3 papers
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…
eess.SY2023
Thompson Sampling for Parameterized Markov Decision Processes with Uninformative Actions
Michael Gimelfarb, Michael Jong Kim
We study parameterized MDPs (PMDPs) in which the key parameters of interest are unknown and must be learned using Bayesian inference. One key defining feature of such models is the…