Kernel-based learning with guarantees for multi-agent applications

2024/04/15 by Krzysztof Kowalczyk, Kowalczyk, Krzysztof, Paweł Wachel +3 · 1 citation
Computer Science · #FOS: Computer and information sciences #Machine Learning (cs.LG) #Multiagent Systems (cs.MA) #Neural Networks and Applications

paper · pdf · doi:10.48550/arxiv.2404.09708

openalex publication_date 2024/04/15 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

This paper addresses a kernel-based learning problem for a network of agents locally observing a latent multidimensional, nonlinear phenomenon in a noisy environment. We propose a learning algorithm that requires only mild a priori knowledge about the phenomenon under investigation and delivers a model with corresponding non-asymptotic high probability error bounds. Both non-asymptotic analysis of the method and numerical simulation results are presented and discussed in the paper.

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