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math.OC2026
Posterior and Likelihood Sensitivity in Bayesian Distributionally Robust Optimization
Jun-ya Gotoh, Andrew E. B. Lim, Michael Jong Kim
We introduce the notion of worst-case posterior and worst-case likelihood sensitivity. These measure, respectively, the sensitivity of the expected cost to worst-case perturbations…
math.OC2025
Robustness Measures in Distributionally Robust Optimization
Jun-ya Gotoh, Michael Jong Kim, Andrew E. B. Lim
Distributionally Robust Optimization (DRO) is a worst-case approach to decision making when there is model uncertainty. It is also well known that for certain uncertainty sets, DRO…
math.OC2021
A data-driven approach to beating SAA out-of-sample
Jun-ya Gotoh, Michael Jong Kim, Andrew E. B. Lim
While solutions of Distributionally Robust Optimization (DRO) problems can sometimes have a higher out-of-sample expected reward than the Sample Average Approximation (SAA), there…