From Contextual Data to Newsvendor Decisions: On the Actual Performance of Data-Driven Algorithms

Understand

In this work, we study how the relevance/quality and quantity of past data influence performance by analyzing a contextual Newsvendor problem, in which a decision-maker trades off between underage and overage costs under uncertain demand.

• We consider a setting in which past demands observed under ``close by'' contexts come from close by distributions and analyze the performance of data-driven algorithms through a notion of context-dependent worst-case expected regret.
• We analyze the broad class of Weighted Empirical Risk Minimization (WERM) policies which weigh past data according to their similarity in the contextual space.
• This class includes classical policies such as ERM, k-Nearest Neighbors and kernel-based policies.