Vishal Gupta, Michael Huang, Paat Rusmevichientong
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引用次数: 0
Abstract
In many modern large-scale decision-making problems, data can be scarce. As a result, traditional methods such as cross-validation perform poorly in evaluating the performance of decision-making policies. In “Debiasing In-Sample Policy Performance for Small-Data, Large-Scale Optimization,” Gupta, Huang, and Rusmevichientong propose a novel estimator of the out-of-sample performance for a policy in data-driven optimization. Unlike cross-validation, their approach avoids sacrificing training data for evaluation. As a result, they theoretically show the estimator is asymptotically unbiased as the problem size grows. Furthermore, they show that the estimator is asymptotically optimal when applied to more specialized “weakly coupled” optimization problems. Finally, using a case study on dispatching emergency medical response services, they demonstrate their proposed method provides more accurate estimates of out-of-sample performance and selects better policies.
期刊介绍:
Operations Research publishes quality operations research and management science works of interest to the OR practitioner and researcher in three substantive categories: methods, data-based operational science, and the practice of OR. The journal seeks papers reporting underlying data-based principles of operational science, observations and modeling of operating systems, contributions to the methods and models of OR, case histories of applications, review articles, and discussions of the administrative environment, history, policy, practice, future, and arenas of application of operations research.