让高斯过程适应动态噪声环境，实现稳定在线建模。
Robust, Online, and Adaptive Decentralized Gaussian Processes
- 用信息滤波框架在线更新，支持分布式计算。
- 对异常值自动降权，提升抗噪能力。
- 可自适应变化函数，适合实时动态系统。
高斯过程（GPs）虽能灵活建模复杂信号并提供不确定性估计，但存在计算复杂度随数据量立方增长、假设目标静态、对异常值敏感等局限，难以应用于大规模动态噪声环境。近期提出的分布式随机傅里叶特征高斯过程（DRFGP）通过信息滤波形式实现了在线、分布式精确序列推断，无需中心融合节点。本文在此基础上提出两个关键改进：一是引入鲁棒滤波更新机制，降低异常观测的影响；二是加入动态自适应机制，以跟踪时变函数。新算法保持递归信息滤波结构，同时提升稳定性与准确性。在大规模地球系统应用中验证了其有效性，展现出原位建模的巨大潜力。
原文摘要 · Abstract (English)
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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