3 citations · 3 across the 2 of their papers we have counts for
4 papers
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…
Worst-case sensitivity
Jun-ya Gotoh, Michael Jong Kim, Andrew E. B. Lim
We introduce the notion of Worst-Case Sensitivity, defined as the worst-case rate of increase in the expected cost of a Distributionally Robust Optimization (DRO) model when the si…
Calibration of Distributionally Robust Empirical Optimization Models
Jun-Ya Gotoh, Michael Jong Kim, Andrew E. B. Lim
We study the out-of-sample properties of robust empirical optimization problems with smooth -divergence penalties and smooth concave objective functions, and develop a theory fo…
Performance-based regularization in mean-CVaR portfolio optimization
Noureddine El Karoui, Andrew E. B. Lim, Gah-Yi Vahn
We introduce performance-based regularization (PBR), a new approach to addressing estimation risk in data-driven optimization, to mean-CVaR portfolio optimization. We assume the av…