Before revision:  we study the out-of-sample properties of robust empirical optimization and develop a theory for data-driven calibration of the robustness parameter for worst-case maximization problems with concave reward functions . Building on the intuition that robust optimization reduces the sensitivity of the expected reward to errors in the model by controlling the spread of the reward distribution,
After revision:  we study the out-of-sample properties of robust empirical optimization and develop a theory for data-driven calibration of the non-negative "robustness parameter" \delta that controls the size of the deviations from the nominal model . Building on the intuition that robust optimization reduces the sensitivity of the expected reward to errors in the model by controlling the spread of the reward distribution,