用可在线训练的代理模型加速决策导向学习，提升优化效率。
Scalable Decision Focused Learning via Online Trainable Surrogates
- 用无偏代理模型替代昂贵的损失计算，实现高效训练
- 减少内层求解器调用次数，保持与顶尖方法相当的解质量
- 适用于黑箱场景，支持回溯动作建模，适合工业级决策系统
决策支持系统常依赖求解复杂优化问题，需预先估计不确定参数。传统估算方法可能导致次优解。采用实际决策成本作为损失函数（即决策导向学习）可缓解此问题，但训练时面临严重可扩展性瓶颈。本文提出一种加速方法：用高效代理模型替代昂贵的损失函数评估。不同于已有代理模型，本方法基于无偏估计器，降低虚假局部最优风险，并能提供局部置信度信息，必要时切换至备用方案。此外，该代理模型专为黑箱设置设计，可弥补优化模型简化带来的偏差，并在成本计算中考虑后续应对措施。实验表明，该方法显著减少了内层求解器调用，同时解质量接近当前最优水平。
原文摘要 · Abstract (English)
Decision support systems often rely on solving complex optimization problems that may require to estimate uncertain parameters beforehand. Recent studies have shown how using traditionally trained estimators for this task can lead to suboptimal solutions. Using the actual decision cost as a loss function (called Decision Focused Learning) can address this issue, but with a severe loss of scalability at training time. To address this issue, we propose an acceleration method based on replacing costly loss function evaluations with an efficient surrogate. Unlike previously defined surrogates, our approach relies on unbiased estimators reducing the risk of spurious local optima and can provide information on its local confidence allowing one to switch to a fallback method when needed. Furthermore, the surrogate is designed for a black-box setting, which enables compensating for simplifications in the optimization model and accounting for recourse actions during cost computation. In our results, the method reduces costly inner solver calls, with a solution quality comparable to other state-of-the-art techniques.
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