# Hyperparameter Optimization for Unsupervised Outlier Detection

arXiv: https://arxiv.org/abs/2208.11727 · HTML: https://mlsanity.com/paper/2208.11727/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 67 references that are in the map.
- First posted on arXiv 24 Aug 2022.

## Builds on (most cited first, 9 of 9)

- [BOHB: Robust and Efficient Hyperparameter Optimization at Scale](https://mlsanity.com/paper/1807.01774/) (2018)
- [Meta-Learning: A Survey](https://mlsanity.com/paper/1810.03548/) (2018)
- [ADBench: Anomaly Detection Benchmark](https://mlsanity.com/paper/2206.09426/) (2022)
- [Hyperparameter Ensembles for Robustness and Uncertainty Quantification](https://mlsanity.com/paper/2006.13570/) (2020)
- [AutoML to Date and Beyond: Challenges and Opportunities](https://mlsanity.com/paper/2010.10777/) (2020)
- [How to Evaluate the Quality of Unsupervised Anomaly Detection Algorithms?](https://mlsanity.com/paper/1607.01152/) (2016)
- [Hyperparameter Sensitivity in Deep Outlier Detection: Analysis and a Scalable Hyper-Ensemble Solution](https://mlsanity.com/paper/2206.07647/) (2022)
- [PyODDS: An End-to-end Outlier Detection System with Automated Machine Learning](https://mlsanity.com/paper/2003.05602/) (2020)
- [A Large-scale Study on Unsupervised Outlier Model Selection: Do Internal Strategies Suffice?](https://mlsanity.com/paper/2104.01422/) (2021)

## Built on by (most cited first, 4 of 4)

- [ClimateLLM: Efficient Weather Forecasting via Frequency-Aware Large Language Models](https://mlsanity.com/paper/2502.11059/) (2025)
- [Towards Automated Self-Supervised Learning for Truly Unsupervised Graph Anomaly Detection](https://mlsanity.com/paper/2501.14694/) (2025)
- [UniOD: A Universal Model for Outlier Detection across Diverse Domains](https://mlsanity.com/paper/2507.06624/) (2025)
- [Automated Machine Learning for Unsupervised Tabular Tasks](https://mlsanity.com/paper/2510.07569/) (2025)

## Abstract (arXiv)

Given an unsupervised outlier detection (OD) algorithm, how can we optimize its hyperparameter(s) (HP) on a new dataset, without any labels? In this work, we address this challenging hyperparameter optimization for unsupervised OD problem, and propose the first systematic approach called HPOD that is based on meta-learning. HPOD capitalizes on the prior performance of a large collection of HPs on existing OD benchmark datasets, and transfers this information to enable HP evaluation on a new dataset without labels. Moreover, HPOD adapts a prominent sampling paradigm to identify promising HPs efficiently. Extensive experiments show that HPOD works with both deep (e.g., Robust AutoEncoder) and shallow (e.g., Local Outlier Factor (LOF) and Isolation Forest (iForest)) OD algorithms on discrete and continuous HP spaces, and outperforms a wide range of baselines with on average 58% and 66% performance improvement over the default HPs of LOF and iForest.