Full Conformal Prediction under Stochastic Non-Conformity Measure
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Abstract (arXiv)
The theory of full conformal prediction uses deterministic non-conformity measure, but modern usage of full conformal prediction often relies on machine learning training, making stochasticity inevitable. A simple sufficient condition of almost sure permutation invariance of the non-conformity measure can be too restrictive, so many have suggested the relaxation to permutation in distribution as a condition for full conformal prediction validity. We, however, show that this commonly known condition is actually insufficient. We then provide a correct sufficient condition: Conditional Independence & Permutation Invariance in Distribution, which encompasses several stochastic settings that may be used in machine learning.
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References · 12
- 2021A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantificationcited by 792
- 2019Conformalized Quantile Regressioncited by 629
- 2022Conformal prediction beyond exchangeabilitycited by 355
- 2023Conformal Language Modelingcited by 170
- 2024API Is Enough: Conformal Prediction for Large Language Models Without Logit-Accesscited by 69
- 2023Conformal Nucleus Samplingcited by 33
- 2023Distribution-free inference with hierarchical datacited by 15
- 2024Optimized Conformal Selection: Powerful Selective Inference After Conformity Score Optimizationcited by 14
- 2025LLMs are Overconfident: Evaluating Confidence Interval Calibration with FermiEvalcited by 8
- 2025Leave-One-Out Stable Conformal Predictioncited by 3
- 2018conformalClassification: A Conformal Prediction R Package for Classificationcited by 1
- 2025Approximating Full Conformal Prediction for Neural Network Regression with Gauss-Newton Influencecited by 1
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