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This paper proposes a robust variant of smart predict-then-optimize that accounts for feature perturbations, providing a convex surrogate with theoretical guarantees and demonstrating superior performance over standard methods.
Proposes learned predictive ambiguity sets (LPAS) for distributionally robust optimization, where a deep contextual model outputs a nominal scenario distribution, state-dependent Wasserstein radius, and ground metric, trained with decision loss and calibration. Applied to portfolio optimization on S&P 500 data, the method achieves higher returns and Sharpe ratio with reduced conservatism compared to fixed-radius baselines.