  There have been rapid developments in the direct calculation in lattice QCD
(LQCD) of the Bjorken-$x$ dependence of hadron structure through large-momentum
effective theory (LaMET). LaMET overcomes the previous limitation of LQCD to
moments (that is, integrals over Bjorken-$x$) of hadron structure, allowing
LQCD to directly provide the kinematic regions where the experimental values
are least known. LaMET requires large-momentum hadron states to minimize its
systematics and allow us to reach small-$x$ reliably. This means that very fine
lattice spacing to minimize lattice artifacts at order $(P_z a)^n$ will become
crucial for next-generation LaMET-like structure calculations. Furthermore,
such calculations require operators with long Wilson-link displacements (in
finer lattice units), increasing the communication costs relative to that of
the propagator inversion. In this work, we explore whether machine-learning
(ML) algorithms can make correlator predictions to reduce the computational
cost of these LQCD calculations. We consider two algorithms, gradient-boosting
decision tree and linear models, applied to LaMET data, the matrix elements
needed to determine the kaon and $\eta_s$ unpolarized parton distribution
functions (PDFs), meson distribution amplitude (DA), and the nucleon gluon PDF.
We find that both algorithms can reliably predict the target observables with
different fit quality and systematic errors. The predictions from smaller
displacement $z$ to larger ones work better than those for momentum $p$ due to
the higher correlation among the data.
