  Bayesian neural network (BNN) priors are defined in parameter space, making
it hard to encode prior knowledge expressed in function space. We formulate a
prior that incorporates functional constraints about what the output can or
cannot be in regions of the input space. Output-Constrained BNNs (OC-BNN)
represent an interpretable approach of enforcing a range of constraints, fully
consistent with the Bayesian framework and amenable to black-box inference. We
demonstrate how OC-BNNs improve model robustness and prevent the prediction of
infeasible outputs in two real-world applications of healthcare and robotics.
