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.
@article{arxiv.2404.09708,
title = {Kernel-based learning with guarantees for multi-agent applications},
author = {Krzysztof Kowalczyk and Paweł Wachel and Cristian R. Rojas},
journal= {arXiv preprint arXiv:2404.09708},
year = {2024}
}