Introduces a measure-transformation-based surrogate loss for solver-free training in predict-then-optimize problems, with Fisher consistency and excess risk bounds.
Integrated condi tional estimation-optimization
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Derives generalization bounds for perturbed surrogate policies in combinatorial optimization by decomposing excess risk into perturbation bias controlled by fan-crossing probability, statistical estimation error, and optimization error.
Establishes equivalence conditions between nested and joint risk assessments in contextual optimization, shows policy independence from contextual risk measure under conditions, and proves SAA consistency in RKHS.
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