2025/05/06 by Noah Schutte, Schutte, Noah, Grigorii Veviurko +5
Computer Science · Decision Sciences · #Advanced Multi-Objective Optimization Algorithms #Bayesian Modeling and Causal Inference #FOS: Computer and information sciences #FOS: Mathematics #Forecasting Techniques and Applications #Machine Learning (cs.LG) #Optimization and Control (math.OC)
paper · pdf · doi:10.48550/arxiv.2505.03953
openalex publication_date 2025/05/06 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
When solving optimization problems under uncertainty with contextual data, utilizing machine learning to predict the uncertain parameters' values is a popular and effective approach. Decision-focused learning (DFL) aims at learning a predictive model such that decision quality, instead of prediction accuracy, is maximized. Common practice is to predict a single scenario representing the uncertain parameters, implicitly assuming that there exists a deterministic problem approximation (proxy) that allows for optimal decision-making. The opposite has also been considered, where the underlying distribution is estimated with a parameterized distribution. However, little is known about when either choice is valid. This paper investigates for the first time problem properties that justify using a certain decision proxy. Using this, we present alternative decision proxies for DFL, with little or no compromise on the complexity of the learning task. We show the effectiveness of presented approaches in experiments on continuous and discrete problems, as well as problems with uncertainty in the objective function and in the constraints.