  Cosmic voids are the largest and most underdense structures in the Universe.
Their properties have been shown to encode precious information about the laws
and constituents of the Universe. We show that machine learning techniques can
unlock the information in void features for cosmological parameter inference.
We rely on thousands of void catalogs from the GIGANTES dataset, where every
catalog contains an average of 11,000 voids from a volume of $1~(h^{-1}{\rm
Gpc})^3$. We focus on three properties of cosmic voids: ellipticity, density
contrast, and radius. We train 1) fully connected neural networks on histograms
from individual void properties and 2) deep sets from void catalogs, to perform
likelihood-free inference on the value of cosmological parameters. We find that
our best models are able to constrain the value of $\Omega_{\rm m}$,
$\sigma_8$, and $n_s$ with mean relative errors of $10\%$, $4\%$, and $3\%$,
respectively, without using any spatial information from the void catalogs. Our
results provide an illustration for the use of machine learning to constrain
cosmology with voids.
