主办方：中国科学技术大学科技商学院

协办方：中国科学技术大学管理学院，国际金融研究院

承办方：现代物流与供应链安徽省重点实验室

Speaker: Prof. Adam N. Elmachtoub

Time: 21: 30-23:30, 8th December 2022 (Beijing time)

Place: https://meeting.tencent.com/dm/L2o5imzwg0VR

Zoom Meeting ID: 918 3063 2834

Code: 209161

Abstract: Many real-world analytics problems involve two significant challenges: prediction and optimization. Because of the typically complex nature of each challenge, the standard paradigm is predict-then-optimize. By and large, machine learning tools are intended to minimize prediction error and do not account for how the predictions will be used in the downstream optimization problem. In contrast, we propose a new and very general framework, called Smart “Predict, then Optimize” (SPO), which directly leverages the optimization problem structure—that is, its objective and constraints—for designing better prediction models. A key component of our framework is the SPO loss function, which measures the decision error induced by a prediction. Training a prediction model with respect to the SPO loss is computationally challenging, and, thus, we derive, using duality theory, a convex surrogate loss function, which we call the SPO+ loss. Most importantly, we prove that the SPO+ loss is statistically consistent with respect to the SPO loss under mild conditions. Our SPO+ loss function can tractably handle any polyhedral, convex, or even mixed-integer optimization problem with a linear objective. Numerical experiments on shortest-path and portfolio-optimization problems show that the SPO framework can lead to significant improvement under the predict-then-optimize paradigm, in particular, when the prediction model being trained is misspecified. We find that linear models trained using SPO+ loss tend to dominate random-forest algorithms, even when the ground truth is highly nonlinear.

Bio: Adam Elmachtoub is an Associate Professor of Industrial Engineering and Operations Research at Columbia University. He received his B.S. degree from Cornell and his Ph.D. from MIT, both in operations research. He has received an NSF CAREER Award, IBM Faculty Award, 1st place in the INFORMS JFIG (Junior Faculty) Paper Competition, Great Teacher Award from the Society of Columbia Graduates, and was on Forbes 30 under 30 in science. His research have been published in top journals such as Management Science, Operations Research, Manufacturing & Service Operations Management, Production and Operations Management, Mathematics of Operations Research, Mathematical Programming. He is the Associate Editor for Management Science, Manufacturing & Service Operations Management, Service Science, and Senior Editor for Production and Operations Management.