  Next-generation large-scale structure surveys will deliver a significant
increase in the precision of growth data, allowing us to use `agnostic' methods
to study the evolution of perturbations without the assumption of a
cosmological model. We focus on a particular machine learning tool, Gaussian
processes, to reconstruct the growth rate $f$, the root mean square of matter
fluctuations $\sigma_8$, and their product $f\sigma_8$. We apply this method to
simulated data, representing the precision of upcoming Stage IV galaxy surveys.
We extend the standard single-task approach to a multi-task approach that
reconstructs the three functions simultaneously, thereby taking into account
their inter-dependence. We find that this multi-task approach outperforms the
single-task approach for future surveys and will allow us to detect departures
from the standard model with higher significance. By contrast, the limited
sensitivity of current data severely hinders the use of agnostic methods, since
the Gaussian processes parameters need to be fine tuned in order to obtain
robust reconstructions.
