This study investigates the machine learning tools that are employed to learn data-to-decision mappings. A data-to-decision mapping, once learnt, adopts relevant operational information as input and effectively outputs a reliable decision variable. The operational data used by the machine...

We consider the problem of online pricing with offline data, where the decision maker has in hand pre-existed offline data and then takes online sequential actions to maximize expected cumulative revenue as well as learning the optimal pricing. We focus on a simple and specific problem setting...