Decision-focused learning (DFL) integrates predictive modeling and optimization by training predictors to optimize the downstream decision target rather than merely minimizing prediction error. To date, existing DFL methods typically rely on deterministic point predictions, which are often insufficient to capture the intrinsic stochasticity of real-world environments. To address this challenge, we propose the first diffusion-based DFL approach, which trains a diffusion model to represent the distribution of uncertain parameters and optimizes the decision by solving a stochastic optimization with samples drawn from the diffusion model. Our contributions are twofold. First, we formulate diffusion DFL using the reparameterization trick, enabling end-to-end training through diffusion. While effective, it is memory and compute-intensive due to the need to differentiate through the diffusion sampling process. Second, we propose a lightweight score function estimator that uses only several forward diffusion passes and avoids backpropagation through the sampling. This follows from our results that backpropagating through stochastic optimization can be approximated by a weighted score function formulation. We empirically show that our diffusion DFL approach consistently outperforms strong baselines in decision quality. The source code for all experiments is available at the project repository: https://github.com/GT-KOALA/Diffusion_DFL.

翻译：决策聚焦学习（DFL）通过训练预测器以优化下游决策目标而非仅最小化预测误差，将预测建模与优化进行整合。迄今为止，现有的DFL方法通常依赖于确定性点预测，这往往不足以捕捉现实环境固有的随机性。为应对这一挑战，我们提出了首个基于扩散的DFL方法，该方法训练一个扩散模型来表示不确定参数的分布，并通过从扩散模型中采样来求解随机优化问题以优化决策。我们的贡献主要体现在两个方面。首先，我们利用重参数化技巧构建了扩散DFL框架，实现了通过扩散进行端到端训练。该方法虽有效，但由于需要对扩散采样过程进行微分，其内存和计算开销较大。其次，我们提出了一种轻量化的得分函数估计器，该估计器仅需若干次前向扩散过程，并避免了采样过程中的反向传播。这源于我们的研究结果：通过随机优化的反向传播可以通过加权得分函数形式进行近似。实验结果表明，我们的扩散DFL方法在决策质量上始终优于强基线模型。所有实验的源代码可在项目仓库获取：https://github.com/GT-KOALA/Diffusion_DFL。