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.

翻译：摘要：预测模型通常独立于决策优化进行优化。智能预测后优化（SPO）框架通过优化预测模型来最小化下游决策遗憾。本文提出dboost，这是首个面向“预测后优化”问题的通用智能梯度提升实现。该框架支持凸二次锥规划，并通过自定义不动点映射的隐式微分实现梯度提升。与最新SPO方法的实验对比表明，dboost可进一步降低样本外决策遗憾。