From Sequential to Recursive: Enhancing Decision-Focused Learning with Bidirectional Feedback

• URL: http://arxiv.org/abs/2511.08035v1
• Date: Wed, 12 Nov 2025 01:35:40 GMT
• Title: From Sequential to Recursive: Enhancing Decision-Focused Learning with Bidirectional Feedback
• Authors: Xinyu Wang, Jinxiao Du, Yiyang Peng, Wei Ma,
• Abstract summary: Decision-focused learning (DFL) has emerged as a powerful end-to-end alternative to conventional predict-then-optimize (PTO) pipelines.<n>Existing DFL frameworks are limited by their strictly sequential structure, referred to as sequential DFL (S-DFL)
• Score: 25.1037007382501
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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