  Epistemic uncertainty quantification is a crucial part of drawing credible
conclusions from predictive models, whether concerned about the prediction at a
given point or any downstream evaluation that uses the model as input. When the
predictive model is simple and its evaluation differentiable, this task is
solved by the delta method, where we propagate the asymptotically-normal
uncertainty in the predictive model through the evaluation to compute standard
errors and Wald confidence intervals. However, this becomes difficult when the
model and/or evaluation becomes more complex. Remedies include the bootstrap,
but it can be computationally infeasible when training the model even once is
costly. In this paper, we propose an alternative, the implicit delta method,
which works by infinitesimally regularizing the training loss of the predictive
model to automatically assess downstream uncertainty. We show that the change
in the evaluation due to regularization is consistent for the asymptotic
variance of the evaluation estimator, even when the infinitesimal change is
approximated by a finite difference. This provides both a reliable
quantification of uncertainty in terms of standard errors as well as permits
the construction of calibrated confidence intervals. We discuss connections to
other approaches to uncertainty quantification, both Bayesian and frequentist,
and demonstrate our approach empirically.
