Daniel Waxman, Sanket Jantre, Nathan M. Urban, Susan E. Minkoff,
• Abstract summary: Decentralized random Fourier feature Gaussian processes (DRFGP) is an online and distributed algorithm that casts GPs in an information-filter form.<n>We extend DRFGP along two key directions: first, by introducing a robust-filtering update that downweights the impact of atypical observations; second, by incorporating a dynamic adaptation mechanism that adapts to time-varying functions.<n>We demonstrate its effectiveness on a large-scale Earth system application, underscoring its potential for in-situ modeling.
• Score: 40.250871864353314
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Gaussian processes (GPs) offer a flexible, uncertainty-aware framework for modeling complex signals, but scale cubically with data, assume static targets, and are brittle to outliers, limiting their applicability in large-scale problems with dynamic and noisy environments. Recent work introduced decentralized random Fourier feature Gaussian processes (DRFGP), an online and distributed algorithm that casts GPs in an information-filter form, enabling exact sequential inference and fully distributed computation without reliance on a fusion center. In this paper, we extend DRFGP along two key directions: first, by introducing a robust-filtering update that downweights the impact of atypical observations; and second, by incorporating a dynamic adaptation mechanism that adapts to time-varying functions. The resulting algorithm retains the recursive information-filter structure while enhancing stability and accuracy. We demonstrate its effectiveness on a large-scale Earth system application, underscoring its potential for in-situ modeling.

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