Title: A Variance Reduction Method for Neural-based Divergence Estimation. Abstract: A central problem in machine learning is the computation of similarity or closeness between two (data) distributions. The applications span from generative modelling via adversarial training, representation learning, and robustness in out-of-distribution settings, to name a few. A palette of divergences, mutual information, and integral probability metrics are indispensable tools for measuring the ``distance'' between distributions and these are made tractable in high dimensional settings through variational representation formulas. Indeed, such formulas transform an estimation problem into an optimization problem. Unfortunately, the approximation of expectations that are inherent in variational formulas by statistical averages can be problematic due to high statistical variance, e.g., exponential for the Kullback-Leibler divergence and certain estimators. In this paper, we propose a new variance penalty term that acts directly on the variance of each component of the statistical estimator. The power of the variance penalty is controlled by a penalty coefficient which trades off bias and variance.  We tested the proposed approach on several variational formulas and synthetic examples and showed that the overall error is decreased about an order of magnitude relative to the baseline statistical estimator. Impressive results are obtained for R\'enyi divergence with large order values due to the improved stability of the proposed estimator. Furthermore, in real biological datasets we are able to detect very rare sub-populations with a moderate sample size. Finally, we obtain improved (in terms of objective measures) disentangled representation of speech signals into text, speaker, and style components via variance-penalized mutual information minimization.