Kernel-based learning with guarantees for multi-agent applications

Krzysztof Kowalczyk, Paweł Wachel, Cristian R. Rojas
4/15/2024
cs.MAcs.LG

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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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Cite this paper

@article{kowalczyk2024kernelbased,
  title  = {Kernel-based learning with guarantees for multi-agent applications},
  author = {Krzysztof Kowalczyk and Paweł Wachel and Cristian R. Rojas},
  year   = {2024},
  eprint = {2404.09708},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2404.09708v1}
}

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