Meta-Calibration: Learning of Model Calibration Using Differentiable Expected Calibration Error

• URL: http://arxiv.org/abs/2106.09613v3
• Date: Fri, 25 Aug 2023 19:18:31 GMT
• Title: Meta-Calibration: Learning of Model Calibration Using Differentiable Expected Calibration Error
• Authors: Ondrej Bohdal, Yongxin Yang, Timothy Hospedales
• Abstract summary: 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.
• Score: 46.12703434199988
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Calibration of neural networks is a topical problem that is becoming more and more important as neural networks increasingly underpin real-world applications. The problem is especially noticeable when using modern neural networks, for which there is a significant difference between the confidence of the model and the probability of correct prediction. Various strategies have been proposed to improve calibration, yet accurate calibration remains challenging. We propose a novel framework with two contributions: introducing a new differentiable surrogate for expected calibration error (DECE) that allows calibration quality to be directly optimised, and a meta-learning framework that uses DECE to optimise for validation set calibration with respect to model hyper-parameters. The results show that we achieve competitive performance with existing calibration approaches. Our framework opens up a new avenue and toolset for tackling calibration, which we believe will inspire further work on this important challenge.

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