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

$\alpha$-divergence Improves the Entropy Production Estimation via Machine Learning