A Coreset-based, Tempered Variational Posterior for Accurate and Scalable Stochastic Gaussian Process Inference. (arXiv:2311.01409v1 [cs.LG])

We present a novel stochastic variational Gaussian process ($\mathcal{GP}$)
inference method, based on a posterior over a learnable set of weighted pseudo
input-output points (coresets). Instead of a free-form variational family, the
proposed coreset-based, variational tempered family for $\mathcal{GP}$s (CVTGP)
is defined in terms of the $\mathcal{GP}$ prior and the data-likelihood; hence,
accommodating the modeling inductive biases. We derive CVTGP's lower bound for
the log-marginal likelihood via marginalization of the proposed posterior over
latent $\mathcal{GP}$ coreset variables, and show it …

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