Author

Andrés Gómez

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Preprint Aug 2026

Coordinate Optimality Reformulation for Mixed-Integer Convex Programs with Indicators

This work develops the main components of CORe, including coordinate-wise optimality conditions, closed-form characterizations, and disjunctive reformulations, and demonstrates the framework across multiple problem families, including quadratic problems and robust single-index models.

Tong Xu, S. Fattahi, Andrés Gómez et al. · 0 citations

#machine learning Review Sep 2026

Learning Risk Scores Robust to Unobserved Confounders

We consider the problem of learning risk scores to prioritize individuals for scarce resources or interventions, from historical observational data affected by unobserved confounding. Decisions about who receives scarce resources are often guided by risk scores based on recorded characteristics, such as responses to a...

Ryan Edmonds, Ying-Xiao Ye, S. Aghaei et al. · 0 citations

Preprint Aug 2026

Convexification of mixed-integer quadratic optimization via decision diagrams

A unified framework, based on decision diagrams, is proposed that serves both to solve the associated optimization problems and to construct ideal conic quadratic extended formulations of the closure of the convex hull of the underlying mixed-integer set.

Soobin Choi, S. Fattahi, Andrés Gómez et al. · 1 citation