Stochastic Optimization Forests
Nathan Kallus, Xiaojie Mao

TL;DR
This paper introduces a novel method for training decision forests tailored for stochastic optimization problems, optimizing decision quality directly rather than prediction accuracy, and demonstrates its efficiency and effectiveness.
Contribution
The paper develops an optimization-aware forest training algorithm with approximate splitting criteria that scale efficiently and improve decision-making in stochastic settings.
Findings
Method reduces training time by up to 100 times.
Achieves asymptotic optimality in decision policies.
Performs close to exact re-optimization methods in experiments.
Abstract
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,…
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Taxonomy
TopicsMachine Learning and Data Classification · Bayesian Modeling and Causal Inference · Explainable Artificial Intelligence (XAI)
