% !TEX root = ../main.tex
We introduced CVGP,
the first $\gp$ inference method that
leverages a coreset-based, variational posterior
for accurate and scalable $\gp$ inference.
CVGP enables stochastic optimization of
its variational lower-bound to the $\gp$'s marginal log-likelihood,
after marginalization of latent $\gp$ variables,
at reduced $\bigO{M}$ parameter complexity,
with $\bigO{M^3}$ time- and $\bigO{M^2}$ space-requirements.

% Results
Experimental results demonstrate that CVGP provides improved inference and predictive capabilities,
outperforming stochastic variational inference-based alternatives.
%
CVGP provides a high-quality $\gp$ posterior approximation that
effectively interpolates between the $\gp$ prior and the data likelihood,
learned via an efficient lower-dimensional stochastic optimization problem
that results in CVGP achieving a tighter lower-bound than stochastic variational alternatives.
Overall, CVGP's coreset-based posterior
accurately approximates the true $\gp$ posterior,
providing a sparse and explainable representation of the $\gp$ posterior,
with added flexibility to adjust (and discard) pseudo-input/output pairs.

% Limitation/future-work
Building upon CVGP's formulation for $\gp$ regression,
we embark on follow-up work
with other data-likelihoods
(\eg for $\gp$-based classification),
and envision methods that leverage the data-compression benefits of CVGP's coreset-based posterior.