Kernel-based learning with guarantees for multi-agent applications

arXiv:2404.09708v1 Announce Type: cross
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.

abstract agent agents algorithm applications arxiv cs.lg cs.ma environment investigation kernel knowledge multi-agent multidimensional network paper type