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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.
A computationally lightweight safe learning algorithm
Unfortunately, Gaussian process inference scales cubically with the number of data points, limiting applicability to high-dimensional and embedded systems.
Decentralized diffusion-based learning under non-parametric limited prior knowledge
We study the problem of diffusion-based network learning of a nonlinear phenomenon, $m$, from local agents' measurements collected in a noisy environment.
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