Mix-n-Match: Ensemble and Compositional Methods for Uncertainty Calibration in Deep Learning Article Swipe
Jize Zhang
,
Bhavya Kailkhura
,
T. Yong-Jin Han
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2003.07329
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2003.07329
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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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2003.07329
- https://arxiv.org/pdf/2003.07329
- OA Status
- green
- Cited By
- 18
- References
- 60
- Related Works
- 20
- OpenAlex ID
- https://openalex.org/W3012254281
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3012254281Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2003.07329Digital Object Identifier
- Title
-
Mix-n-Match: Ensemble and Compositional Methods for Uncertainty Calibration in Deep LearningWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2020Year of publication
- Publication date
-
2020-03-16Full publication date if available
- Authors
-
Jize Zhang, Bhavya Kailkhura, T. Yong-Jin HanList of authors in order
- Landing page
-
https://arxiv.org/abs/2003.07329Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2003.07329Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2003.07329Direct OA link when available
- Concepts
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Computer science, Calibration, Estimator, Classifier (UML), Consistency (knowledge bases), Artificial intelligence, Ensemble learning, Machine learning, Kernel (algebra), Kernel density estimation, Data mining, Algorithm, Mathematics, Statistics, CombinatoricsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
18Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 6, 2024: 5, 2023: 6, 2022: 1Per-year citation counts (last 5 years)
- References (count)
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60Number of works referenced by this work
- Related works (count)
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20Other works algorithmically related by OpenAlex
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| primary_location.is_published | False |
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| publication_date | 2020-03-16 |
| publication_year | 2020 |
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