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arXiv AI By Sanjeeb Dash, Soumyadip Ghosh, Joao Goncalves, Mark S. Squillante

Distributionally Robust Regularization of Sparse Integer Programming Trained Learning Models

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The paper proposes a distributionally robust regularization technique for mixed‑integer sparse programming models used in explainable machine learning. It extends recent DRO advances, traditionally applied to convex formulations, to mixed‑integer settings, offering statistical guarantees linked to a sparsity parameter. The method is demonstrated on binary classification models built from feature‑value rule sets.

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