5. Notes

The work in [10] provides a nice intuitive introduction to robust optimization emphasizing the parallel with static optimization. Another nice treatment [11], focusing on robust empirical risk minimization problem, is designed to give calibrated confidence intervals on performance and provide optimal tradeoffs between bias and variance [12, 13]. f-divergence based performance evaluations are conducted in [11, 14, 15]. The connection between risk-sensitivity measures such as the exponentiated payoff and distributionally robustness can be found in [16]. Distributionally robust optimization and learning are extended to multiple strategic decisionmaking problems i.e., distributionally robust games in [17, 18].
