A robust optimization framework trains interpretable decision-tree surrogates for optimization problems under budgeted observation perturbations, with exact and heuristic solution methods and NP-hardness analysis.
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Towards Robust Interpretable Surrogates for Optimization
A robust optimization framework trains interpretable decision-tree surrogates for optimization problems under budgeted observation perturbations, with exact and heuristic solution methods and NP-hardness analysis.