2024-10-09-zhao24a.md

title	software	openreview	abstract	layout	series	publisher	issn	id	month	tex_title	firstpage	lastpage	page	order	cycles	bibtex_author	author	date	address	container-title	volume	genre	issued	pdf	extras
HPOD: Hyperparameter Optimization for Unsupervised Outlier Detection	https://github.com/review2024/HPOD	pypP5uaHxg	Given an unsupervised outlier detection (OD) algorithm, how can we optimize its hyperparameter(s) (HP) on a new dataset, without using any labels? In this work, we address this challenging hyperparameter optimization for unsupervised OD problem, and propose the first continuous HP search method called HPOD. It 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. Also, HPOD adapts a prominent, (originally) supervised, sampling paradigm to efficiently identify promising HPs in iterations. Extensive experiments show that HPOD works for both deep (e.g., Robust AutoEncoder (RAE)) and shallow (e.g., Local Outlier Factor (LOF) and Isolation Forest (Forest)) algorithms on discrete and continuous HP spaces. HPOD outperforms a wide range of diverse baselines with 37% improvement on average over the minimal loss HPs of RAE, and 58% and 66% improvement on average over the default HPs of LOF and iForest.	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	zhao24a	0	HPOD: Hyperparameter Optimization for Unsupervised Outlier Detection	2/1	24	2/1-24	2	false	Zhao, Yue and Akoglu, Leman	given family Yue Zhao given family Leman Akoglu	2024-10-09		Proceedings of the Third International Conference on Automated Machine Learning	256	inproceedings	date-parts 2024 10 9	https://raw.githubusercontent.com/mlresearch/v256/main/assets/zhao24a/zhao24a.pdf