Uncertainty Quantification via Stable Distribution Propagation
Felix Petersen Aashwin Mishra Hilde Kuehne Christian Borgelt Oliver Deussen Mikhail Yurochkin
We propose a new approach for propagating stable probability distributions
through neural networks. Our method is based on local linearization, which we
show to be an optimal approximation in terms of total variation distance for
the ReLU non-linearity. This allows propagating Gaussian and Cauchy input
uncertainties through neural networks to quantify their output uncertainties.
To demonstrate the utility of propagating distributions, we apply the proposed
method to predicting calibrated confidence intervals and selective prediction
on out-of-distribution data. The results demonstrate a broad applicability of
propagating distributions and show the advantages of our method over other
approaches such as moment matching.