  Prediction models are typically optimized independently from decision
optimization. A smart predict then optimize (SPO) framework optimizes
prediction models to minimize downstream decision regret. In this paper we
present dboost, the first general purpose implementation of smart gradient
boosting for `predict, then optimize' problems. The framework supports convex
quadratic cone programming and gradient boosting is performed by implicit
differentiation of a custom fixed-point mapping. Experiments comparing with
state-of-the-art SPO methods show that dboost can further reduce out-of-sample
decision regret.
