Likelihood-ratio-based confidence intervals for neural networks
Laurens Sluijterman Eric Cator Tom Heskes
This paper introduces a first implementation of a novel
likelihood-ratio-based approach for constructing confidence intervals for
neural networks. Our method, called DeepLR, offers several qualitative
advantages: most notably, the ability to construct asymmetric intervals that
expand in regions with a limited amount of data, and the inherent incorporation
of factors such as the amount of training time, network architecture, and
regularization techniques. While acknowledging that the current implementation
of the method is prohibitively expensive for many deep-learning applications,
the high cost may already be justified in specific fields like medical
predictions or astrophysics, where a reliable uncertainty estimate for a single
prediction is essential. This work highlights the significant potential of a
likelihood-ratio-based uncertainty estimate and establishes a promising avenue
for future research.