# Kernel-based learning with guarantees for multi-agent applications

arXiv: https://arxiv.org/abs/2404.09708 · HTML: https://mlsanity.com/paper/2404.09708/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 14 references that are in the map.
- First posted on arXiv 15 Apr 2024.

## Builds on (most cited first, 1 of 1)

- [Decentralized diffusion-based learning under non-parametric limited prior knowledge](https://mlsanity.com/paper/2305.03295/) (2023)

## Built on by (most cited first, 1 of 1)

- [Computationally lightweight classifiers with frequentist bounds on predictions](https://mlsanity.com/paper/2603.22128/) (2026)

## Abstract (arXiv)

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.