mlsanityData to 14 Oct 2026

Target Strangeness: A Novel Conformal Prediction Difficulty Estimator

arXiv 2410.19077 ↗First posted 24 Oct 2024cs.LGcited by 2

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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.

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

4 of 14 references.

References · 4

  1. 2021A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantificationcited by 792
  2. 2014Regression Conformal Prediction with Nearest Neighbourscited by 59
  3. 2019Computationally efficient versions of conformal predictive distributionscited by 20
  4. 2024Machine Learning-Based Path Loss Modeling with Simplified Featurescited by 9

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Cited by year

Citing papers per year: 2024: 1; 2025: 1.

2 papers.

  1. 2024Conformal Prediction for Multimodal Regressioncited by 2
  2. 2025Uncertainty Estimation for Path Loss and Radio Metric Modelscited by 1