🤖 AI Summary
This work systematically investigates decision-focused learning (DFL) in the context of stochastic linear programming, revealing that under the conventional “predict-then-optimize” paradigm, improved prediction accuracy does not necessarily translate into better downstream decision quality. The study demonstrates fundamental limitations of standard statistical learning approaches and common data collection strategies—along with distributional metrics such as Wasserstein distance—when applied to optimization tasks. By developing a unified framework that jointly models prediction and optimization, the paper clarifies the essential differences between DFL and traditional predictive modeling. Building on these insights, it proposes novel methods explicitly designed to optimize decision performance, thereby laying foundational groundwork for theory and tools in decision-oriented machine learning.
📝 Abstract
Plugging predictions of unknown parameters into downstream optimization problems, often referred to as the ``predict-then-optimize'' paradigm, has long been a standard approach in decision-making under uncertainty. However, improved predictive accuracy does not, in general, translate into improved decision quality. This disconnect has motivated growing interest in decision-focused learning (DFL) within the operations research community. This tutorial reviews recent developments in DFL and highlights key methodological insights, with a particular focus on stochastic linear programming as the downstream decision-making problem. We discuss why several widely used tools in traditional statistical learning are not directly suited to decision-focused settings and must be rethought, including (i) data collection strategies driven purely by predictive uncertainty and (ii) distributional distance measures such as the Wasserstein distance. We summarize properties of DFL that distinguish it from conventional predictive modeling and provide insights into the development of new decision-focused tools.