High-Quality Prediction Intervals for Deep Learning: A Distribution-Free, Ensembled Approach

Understand

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.