Better Uncertainty Calibration via Proper Scores for Classification and Beyond

• URL: http://arxiv.org/abs/2203.07835v4
• Date: Tue, 12 Mar 2024 20:48:50 GMT
• Title: Better Uncertainty Calibration via Proper Scores for Classification and Beyond
• Authors: Sebastian G. Gruber and Florian Buettner
• Abstract summary: We introduce the framework of proper calibration errors, which relates every calibration error to a proper score. This relationship can be used to reliably quantify the model calibration improvement.
• Score: 15.981380319863527
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With model trustworthiness being crucial for sensitive real-world applications, practitioners are putting more and more focus on improving the uncertainty calibration of deep neural networks. Calibration errors are designed to quantify the reliability of probabilistic predictions but their estimators are usually biased and inconsistent. In this work, we introduce the framework of proper calibration errors, which relates every calibration error to a proper score and provides a respective upper bound with optimal estimation properties. This relationship can be used to reliably quantify the model calibration improvement. We theoretically and empirically demonstrate the shortcomings of commonly used estimators compared to our approach. Due to the wide applicability of proper scores, this gives a natural extension of recalibration beyond classification.

Related papers

• Optimizing Estimators of Squared Calibration Errors in Classification [2.3020018305241337]
  We propose a mean-squared error-based risk that enables the comparison and optimization of estimators of squared calibration errors. Our approach advocates for a training-validation-testing pipeline when estimating a calibration error.
  arXiv  Detail & Related papers  (2024-10-09T15:58:06Z)
• Towards Certification of Uncertainty Calibration under Adversarial Attacks [96.48317453951418]
  We show that attacks can significantly harm calibration, and thus propose certified calibration as worst-case bounds on calibration under adversarial perturbations. We propose novel calibration attacks and demonstrate how they can improve model calibration through textitadversarial calibration training
  arXiv  Detail & Related papers  (2024-05-22T18:52:09Z)
• From Uncertainty to Precision: Enhancing Binary Classifier Performance through Calibration [0.3495246564946556]
  Given that model-predicted scores are commonly seen as event probabilities, calibration is crucial for accurate interpretation. We analyze the sensitivity of various calibration measures to score distortions and introduce a refined metric, the Local Score. We apply these findings in a real-world scenario using Random Forest classifier and regressor to predict credit default while simultaneously measuring calibration.
  arXiv  Detail & Related papers  (2024-02-12T16:55:19Z)
• Consistent and Asymptotically Unbiased Estimation of Proper Calibration Errors [23.819464242327257]
  We propose a method that allows consistent estimation of all proper calibration errors and refinement terms. We prove the relation between refinement and f-divergences, which implies information monotonicity in neural networks. Our experiments validate the claimed properties of the proposed estimator and suggest that the selection of a post-hoc calibration method should be determined by the particular calibration error of interest.
  arXiv  Detail & Related papers  (2023-12-14T01:20:08Z)
• Calibration by Distribution Matching: Trainable Kernel Calibration Metrics [56.629245030893685]
  We introduce kernel-based calibration metrics that unify and generalize popular forms of calibration for both classification and regression. These metrics admit differentiable sample estimates, making it easy to incorporate a calibration objective into empirical risk minimization. We provide intuitive mechanisms to tailor calibration metrics to a decision task, and enforce accurate loss estimation and no regret decisions.
  arXiv  Detail & Related papers  (2023-10-31T06:19:40Z)
• Calibration of Neural Networks [77.34726150561087]
  This paper presents a survey of confidence calibration problems in the context of neural networks. We analyze problem statement, calibration definitions, and different approaches to evaluation. Empirical experiments cover various datasets and models, comparing calibration methods according to different criteria.
  arXiv  Detail & Related papers  (2023-03-19T20:27:51Z)
• Sharp Calibrated Gaussian Processes [58.94710279601622]
  State-of-the-art approaches for designing calibrated models rely on inflating the Gaussian process posterior variance. We present a calibration approach that generates predictive quantiles using a computation inspired by the vanilla Gaussian process posterior variance. Our approach is shown to yield a calibrated model under reasonable assumptions.
  arXiv  Detail & Related papers  (2023-02-23T12:17:36Z)
• T-Cal: An optimal test for the calibration of predictive models [49.11538724574202]
  We consider detecting mis-calibration of predictive models using a finite validation dataset as a hypothesis testing problem. detecting mis-calibration is only possible when the conditional probabilities of the classes are sufficiently smooth functions of the predictions. We propose T-Cal, a minimax test for calibration based on a de-biased plug-in estimator of the $ell$-Expected Error (ECE)
  arXiv  Detail & Related papers  (2022-03-03T16:58:54Z)
• Estimating Expected Calibration Errors [1.52292571922932]
  Uncertainty in probabilistics predictions is a key concern when models are used to support human decision making. Most models are not intrinsically well calibrated, meaning that their decision scores are not consistent with posterior probabilities. We build an empirical procedure to quantify the quality of $ECE$ estimators, and use it to decide which estimator should be used in practice for different settings.
  arXiv  Detail & Related papers  (2021-09-08T08:00:23Z)
• Unsupervised Calibration under Covariate Shift [92.02278658443166]
  We introduce the problem of calibration under domain shift and propose an importance sampling based approach to address it. We evaluate and discuss the efficacy of our method on both real-world datasets and synthetic datasets.
  arXiv  Detail & Related papers  (2020-06-29T21:50:07Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.