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Wasserstein-Splitting Gaussian Process Regression for Heterogeneous Online Bayesian Inference
Gaussian processes (GPs) are a well-known nonparametric Bayesian inference technique, but they suffer from scalability problems for large sample sizes, and their performance can degrade for non-stationary or spatially heterogeneous data.
Strictly Decentralized Adaptive Estimation of External Fields using Reproducing Kernels
Approximations of the evolution of the ideal local estimate $\hat{g}^i_t$ of agent $i$ is constructed solely using observations made by agent $i$ on a fine time scale.
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