mlsanityData to 7 Oct 2026

Large-scale probabilistic predictors with and without guarantees of validity

arXiv 1511.00213 ↗First posted 1 Nov 2015cs.LGcited by 18

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Abstract (arXiv)

This paper studies theoretically and empirically a method of turning machine-learning algorithms into probabilistic predictors that automatically enjoys a property of validity (perfect calibration) and is computationally efficient. The price to pay for perfect calibration is that these probabilistic predictors produce imprecise (in practice, almost precise for large data sets) probabilities. When these imprecise probabilities are merged into precise probabilities, the resulting predictors, while losing the theoretical property of perfect calibration, are consistently more accurate than the existing methods in empirical studies.

Key facts. Cited by 18 papers in the map (data through 7 Oct 2026). First posted on arXiv 1 Nov 2015.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 4; 2023: 3; 2024: 1; 2025: 8; 2026: 2.

18 papers.

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  1. 2023Conformal Language Modelingcited by 163
  2. 2022Conformal Prediction Sets with Limited False Positivescited by 28
  3. 2022Efficiently Controlling Multiple Risks with Pareto Testingcited by 21
  4. 2023Calibrated Explanations: with Uncertainty Information and Counterfactualscited by 16
  5. 2025Rethinking Early Stopping: Refine, Then Calibratecited by 13
  6. 2022Calibration of Natural Language Understanding Models with Venn--ABERS Predictorscited by 6
  7. 2023Risk-Controlling Model Selection via Guided Bayesian Optimizationcited by 5
  8. 2024Calibrated Large Language Models for Binary Question Answeringcited by 3
  9. 2025Conformal Predictive Distributions for Order Fulfillment Time Forecastingcited by 3
  10. 2025Exploring the Impact of Explainable AI and Cognitive Capabilities on Users' Decisionscited by 1
  11. 2025Adaptive Cumulative Mass Calibration with Conformal Predictioncited by 1
  12. 2025A systematic evaluation of uncertainty quantification techniques in deep learning: a case study in photoplethysmography signal analysiscited by 1
6 more
  1. 2026CalArena: A Large-Scale Post-Hoc Calibration Benchmarkcited by 1
  2. 2022Approximate Conditional Coverage & Calibration via Neural Model Approximationscited by 0
  3. 2025Out-of-Sample Hydrocarbon Production Forecasting: Time Series Machine Learning using Productivity Index-Driven Features and Inductive Conformal Predictioncited by 0
  4. 2025Protected Probabilistic Classification Librarycited by 0
  5. 2025Residual Distribution Predictive Systemscited by 0
  6. 2026Inductive Venn-Abers and related regressorscited by 0