Author

G. Loke

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Open access Oct 2026

Optimize-via-Estimate: How to Make Finite-Sample Corrections for Sampling Uncertainty in Data-Driven Optimization

We propose a novel procedure for correcting for the effects of sampling and parameter uncertainty in data-driven convex optimization. We consider the data-driven setting wherein the distribution lies in a parametric family with unknown parameter. The decision-maker possesses a historical data set, from which solves a p...

G. Loke, Tao-Zeng Zhu, Rui-Ting Zuo · Operational Research · 1 citation

Open access Aug 2026

Decision-Driven Regularization: A Blended Model for Learning and Optimization

This paper proposes a biobjective formulation that balances prediction accuracy and cost minimization, termed decision-driven regularization, which is shown to be numerically superior to other benchmarks, such as ordinary least squares, random forest, XGBoost, SPO+, perturbation gradient, and learning and rank, in the...

G. Loke, Qin-Shen Tang, Yangge Xiao et al. · INFORMS journal on computing · 1 citation