  We propose a modified density estimation problem that is highly effective for
detecting anomalies in tabular data. Our approach assumes that the density
function is relatively stable (with lower variance) around normal samples. We
have verified this hypothesis empirically using a wide range of real-world
data. Then, we present a variance-stabilized density estimation problem for
maximizing the likelihood of the observed samples while minimizing the variance
of the density around normal samples. To obtain a reliable anomaly detector, we
introduce a spectral ensemble of autoregressive models for learning the
variance-stabilized distribution. We have conducted an extensive benchmark with
52 datasets, demonstrating that our method leads to state-of-the-art results
while alleviating the need for data-specific hyperparameter tuning. Finally, we
have used an ablation study to demonstrate the importance of each of the
proposed components, followed by a stability analysis evaluating the robustness
of our model.
