{"ID":2867839,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.18011","arxiv_id":"2509.18011","title":"Robust, Online, and Adaptive Decentralized Gaussian Processes","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.","short_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 Fo...","url_abs":"https://arxiv.org/abs/2509.18011","url_pdf":"https://arxiv.org/pdf/2509.18011v1","authors":"[\"Fernando Llorente\",\"Daniel Waxman\",\"Sanket Jantre\",\"Nathan M. Urban\",\"Susan E. Minkoff\"]","published":"2025-09-22T16:49:49Z","proceeding":"stat.ML","tasks":"[\"stat.ML\",\"cs.LG\",\"cs.MA\",\"eess.SP\"]","methods":"[]","has_code":false}