Mix-n-Match: Ensemble and Compositional Methods for Uncertainty Calibration in Deep Learning

• URL: http://arxiv.org/abs/2003.07329v2
• Date: Tue, 30 Jun 2020 06:44:30 GMT
• Title: Mix-n-Match: Ensemble and Compositional Methods for Uncertainty Calibration in Deep Learning
• Authors: Jize Zhang and Bhavya Kailkhura and T. Yong-Jin Han
• Abstract summary: We show how Mix-n-Match calibration strategies can help achieve remarkably better data-efficiency and expressive power. We also reveal potential issues in standard evaluation practices. Our approaches outperform state-of-the-art solutions on both the calibration as well as the evaluation tasks.
• Score: 21.08664370117846
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: This paper studies the problem of post-hoc calibration of machine learning classifiers. We introduce the following desiderata for uncertainty calibration: (a) accuracy-preserving, (b) data-efficient, and (c) high expressive power. We show that none of the existing methods satisfy all three requirements, and demonstrate how Mix-n-Match calibration strategies (i.e., ensemble and composition) can help achieve remarkably better data-efficiency and expressive power while provably maintaining the classification accuracy of the original classifier. Mix-n-Match strategies are generic in the sense that they can be used to improve the performance of any off-the-shelf calibrator. We also reveal potential issues in standard evaluation practices. Popular approaches (e.g., histogram-based expected calibration error (ECE)) may provide misleading results especially in small-data regime. Therefore, we propose an alternative data-efficient kernel density-based estimator for a reliable evaluation of the calibration performance and prove its asymptotically unbiasedness and consistency. Our approaches outperform state-of-the-art solutions on both the calibration as well as the evaluation tasks in most of the experimental settings. Our codes are available at https://github.com/zhang64-llnl/Mix-n-Match-Calibration.

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