Exploring the Potential of Bilevel Optimization for Calibrating Neural Networks

• URL: http://arxiv.org/abs/2503.13113v1
• Date: Mon, 17 Mar 2025 12:34:55 GMT
• Title: Exploring the Potential of Bilevel Optimization for Calibrating Neural Networks
• Authors: Gabriele Sanguin, Arjun Pakrashi, Marco Viola, Francesco Rinaldi,
• Abstract summary: Modern neural networks are poorly calibrated, resulting in predicted confidence scores that are difficult to use.<n>This article explores improving confidence estimation and calibration through the application of bilevel optimization.<n>A self-calibrating bilevel neural-network training approach is introduced to improve a model's predicted confidence scores.
• Score: 0.7499722271664147
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Handling uncertainty is critical for ensuring reliable decision-making in intelligent systems. Modern neural networks are known to be poorly calibrated, resulting in predicted confidence scores that are difficult to use. This article explores improving confidence estimation and calibration through the application of bilevel optimization, a framework designed to solve hierarchical problems with interdependent optimization levels. A self-calibrating bilevel neural-network training approach is introduced to improve a model's predicted confidence scores. The effectiveness of the proposed framework is analyzed using toy datasets, such as Blobs and Spirals, as well as more practical simulated datasets, such as Blood Alcohol Concentration (BAC). It is compared with a well-known and widely used calibration strategy, isotonic regression. The reported experimental results reveal that the proposed bilevel optimization approach reduces the calibration error while preserving accuracy.

Related papers

• Feature Clipping for Uncertainty Calibration [24.465567005078135]
  Modern deep neural networks (DNNs) often suffer from overconfidence, leading to miscalibration. We propose a novel post-hoc calibration method called feature clipping (FC) to address this issue. FC involves clipping feature values to a specified threshold, effectively increasing entropy in high calibration error samples.
  arXiv  Detail & Related papers  (2024-10-16T06:44:35Z)
• 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)
• Calibrating Bayesian Learning via Regularization, Confidence Minimization, and Selective Inference [37.82259435084825]
  A well-calibrated AI model must correctly report its accuracy on in-distribution (ID) inputs, while also enabling the detection of out-of-distribution (OOD) inputs. This paper proposes an extension of variational inference (VI)-based Bayesian learning that integrates calibration regularization for improved ID performance.
  arXiv  Detail & Related papers  (2024-04-17T13:08:26Z)
• Towards Calibrated Deep Clustering Network [60.71776081164377]
  In deep clustering, the estimated confidence for a sample belonging to a particular cluster greatly exceeds its actual prediction accuracy. We propose a novel dual-head (calibration head and clustering head) deep clustering model that can effectively calibrate the estimated confidence and the actual accuracy. Extensive experiments demonstrate the proposed calibrated deep clustering model not only surpasses state-of-the-art deep clustering methods by 10 times in terms of expected calibration error but also significantly outperforms them in terms of clustering accuracy.
  arXiv  Detail & Related papers  (2024-03-04T11:23:40Z)
• Calibration-Aware Bayesian Learning [37.82259435084825]
  This paper proposes an integrated framework, referred to as calibration-aware Bayesian neural networks (CA-BNNs) It applies both data-dependent or data-independent regularizers while optimizing over a variational distribution as in Bayesian learning. Numerical results validate the advantages of the proposed approach in terms of expected calibration error (ECE) and reliability diagrams.
  arXiv  Detail & Related papers  (2023-05-12T14:19:15Z)
• 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)
• On Calibrating Semantic Segmentation Models: Analyses and An Algorithm [51.85289816613351]
  We study the problem of semantic segmentation calibration. Model capacity, crop size, multi-scale testing, and prediction correctness have impact on calibration. We propose a simple, unifying, and effective approach, namely selective scaling.
  arXiv  Detail & Related papers  (2022-12-22T22:05:16Z)
• On double-descent in uncertainty quantification in overparametrized models [24.073221004661427]
  Uncertainty quantification is a central challenge in reliable and trustworthy machine learning. We show a trade-off between classification accuracy and calibration, unveiling a double descent like behavior in the calibration curve of optimally regularized estimators. This is in contrast with the empirical Bayes method, which we show to be well calibrated in our setting despite the higher generalization error and overparametrization.
  arXiv  Detail & Related papers  (2022-10-23T16:01:08Z)
• Meta-Calibration: Learning of Model Calibration Using Differentiable Expected Calibration Error [46.12703434199988]
  We introduce a new differentiable surrogate for expected calibration error (DECE) that allows calibration quality to be directly optimised. We also propose a meta-learning framework that uses DECE to optimise for validation set calibration.
  arXiv  Detail & Related papers  (2021-06-17T15:47:50Z)
• Privacy Preserving Recalibration under Domain Shift [119.21243107946555]
  We introduce a framework that abstracts out the properties of recalibration problems under differential privacy constraints. We also design a novel recalibration algorithm, accuracy temperature scaling, that outperforms prior work on private datasets.
  arXiv  Detail & Related papers  (2020-08-21T18:43:37Z)
• Calibrating Deep Neural Networks using Focal Loss [77.92765139898906]
  Miscalibration is a mismatch between a model's confidence and its correctness. We show that focal loss allows us to learn models that are already very well calibrated. We show that our approach achieves state-of-the-art calibration without compromising on accuracy in almost all cases.
  arXiv  Detail & Related papers  (2020-02-21T17:35:50Z)

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.