Robust, Online, and Adaptive Decentralized Gaussian Processes
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866912599683104768 |
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| author | Llorente, Fernando Waxman, Daniel Jantre, Sanket Urban, Nathan M. Minkoff, Susan E. |
| author_facet | Llorente, Fernando Waxman, Daniel Jantre, Sanket Urban, Nathan M. Minkoff, Susan E. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_18011 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Robust, Online, and Adaptive Decentralized Gaussian Processes Llorente, Fernando Waxman, Daniel Jantre, Sanket Urban, Nathan M. Minkoff, Susan E. Machine Learning Multiagent Systems Signal Processing 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. |
| title | Robust, Online, and Adaptive Decentralized Gaussian Processes |
| topic | Machine Learning Multiagent Systems Signal Processing |
| url | https://arxiv.org/abs/2509.18011 |