  Recent years have seen a surge of interest in the algorithmic estimation of
stochastic entropy production (EP) from trajectory data via machine learning. A
crucial element of such algorithms is the identification of a loss function
whose minimization guarantees the accurate EP estimation. In this study, we
show that there exists a host of loss functions, namely those implementing a
variational representation of the $\alpha$-divergence, which can be used for
the EP estimation. By fixing $\alpha$ to a value between $-1$ and $0$, the
$\alpha$-NEEP (Neural Estimator for Entropy Production) exhibits a much more
robust performance against strong nonequilibrium driving or slow dynamics,
which adversely affects the existing method based on the Kullback-Leibler
divergence ($\alpha = 0$). In particular, the choice of $\alpha = -0.5$ tends
to yield the optimal results. To corroborate our findings, we present an
exactly solvable simplification of the EP estimation problem, whose loss
function landscape and stochastic properties give deeper intuition into the
robustness of the $\alpha$-NEEP.
