# Collective Outlier Detection and Enumeration with Conformalized Closed Testing

arXiv: https://arxiv.org/abs/2308.05534 · HTML: https://mlsanity.com/paper/2308.05534/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Aug 2023.

## Built on by (most cited first, 2 of 2)

- [Elements of Conformal Prediction](https://mlsanity.com/paper/2603.23923/) (2026)
- [Structure-Adaptive Conformal Inference for Large-Scale Out-of-Distribution Testing](https://mlsanity.com/paper/2605.26429/) (2026)

## Abstract (arXiv)

This paper develops a flexible distribution-free method for collective outlier detection and enumeration, designed for situations in which the presence of outliers can be detected powerfully even though their precise identification may be challenging due to the sparsity, weakness, or elusiveness of their signals. This method builds upon recent developments in conformal inference and integrates classical ideas from other areas, including multiple testing, locally most powerful and adaptive rank tests, and non-parametric large-sample asymptotics. The key innovation lies in developing a principled and effective approach for automatically choosing the most appropriate machine learning classifier and two-sample testing procedure for a given data set. The performance of our method is investigated through extensive empirical demonstrations, including an analysis of the LHCO high-energy particle collision data set.