Learning Best-in-Class Policies for the Predict-then-Optimize Framework
We propose a novel family of decision-aware surrogate losses, called Perturbation Gradient
(PG) losses, for the predict-then-optimize framework. These losses directly approximate the …
(PG) losses, for the predict-then-optimize framework. These losses directly approximate the …
[PDF][PDF] Decision-Focused Learning with Directional Gradients
We propose a novel family of decision-aware surrogate losses, called Perturbation Gradient
(PG) losses, for the predict-then-optimize framework. The key idea is to connect the …
(PG) losses, for the predict-then-optimize framework. The key idea is to connect the …
Decision-Focused Learning with Directional Gradients
We propose a novel family of decision-aware surrogate losses, called Perturbation Gradient
(PG) losses, for the predict-then-optimize framework. These losses directly approximate the …
(PG) losses, for the predict-then-optimize framework. These losses directly approximate the …
More Data or Better Data? Impact of Costly Data Collection on the Newsvendor Problem
Data-driven algorithms and policies have flourished in many areas of operations
management. While many applications can utilize preexisting data, there are many cases …
management. While many applications can utilize preexisting data, there are many cases …