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

[2608.15124] Decision-Driven Regularization: A Blended Model for Learning and Optimization Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Machine Learning arXiv:2608.15124 (cs) [Submitted on 15 Aug 2026] Title: Decision-Driven Regularization: A Blended Model for Learning and Optimization Authors: Gar Goei Loke , Qinshen Tang , Yangge Xiao , Xun Zhang View a PDF of the paper titled Decision-Driven Regularization: A Blended Model for Learning and Optimization, by Gar Goei Loke and Qinshen Tang and Yangge Xiao and Xun Zhang View PDF HTML (experimental) Abstract: In contextual optimization, the decision-maker seeks optimal decisions to minimize a cost function, that varies based on observed features. This context is common in many business applications ranging from on-demand delivery and retail operations to portfolio optimization and inventory management. In this paper, we study the learning and optimization approach, which first learns how outcomes result from the features, and then selects optimal decisions based on these outcomes. We focus on the integrated learning and optimization literature, and identify that a lack of control for prediction accuracy can lead to overfitting and a loss of decision effectiveness against simple separate learning and optimization models. Instead, we propose a bi-objective formulation that balances prediction accuracy and cost minimization, termed decision-driven regularization. It also addresses ambiguity in the definition of the cost function via a surrogate that depends on a new hyperparameter. We additionally show that alternative perspectives for formulating the problem, namely robust optimization and regret minimization, lead to models that are closely related to our proposed model. As a consequence, our framework generalizes models such as SPO+. Our model is shown to be numerically superior to other benchmarks, such as OLS, Random Forest, XGBoost, SPO+, Perturbation Gradient, and Learning and Rank, in our synthetic studies. Comments: 42 pages (including appendix), 7 figures in main, journal paper Subjects: Machine Learning (cs.LG) ; Optimization and Control (math.OC) Cite as: arXiv:2608.15124 [cs.LG] (or arXiv:2608.15124v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2608.15124 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Qinshen Tang [ view email ] [v1] Sat, 15 Aug 2026 08:49:33 UTC (367 KB) Full-text links: Access Paper: View a PDF of the paper titled Decision-Driven Regularization: A Blended Model for Learning and Optimization, by Gar Goei Loke and Qinshen Tang and Yangge Xiao and Xun Zhang View PDF HTML (experimental) TeX Source view license Current browse context: cs.LG < prev | next > new | recent | 2026-08 Change to browse by: cs math math.OC References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... 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