Decision-Focused Learning: When and Why Traditional Prediction Models Fail
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 statist...
| Authors | Mo Liu |
|---|---|
| Primary category | cs.LG |
| Published | 19 June 2026 |
| Last updated at source | 19 June 2026 |
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