分布式多智能体系统中实现精准高效的高斯过程集成学习
Decentralized Online Ensembles of Gaussian Processes for Multi-Agent Systems
- 提出全分布式高斯过程随机特征近似算法，保证渐近精确
- 通过在线贝叶斯模型平均实现核函数超参数自适应选择
- 适合需要实时、去中心化建模的多智能体场景
灵活且可扩展的去中心化学习方案在多智能体系统应用中至关重要。尽管近期已有若干方法将核机器（包括集成）引入分布式环境，但贝叶斯方法仍相对有限。本文提出一种完全去中心化的、渐近精确的高斯过程随机特征近似计算方案。同时，通过基于在线贝叶斯模型平均的集成策略，解决超参数选择问题。所提算法在模拟数据和真实数据集上与贝叶斯及频率派方法进行了对比测试，验证了其有效性与鲁棒性。
原文摘要 · Abstract (English)
Flexible and scalable decentralized learning solutions are fundamentally important in the application of multi-agent systems. While several recent approaches introduce (ensembles of) kernel machines in the distributed setting, Bayesian solutions are much more limited. We introduce a fully decentralized, asymptotically exact solution to computing the random feature approximation of Gaussian processes. We further address the choice of hyperparameters by introducing an ensembling scheme for Bayesian multiple kernel learning based on online Bayesian model averaging. The resulting algorithm is tested against Bayesian and frequentist methods on simulated and real-world datasets.
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