Smart Surrogate Losses for Contextual Stochastic Linear Optimization with Robust Constraints

This research extends contextual stochastic linear optimization to include inequality constraints dependent on uncertain parameters predicted by machine learning models, using conformal prediction-based uncertainty sets. The authors propose the SPO-RC loss and its convex surrogate SPO-RC+ to improve decision-making under constraint uncertainty, demonstrating enhanced performance through experiments and bias correction techniques.