Kernel-based learning with guarantees for multi-agent applications
<|reference_start|>Kernel-based learning with guarantees for multi-agent applications: 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.<|reference_end|>
@article{kowalczyk2024kernel-based,
    title={Kernel-based learning with guarantees for multi-agent applications},
    author={Krzysztof Kowalczyk, Pawe{l} Wachel and Cristian R. Rojas},
    journal={arXiv preprint arXiv:2404.09708},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.09708},
    primaryClass={cs.MA cs.LG}
}