DFF: Decision-Focused Fine-tuning for Smarter Predict-then-Optimize with Limited Data

TL;DR

This paper introduces DFF, a novel fine-tuning framework for predict-then-optimize tasks that maintains prediction accuracy and decision quality under limited data by controlling bias and enabling broad model compatibility.

Contribution

DFF is a new bias correction-based fine-tuning method that integrates decision-focused learning into diverse predictive models within constrained optimization, ensuring robust decision performance.

Findings

DFF reduces prediction bias within a predefined bound.

DFF improves decision quality across synthetic and real datasets.

DFF is adaptable to various models and decision tasks.

Abstract

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-tuning constraints, showcasing robust adaptability across various scenarios.