Dissecting the Impact of Model Misspecification in Data-driven Optimization

Dissecting the Impact of Model Misspecification in Data-driven OptimizationOnline

Data-driven optimization aims to translate machine learning predictions into decisions, but the right way to do so depends on whether the model is correctly specified. This talk dissects when the standard two-stage estimate-then-optimize approach outperforms integrated estimation-optimization, and when it does not, drawing the line in terms of how misspecified the predictor is. The speakers also connect to related work on the bias-variance tradeoff under local misspecification, with implications for learning-based decision systems in pricing, retail, and revenue management.

Operations ResearchMachine LearningData-driven Optimization

Speakers: Prof. Adam N. Elmachtoub, Haixiang Lan (Columbia University)

Prof. Adam N. Elmachtoub is an Associate Professor of Industrial Engineering and Operations Research at Columbia University, where he is also a member of the Data Science Institute and DAPLab. His research sits at the intersection of machine learning and operations research, with applications in retail, logistics, pricing, and supply chain management. He received his B.S. from Cornell and Ph.D. from MIT, both in operations research. He is joined by Haixiang Lan, a PhD student in Columbia IEOR co-advised by Adam Elmachtoub and Henry Lam, whose research focuses on data-driven decision-making under uncertainty, uncertainty quantification, and robust optimization.