A Large-Scale Study of Probabilistic Calibration in Neural Network Regression

• URL: http://arxiv.org/abs/2306.02738v2
• Date: Wed, 7 Jun 2023 10:42:23 GMT
• Title: A Large-Scale Study of Probabilistic Calibration in Neural Network Regression
• Authors: Victor Dheur and Souhaib Ben Taieb
• Abstract summary: We conduct the largest empirical study to date to assess the probabilistic calibration of neural networks. We introduce novel differentiable recalibration and regularization methods, uncovering new insights into their effectiveness.
• Score: 3.13468877208035
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Accurate probabilistic predictions are essential for optimal decision making. While neural network miscalibration has been studied primarily in classification, we investigate this in the less-explored domain of regression. We conduct the largest empirical study to date to assess the probabilistic calibration of neural networks. We also analyze the performance of recalibration, conformal, and regularization methods to enhance probabilistic calibration. Additionally, we introduce novel differentiable recalibration and regularization methods, uncovering new insights into their effectiveness. Our findings reveal that regularization methods offer a favorable tradeoff between calibration and sharpness. Post-hoc methods exhibit superior probabilistic calibration, which we attribute to the finite-sample coverage guarantee of conformal prediction. Furthermore, we demonstrate that quantile recalibration can be considered as a specific case of conformal prediction. Our study is fully reproducible and implemented in a common code base for fair comparisons.

Related papers

• Achieving Well-Informed Decision-Making in Drug Discovery: A Comprehensive Calibration Study using Neural Network-Based Structure-Activity Models [4.619907534483781]
  computational models that predict drug-target interactions are valuable tools to accelerate the development of new therapeutic agents. However, such models can be poorly calibrated, which results in unreliable uncertainty estimates. We show that combining post hoc calibration method with well-performing uncertainty quantification approaches can boost model accuracy and calibration.
  arXiv  Detail & Related papers  (2024-07-19T10:29:00Z)
• Probabilistic Calibration by Design for Neural Network Regression [2.3020018305241337]
  We introduce a novel end-to-end model training procedure called Quantile Recalibration Training. We demonstrate the performance of our method in a large-scale experiment involving 57 regression datasets.
  arXiv  Detail & Related papers  (2024-03-18T17:04:33Z)
• 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)
• Modular Conformal Calibration [80.33410096908872]
  We introduce a versatile class of algorithms for recalibration in regression. This framework allows one to transform any regression model into a calibrated probabilistic model. We conduct an empirical study of MCC on 17 regression datasets.
  arXiv  Detail & Related papers  (2022-06-23T03:25:23Z)
• Learning Prediction Intervals for Regression: Generalization and Calibration [12.576284277353606]
  We study the generation of prediction intervals in regression for uncertainty quantification. We use a general learning theory to characterize the optimality-feasibility tradeoff that encompasses Lipschitz continuity and VC-subgraph classes. We empirically demonstrate the strengths of our interval generation and calibration algorithms in terms of testing performances compared to existing benchmarks.
  arXiv  Detail & Related papers  (2021-02-26T17:55:30Z)
• Improving Uncertainty Calibration via Prior Augmented Data [56.88185136509654]
  Neural networks have proven successful at learning from complex data distributions by acting as universal function approximators. They are often overconfident in their predictions, which leads to inaccurate and miscalibrated probabilistic predictions. We propose a solution by seeking out regions of feature space where the model is unjustifiably overconfident, and conditionally raising the entropy of those predictions towards that of the prior distribution of the labels.
  arXiv  Detail & Related papers  (2021-02-22T07:02:37Z)
• 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)
• Calibration of Neural Networks using Splines [51.42640515410253]
  Measuring calibration error amounts to comparing two empirical distributions. We introduce a binning-free calibration measure inspired by the classical Kolmogorov-Smirnov (KS) statistical test. Our method consistently outperforms existing methods on KS error as well as other commonly used calibration measures.
  arXiv  Detail & Related papers  (2020-06-23T07:18:05Z)
• Revisiting Explicit Regularization in Neural Networks for Well-Calibrated Predictive Uncertainty [6.09170287691728]
  In this work, we revisit the importance of explicit regularization for obtaining well-calibrated predictive uncertainty. We introduce a measure of calibration performance, which is lower bounded by the log-likelihood. We then explore explicit regularization techniques for improving the log-likelihood on unseen samples, which provides well-calibrated predictive uncertainty.
  arXiv  Detail & Related papers  (2020-06-11T13:14:01Z)

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.