Score Function Gradient Estimation to Widen the Applicability of Decision-Focused Learning

Researchers propose a new decision-focused learning method using score function gradient estimation and stochastic smoothing to train machine learning models that directly optimize for task performance rather than prediction accuracy. The approach removes restrictive assumptions about problem structure, extending applicability to nonlinear objectives, constrained optimization, and two-stage stochastic problems.

Decision-focused learning represents a fundamental shift in how machine learning models are trained for real-world optimization tasks. Traditional approaches train models to minimize prediction error, but this metric often misaligns with actual operational objectives like cost minimization or regret reduction. This research addresses a critical gap by proposing a generalizable method that works across diverse problem structures without requiring convexity assumptions or constraint restrictions.

The field has struggled with non-informative gradients in combinatorial optimization problems, forcing existing DFL methods to rely on problem-specific surrogates that limit their applicability. By combining stochastic smoothing with score function gradient estimation, this work enables training on arbitrary task losses—a significant theoretical advance. The method's ability to handle uncertain parameters within constraints, not just objectives, opens new possibilities for real-world supply chain, routing, and resource allocation problems where constraint uncertainty is common.

Practically, the approach trades computational efficiency for generality, typically requiring more training epochs than specialized methods. However, experiments demonstrate competitive or superior performance, particularly for constraint uncertainty scenarios that existing methods struggle with. This makes the method valuable for practitioners facing novel problem structures without domain-specific optimization solutions.

Looking ahead, the generalizability of this technique could accelerate adoption of decision-focused learning across industries. The ability to handle two-stage stochastic optimization suggests applications in financial planning, energy markets, and disaster response. Further work on computational efficiency would strengthen the practical case for deployment at scale.

• →Score function gradient estimation removes structural assumptions that limited prior decision-focused learning methods to specific problem types.
• →The method handles uncertain parameters in both objectives and constraints, extending applicability beyond previous DFL approaches.
• →Computational overhead is higher than specialized methods, but solution quality is competitive or superior, especially for constraint uncertainty.
• →Two-stage stochastic optimization support opens applications in finance, energy, and logistics where future uncertainty is inherent.
• →Generalizability across problem structures could democratize task-aware ML model training for complex real-world optimization.