# Target Strangeness: A Novel Conformal Prediction Difficulty Estimator

arXiv: https://arxiv.org/abs/2410.19077 · HTML: https://mlsanity.com/paper/2410.19077/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 14 references that are in the map.
- First posted on arXiv 24 Oct 2024.

## Builds on (most cited first, 4 of 4)

- [A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantification](https://mlsanity.com/paper/2107.07511/) (2021)
- [Regression Conformal Prediction with Nearest Neighbours](https://mlsanity.com/paper/1401.3880/) (2014)
- [Computationally efficient versions of conformal predictive distributions](https://mlsanity.com/paper/1911.00941/) (2019)
- [Machine Learning-Based Path Loss Modeling with Simplified Features](https://mlsanity.com/paper/2405.10006/) (2024)

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

- [Conformal Prediction for Multimodal Regression](https://mlsanity.com/paper/2410.19653/) (2024)
- [Uncertainty Estimation for Path Loss and Radio Metric Models](https://mlsanity.com/paper/2501.06308/) (2025)

## Abstract (arXiv)

This paper introduces Target Strangeness, a novel difficulty estimator for conformal prediction (CP) that offers an alternative approach for normalizing prediction intervals (PIs). By assessing how atypical a prediction is within the context of its nearest neighbours' target distribution, Target Strangeness can surpass the current state-of-the-art performance. This novel difficulty estimator is evaluated against others in the context of several conformal regression experiments.