题　目：Optimize-via-Estimate: How to make finite-sample corrections for sampling uncertainty in data-driven optimization

嘉　宾：Gar Goei Loke 副教授 杜伦大学

主持人：江浦平 助理教授 上海交通大学安泰经济与管理学院

时　间：2025年7月23日（周三）14:00-15:30

地　点：安泰楼B207室

内容简介：

We propose a novel procedure for correcting for the effects of sampling and parameter uncertainty in data-driven convex optimization. We consider the data-driven setting wherein the distribution lies in a parametric family with unknown parameter. The decision-maker possesses a historical data set, from which solves a prescriptive solution as a function mapping such a data set to decisions, and measures its out-of-sample performance as an average over data sets of the same size under the sampling distribution. As the parameter is unknown to the decision-maker, we argue that this problem is ill-defined and further prove that there cannot exist solutions generalizably optimal over all parameters. As such, we formulate the problem as a weighted multi-criteria optimization with weights defined by a density (termed 'prior') over the uncertain parameter, and call its optimal solution 'prior optimal'. We prove that sufficient conditions for prior optimality reduces to functions of the sufficient statistic of the parametric family, and this function can be solved as a point-wise optima of a corrected function of the original objective, with terms accounting for parameter uncertainty and sampling uncertainty respectively. The correction for sampling interpretation is verified by a close to (but not) zero ex-ante regret against a perfect information oracle in numerical illustrations on the newsvendor and portfolio optimization problems.

演讲人简介：

Dr Gar Goei Loke is currently an associate professor with the operations management and operations research group at Durham University Business School. Prior to this, he has spent time as an assistant professor at both Rotterdam School of Management (Erasmus University) and National University of Singapore (NUS) Business School. He obtained his PhD from the Department of Mathematics at NUS, under the tutelage of Prof Kim Chuan Toh and Prof Melvyn Sim. His research interests are in the area of robust optimization. More recently, he has started examining topics in data-driven optimization such as how to meld the predictive and prescriptive. While some of his works are theoretical, he also finds applications in healthcare, energy / utilities and public sector. He has publications in Operations Research and Manufacturing & Service Operations Management. Prior to his career, he was the team lead of data science in the Prime Minister's Office, Singapore.

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