Reliable neural networks for regression uncertainty estimation

Tony Tohme, Kevin Vanslette and Kamal Youcef-Toumi

Reliability Engineering and System Safety, 2023, vol. 229, issue C

Abstract: While deep neural networks are highly performant and successful in a wide range of real-world problems, estimating their predictive uncertainty remains a challenging task. To address this challenge, we propose and implement a loss function for regression uncertainty estimation based on the Bayesian Validation Metric (BVM) framework while using ensemble learning. The proposed loss reproduces maximum likelihood estimation in the limiting case. A series of experiments on in-distribution data show that the proposed method is competitive with existing state-of-the-art methods. Experiments on out-of-distribution data show that the proposed method is robust to statistical change and exhibits superior predictive capability.

Keywords: Neural networks; Reliability; Regression; Predictive uncertainty estimation (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:229:y:2023:i:c:s0951832022004306

DOI: 10.1016/j.ress.2022.108811

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