High-Quality Prediction Intervals for Deep Learning: A Distribution-Free, Ensembled Approach
This work addresses uncertainty quantification for regression tasks in machine learning, offering a distribution-free, ensembled approach that is incremental but provides specific improvements.
The paper tackles the problem of generating high-quality prediction intervals for uncertainty quantification in regression tasks using neural networks, achieving a reduction in average prediction interval width by over 10% compared to state-of-the-art methods.
This paper considers the generation of prediction intervals (PIs) by neural networks for quantifying uncertainty in regression tasks. It is axiomatic that high-quality PIs should be as narrow as possible, whilst capturing a specified portion of data. We derive a loss function directly from this axiom that requires no distributional assumption. We show how its form derives from a likelihood principle, that it can be used with gradient descent, and that model uncertainty is accounted for in ensembled form. Benchmark experiments show the method outperforms current state-of-the-art uncertainty quantification methods, reducing average PI width by over 10%.