提出新数据集距离度量，更准确评估预测后优化任务的模型迁移能力。
What is the Right Notion of Distance between Predict-then-Optimize Tasks?
- 引入决策感知的最优传输距离，融合特征、标签与下游决策
- 传统距离在预测后优化任务中无效，新方法能准确预测迁移效果
- 适用于需要优化决策的场景，如供应链、金融建模
在预测后优化（PtO）框架中，模型性能应以决策后悔值衡量，而非预测误差。本文提出一种新型数据集距离——OTD³（Optimal Transport Decision-aware Dataset Distance），将下游优化决策纳入距离计算。实验表明，传统基于特征和标签的距离在PtO设置下缺乏信息量，而OTD³能更有效捕捉模型适应成功程度。我们还推导了基于该距离的特定适应性边界。在三个来自文献的PtO任务中，所提距离均能准确预测模型迁移能力。代码已开源。
原文摘要 · Abstract (English)
Comparing datasets is a fundamental task in machine learning, essential for various learning paradigms-from evaluating train and test datasets for model generalization to using dataset similarity for detecting data drift. While traditional notions of dataset distances offer principled measures of similarity, their utility has largely been assessed through prediction error minimization. However, in Predict-then-Optimize (PtO) frameworks, where predictions serve as inputs for downstream optimization tasks, model performance is measured through decision regret rather than prediction error. In this work, we propose OTD$^3$ (Optimal Transport Decision-aware Dataset Distance), a novel dataset distance that incorporates downstream decisions in addition to features and labels. We show that traditional feature-label distances lack informativeness in PtO settings, while OTD$^3$ more effectively captures adaptation success. We also derive a PtO-specific adaptation bound based on this distance. Empirically, we show that our proposed distance accurately predicts model transferability across three different PtO tasks from the literature. The code is available at https://github.com/paularodr/OTD3.
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