Learning and Decision-Making with Data: Optimal Formulations and Phase Transitions

Understand

We study the problem of designing optimal learning and decision-making formulations when only historical data is available.

• Prior work typically commits to a particular class of data-driven formulation and subsequently tries to establish out-of-sample performance guarantees.
• We take here the opposite approach.
• We define first a sensible yard stick with which to measure the quality of any data-driven formulation and subsequently seek to find an optimal such formulation.