mlsanityData to 7 Oct 2026

Guaranteed Coverage Prediction Intervals with Gaussian Process Regression

arXiv 2310.15641 ↗First posted 24 Oct 2023cs.LGcited by 12

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Abstract (arXiv)

Gaussian Process Regression (GPR) is a popular regression method, which unlike most Machine Learning techniques, provides estimates of uncertainty for its predictions. These uncertainty estimates however, are based on the assumption that the model is well-specified, an assumption that is violated in most practical applications, since the required knowledge is rarely available. As a result, the produced uncertainty estimates can become very misleading; for example the prediction intervals (PIs) produced for the 95% confidence level may cover much less than 95% of the true labels. To address this issue, this paper introduces an extension of GPR based on a Machine Learning framework called, Conformal Prediction (CP). This extension guarantees the production of PIs with the required coverage even when the model is completely misspecified. The proposed approach combines the advantages of GPR with the valid coverage guarantee of CP, while the performed experimental results demonstrate its superiority over existing methods.

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Builds on 19 of its 56 references that are in the map. First posted on arXiv 24 Oct 2023.

Builds on

19 of 56 references.

References · 19

  1. 2021A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantificationcited by 777
  2. 2018Accurate Uncertainties for Deep Learning Using Calibrated Regressioncited by 417
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  8. 2015Gaussian Processes for Data-Efficient Learning in Robotics and Controlcited by 97
  9. 2019Distribution Calibration for Regressioncited by 88
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  11. 2021Practical and Rigorous Uncertainty Bounds for Gaussian Process Regressioncited by 49
  12. 2017Fast Exact Conformalization of Lasso using Piecewise Linear Homotopycited by 20
7 more
  1. 2019Computationally efficient versions of conformal predictive distributionscited by 20
  2. 2019Conformal Prediction Interval Estimations with an Application to Day-Ahead and Intraday Power Marketscited by 19
  3. 2021Gaussian Process Uniform Error Bounds with Unknown Hyperparameters for Safety-Critical Applicationscited by 17
  4. 2022Modular Conformal Calibrationcited by 15
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  7. 2022A Cross-Conformal Predictor for Multi-label Classificationcited by 8

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 6; 2025: 4; 2026: 1.

12 papers.

  1. 2024Uncertainty quantification for probabilistic machine learning in earth observation using conformal predictioncited by 9
  2. 2024TorchCP: A Python Library for Conformal Predictioncited by 9
  3. 2024Conformal Approach To Gaussian Process Surrogate Evaluation With Coverage Guaranteescited by 7
  4. 2023Uncertainty quantification in automated valuation models with spatially weighted conformal predictioncited by 3
  5. 2024Gaussian process interpolation with conformal prediction: methods and comparative analysiscited by 3
  6. 2024Online scalable Gaussian processes with conformal prediction for guaranteed coveragecited by 3
  7. 2025Uncertainty Estimation for Path Loss and Radio Metric Modelscited by 1
  8. 2025Scalable and adaptive prediction bands with kernel sum-of-squarescited by 1
  9. 2025Approximating Full Conformal Prediction for Neural Network Regression with Gauss-Newton Influencecited by 1
  10. 2026Conformal prediction for full and sparse polynomial chaos expansionscited by 1
  11. 2024Inductive Conformal Prediction under Data Scarcity: Exploring the Impacts of Nonconformity Measurescited by 0
  12. 2025Tackling Incomplete Data in Air Quality Prediction: A Bayesian Deep Learning Framework for Uncertainty Quantificationcited by 0