从顺序到递归：通过双向反馈增强决策聚焦学习
From Sequential to Recursive: Enhancing Decision-Focused Learning with Bidirectional Feedback
AI总结：
本文提出递归决策聚焦学习（R-DFL），通过引入双向反馈机制，提升决策质量并增强适应性。
摘要：
决策聚焦学习（DFL）通过直接优化预测模型通过下游决策损失，已作为一种强大的端到端替代传统预测-然后优化（PTO）流程。现有DFL框架受限于其严格顺序结构，称为顺序DFL（S-DFL）。然而，S-DFL在复杂交互场景中未能捕捉预测与优化之间的双向反馈。为此，我们首次提出递归决策聚焦学习（R-DFL），一种引入下游优化与上游预测之间双向反馈的新框架。我们进一步扩展了两种不同的微分方法：通过自动微分的显式展开和基于固定点方法的隐式微分，以促进R-DFL中的高效梯度传播。我们严格证明了两种方法在梯度精度上具有可比性，隐式方法在计算效率上更具优势。在合成和现实世界数据集上进行了广泛的实验，包括新供应商问题和二部匹配问题，证明R-DFL不仅显著提升了最终决策质量，超过顺序基线，还表现出在闭环决策问题中的稳健适应性。
英文摘要：
Decision-focused learning (DFL) has emerged as a powerful end-to-end alternative to conventional predict-then-optimize (PTO) pipelines by directly optimizing predictive models through downstream decision losses. Existing DFL frameworks are limited by their strictly sequential structure, referred to as sequential DFL (S-DFL). However, S-DFL fails to capture the bidirectional feedback between prediction and optimization in complex interaction scenarios. In view of this, we first time propose recursive decision-focused learning (R-DFL), a novel framework that introduces bidirectional feedback between downstream optimization and upstream prediction. We further extend two distinct differentiation methods: explicit unrolling via automatic differentiation and implicit differentiation based on fixed-point methods, to facilitate efficient gradient propagation in R-DFL. We rigorously prove that both methods achieve comparable gradient accuracy, with the implicit method offering superior computational efficiency. Extensive experiments on both synthetic and real-world datasets, including the newsvendor problem and the bipartite matching problem, demonstrate that R-DFL not only substantially enhances the final decision quality over sequential baselines but also exhibits robust adaptability across diverse scenarios in closed-loop decision-making problems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
