DFF: Decision-Focused Fine-Tuning for Smarter Predict-Then-Optimize with Limited Data

作者：Jiaqi Yang, Enming Liang, Zicheng Su, Zhichao Zou, Peng Zhen, Jian‐Hua Guo, Wanjing Ma, Kun An · 发表于：Proceedings of the AAAI Conference on Artificial Intelligence · 年份：2025 · DOI：10.1609/aaai.v39i25.34891 · 被引用次数：4 · 研究领域：Smart Grid Energy Management、Machine Learning and Data Classification

Decision-focused learning (DFL) offers an end-to-end approach to the predict-then-optimize (PO) framework by training predictive models directly on decision loss (DL), enhancing decision-making performance within PO contexts. However, the implementation of DFL poses distinct challenges. Primarily, DL can result in deviation from the physical significance of the predictions under limited data. Additionally, some predictive models are non-differentiable or black-box, which cannot be adjusted using gradient-based methods. To tackle the above challenges, we propose a novel framework, Decision-Focused Fine-tuning (DFF), which embeds the DFL module into the PO pipeline via a novel bias correction module. DFF is formulated as a constrained optimization problem that maintains the proximity of the DL-enhanced model to the original predictive model within a defined trust region. We theoretically prove that DFF strictly confines prediction bias within a predetermined upper bound, even with limited datasets, thereby substantially reducing prediction shifts caused by DL under limited data. Furthermore, the bias correction module can be integrated into diverse predictive models, enhancing adaptability to a broad range of PO tasks. Extensive evaluations on synthetic and real-world datasets, including network flow, portfolio optimization, and resource allocation problems with different predictive models, demonstrate that DFF not only improves decision performance but also adheres to fine-tun...