  Bayesian decision theory outlines a rigorous framework for making optimal
decisions based on maximizing expected utility over a model posterior. However,
practitioners often do not have access to the full posterior and resort to
approximate inference strategies. In such cases, taking the eventual
decision-making task into account while performing the inference allows for
calibrating the posterior approximation to maximize the utility. We present an
automatic pipeline that co-opts continuous utilities into variational inference
algorithms to account for decision-making. We provide practical strategies for
approximating and maximizing the gain, and empirically demonstrate consistent
improvement when calibrating approximations for specific utilities.

Machine Learning;Machine Learning