Generalization Bounds in the Predict-then-Optimize Framework

NeurIPS 2019 Othman El BalghitiAdam N. ElmachtoubPaul GrigasAmbuj Tewari

The predict-then-optimize framework is fundamental in many practical settings: predict the unknown parameters of an optimization problem, and then solve the problem using the predicted values of the parameters. A natural loss function in this environment is to consider the cost of the decisions induced by the predicted parameters, in contrast to the prediction error of the parameters... (read more)

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