mlsanityData to 7 Oct 2026

Can bin-wise scaling improve consistency and adaptivity of prediction uncertainty for machine learning regression ?

arXiv 2310.11978 ↗First posted 18 Oct 2023stat.ML cs.LG physics.chem-phcited by 4

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Abstract (arXiv)

Binwise Variance Scaling (BVS) has recently been proposed as a post hoc recalibration method for prediction uncertainties of machine learning regression problems that is able of more efficient corrections than uniform variance (or temperature) scaling. The original version of BVS uses uncertainty-based binning, which is aimed to improve calibration conditionally on uncertainty, i.e. consistency. I explore here several adaptations of BVS, in particular with alternative loss functions and a binning scheme based on an input-feature (X) in order to improve adaptivity, i.e. calibration conditional on X. The performances of BVS and its proposed variants are tested on a benchmark dataset for the prediction of atomization energies and compared to the results of isotonic regression.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 11 of its 26 references that are in the map. First posted on arXiv 18 Oct 2023.

Builds on

11 of 26 references.

References · 11

  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
  3. 2019Evaluating and Calibrating Uncertainty Prediction in Regression Taskscited by 85
  4. 2021Uncertainty Toolbox: an Open-Source Library for Assessing, Visualizing, and Improving Uncertainty Quantificationcited by 57
  5. 2021Calibrated Uncertainty for Molecular Property Prediction using Ensembles of Message Passing Neural Networkscited by 21
  6. 2023Calibration in Machine Learning Uncertainty Quantification: beyond consistency to target adaptivitycited by 13
  7. 2023Properties of the ENCE and other MAD-based calibration metricscited by 7
  8. 2023Stratification of uncertainties recalibrated by isotonic regression and its impact on calibration error statisticscited by 6
  9. 2023Validation of uncertainty quantification metrics: a primer based on the consistency and adaptivity conceptscited by 5
  10. 2022The Shannon Entropy of a Histogramcited by 3
  11. 2023ELUQuant: Event-Level Uncertainty Quantification in Deep Inelastic Scatteringcited by 3

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 2; 2025: 1; 2026: 1.

4 papers.

  1. 2024Negative impact of heavy-tailed uncertainty and error distributions on the reliability of calibration statistics for machine learning regression taskscited by 1
  2. 2024Validation of ML-UQ calibration statistics using simulated reference values: a sensitivity analysiscited by 0
  3. 2025Robust Taxi Fare Prediction Under Noisy Conditions: A Comparative Study of GAT, TimesNet, and XGBoostcited by 0
  4. 2026Trustworthy deep domain adaptation for wearable photoplethysmography signal analysis with decision-theoretic uncertainty quantificationcited by 0