Anomaly Detection with Variance Stabilized Density Estimation

Amit Rozner, Barak Battash, Henry Li, Lior Wolf, Ofir Lindenbaum

Keywords: Anomaly detection, Density estimation, Autoregresive model, Outlier detection

Abstract: We propose a modified density estimation problem that is highly effective for detecting anomalies in tabular data. Specifically, we hypothesize that the density function is more stable (with lower variance) around normal samples than anomalies. We first corroborate this hypothesis empirically using a wide range of real-world data. Then, we propose 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. Finally, we perform 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.

Primary Area: general machine learning (i.e., none of the above)

Code Of Ethics: I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics.

Submission Guidelines: I certify that this submission complies with the submission instructions as described on https://iclr.cc/Conferences/2024/AuthorGuide.

Anonymous Url: I certify that there is no URL (e.g., github page) that could be used to find authors' identity.

No Acknowledgement Section: I certify that there is no acknowledgement section in this submission for double blind review.

Submission Number: 5992