# An Efficient Variant of One-Class SVM with Lifelong Online Learning Guarantees

arXiv: https://arxiv.org/abs/2512.11052 · HTML: https://mlsanity.com/paper/2512.11052/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 38 references that are in the map.
- First posted on arXiv 11 Dec 2025.

## Builds on (most cited first, 8 of 8)

- [Strongly Adaptive Online Learning](https://mlsanity.com/paper/1502.07073/) (2015)
- [Tight Analyses for Non-Smooth Stochastic Gradient Descent](https://mlsanity.com/paper/1812.05217/) (2018)
- [On the Error of Random Fourier Features](https://mlsanity.com/paper/1506.02785/) (2015)
- [Machine learning for Internet of Things data analysis: A survey](https://mlsanity.com/paper/1802.06305/) (2018)
- [Probabilistic smallest enclosing ball in high dimensions via subgradient sampling](https://mlsanity.com/paper/1902.10966/) (2019)
- [Transfer Neyman-Pearson Algorithm for Outlier Detection](https://mlsanity.com/paper/2501.01525/) (2025)
- [Tight Rates in Supervised Outlier Transfer Learning](https://mlsanity.com/paper/2310.04686/) (2023)
- [Neyman-Pearson Classification under Both Null and Alternative Distributions Shift](https://mlsanity.com/paper/2511.06641/) (2025)

## Abstract (arXiv)

We study outlier (a.k.a., anomaly) detection for single-pass non-stationary streaming data. In the well-studied offline or batch outlier detection problem, traditional methods such as kernel One-Class SVM (OCSVM) are both computationally heavy and prone to large false-negative (Type II) errors under non-stationarity. To remedy this, we introduce SONAR, an efficient SGD-based OCSVM solver with strongly convex regularization. We show novel theoretical guarantees on the Type I/II errors of SONAR, superior to those known for OCSVM, and further prove that SONAR ensures favorable lifelong learning guarantees under benign distribution shifts. In the more challenging problem of adversarial non-stationary data, we show that SONAR can be used within an ensemble method and equipped with changepoint detection to achieve adaptive guarantees, ensuring small Type I/II errors on each phase of data. We validate our theoretical findings on synthetic and real-world datasets.