  Bayesian decision theory provides an elegant framework for acting optimally
under uncertainty when tractable posterior distributions are available. Modern
Bayesian models, however, typically involve intractable posteriors that are
approximated with, potentially crude, surrogates. This difficulty has
engendered loss-calibrated techniques that aim to learn posterior
approximations that favor high-utility decisions. In this paper, focusing on
Bayesian neural networks, we develop methods for correcting approximate
posterior predictive distributions encouraging them to prefer high-utility
decisions. In contrast to previous work, our approach is agnostic to the choice
of the approximate inference algorithm, allows for efficient test time decision
making through amortization, and empirically produces higher quality decisions.
We demonstrate the effectiveness of our approach through controlled experiments
spanning a diversity of tasks and datasets.
