A new conformal framework learns polyhedral uncertainty sets tailored to robust optimization objectives, minimizing decision loss while preserving coverage via calibration and independent re-calibration.
We fit ˆfby the same ridge feature regression as in the synthetic experiments
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Learning Polyhedral Conformal Sets for Robust Optimization
A new conformal framework learns polyhedral uncertainty sets tailored to robust optimization objectives, minimizing decision loss while preserving coverage via calibration and independent re-calibration.