Residuals-based distributionally robust optimization with covariate information
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Publication:6355120
arXiv2012.01088MaRDI QIDQ6355120
Güzin Bayraksan, James R. Luedtke, Rohit Kannan
Publication date: 2 December 2020
Abstract: We consider data-driven approaches that integrate a machine learning prediction model within distributionally robust optimization (DRO) given limited joint observations of uncertain parameters and covariates. Our framework is flexible in the sense that it can accommodate a variety of regression setups and DRO ambiguity sets. We investigate asymptotic and finite sample properties of solutions obtained using Wasserstein, sample robust optimization, and phi-divergence-based ambiguity sets within our DRO formulations, and explore cross-validation approaches for sizing these ambiguity sets. Through numerical experiments, we validate our theoretical results, study the effectiveness of our approaches for sizing ambiguity sets, and illustrate the benefits of our DRO formulations in the limited data regime even when the prediction model is misspecified.
Has companion code repository: https://github.com/rohitkannan/er-dro
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