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

2025-11-11

AI Summary

Existing sequential decision-focused learning (S-DFL) suffers from unidirectional prediction→optimization pipelines, limiting its ability to model bidirectional feedback in complex interactive decision-making scenarios. To address this, we propose recursive decision-focused learning (R-DFL), the first framework to establish a closed-loop, iterative interaction between prediction and optimization modules, enabling end-to-end joint modeling. R-DFL unifies explicit unrolling with implicit differentiation based on fixed-point equations, ensuring both high-precision and computationally efficient gradient propagation through the optimization layer. Empirically, R-DFL achieves significant improvements over S-DFL on benchmark tasks—including the newsvendor problem and bipartite matching—yielding markedly superior decision quality. Moreover, it demonstrates strong cross-scenario generalization capability, validating its robustness beyond task-specific training distributions.

Technology Category

• Search and Optimization: Learning to Search
• Reasoning under Uncertainty: Sequential Decision Making
• Natural Language Processing: Learning & Optimization for NLP

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Responsible Web: Machine-in-the-loop, human agency and autonomy

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.

Problem

• Enhancing decision quality through bidirectional prediction-optimization feedback
• Overcoming sequential limitations in decision-focused learning frameworks
• Developing efficient gradient methods for recursive decision-making systems

Innovation

• Introduces bidirectional feedback between optimization and prediction
• Extends explicit unrolling and implicit differentiation methods
• Enhances decision quality and adaptability in diverse scenarios