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arXiv · 2404.09708

Kernel-based learning with guarantees for multi-agent applications

Krzysztof Kowalczyk·Paweł Wachel·Cristian R. Rojas
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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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Krzysztof Kowalczyk, Paweł Wachel, Cristian R. Rojas. 2024-04-15. Kernel-based learning with guarantees for multi-agent applications. https://arxiv.org/abs/2404.09708

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