Improving Regression Uncertainty Estimation Under Statistical Change

• URL: http://arxiv.org/abs/2109.08213v1
• Date: Thu, 16 Sep 2021 20:32:58 GMT
• Title: Improving Regression Uncertainty Estimation Under Statistical Change
• Authors: Tony Tohme, Kevin Vanslette, Kamal Youcef-Toumi
• Abstract summary: We propose and implement a loss function for regression uncertainty estimation based on the Bayesian Validation Metric framework. A series of experiments on in-distribution data show that the proposed method is competitive with existing state-of-the-art methods.
• Score: 7.734726150561088
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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. A series of experiments on in-distribution data show that the proposed method is competitive with existing state-of-the-art methods. In addition, experiments on out-of-distribution data show that the proposed method is robust to statistical change and exhibits superior predictive capability.

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