We study contextual stochastic optimization problems, where we leverage rich
auxiliary observations (e.g., product characteristics) to improve decision
making with uncertain variables (e.g., demand). We show how to train forest
decision policies for this problem by growing trees that choose splits to
directly optimize the downstream decision quality, rather than splitting to
improve prediction accuracy as in the standard random forest algorithm. We
realize this seemingly computationally intractable problem by developing
approximate splitting criteria that utilize optimization perturbation analysis
to eschew burdensome re-optimization for every candidate split, so that our
method scales to large-scale problems. We prove that our splitting criteria
consistently approximate the true risk and that our method achieves asymptotic
optimality. We extensively validate our method empirically, demonstrating the
value of optimization-aware construction of forests and the success of our
efficient approximations. We show that our approximate splitting criteria can
reduce running time hundredfold, while achieving performance close to forest
algorithms that exactly re-optimize for every candidate split.

本文研究了背景下的随机优化问题，提出了一种基于森林决策策略的解决方案，通过开发近似分裂准则，我们能够有效降低运行时间，并在性能上接近于每个候选分裂都进行重新优化的森林算法.