mlsanityData to 14 Oct 2026

Enhancing Conformal Prediction Using E-Test Statistics

arXiv 2403.19082 ↗First posted 28 Mar 2024cs.LG cs.AI math.STcited by 19

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Abstract (arXiv)

Conformal Prediction (CP) serves as a robust framework that quantifies uncertainty in predictions made by Machine Learning (ML) models. Unlike traditional point predictors, CP generates statistically valid prediction regions, also known as prediction intervals, based on the assumption of data exchangeability. Typically, the construction of conformal predictions hinges on p-values. This paper, however, ventures down an alternative path, harnessing the power of e-test statistics to augment the efficacy of conformal predictions by introducing a BB-predictor (bounded from the below predictor).

Key facts. Cited by 19 papers in the map (data through 14 Oct 2026). Builds on 2 of its 4 references that are in the map. First posted on arXiv 28 Mar 2024.

Builds on

2 of 4 references.

References · 2

  1. 2020Conformal Prediction: a Unified Review of Theory and New Challengescited by 118
  2. 2019Safe Testingcited by 101

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 11; 2026: 8.

19 papers.

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