When tree-based models, such as Random Forest or XGBoost, are used in optimization, their formulations are often intractable and unscalable. We propose a scalable approximation of the optimization formulation that can optimize over ensemble tree models in linear time while still capturing over 90% of optimality on a variety of datasets. MOTEM (Method for Optimizing over Tree Ensemble Models) is an algorithm for optimizing an objective function that is determined by an ensemble tree model.

AI systems are increasingly demonstrating their capacity to make better predictions

than humans. Yet, recent empirical studies suggest that experts may doubt the quality of these systems. We explore the extent to which a decision maker (DM) can properly learn whether a machine produces better recommendations, and analyze a dynamic Bayesian model, where a machine performs repeated decision tasks under a DM’s supervision. We fully characterize the conditions at which learning fails and succeeds.

We study data-driven operations management problems with high-dimensional data. The standard approach involves two separate modeling phases: learning a low dimensional statistical model (dimensionality reduction) from data and then optimizing a decision problem with parameters input from said statistical model. We propose a prescriptive dimensionality reduction approach that better aligns the two phases and delivers superior results over standard methods (e.g., principal component analysis).