Residuals-based distributionally robust optimization with covariate information

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dc.contributor.authorKannan, Rohiten
dc.contributor.authorBayraksan, Guezinen
dc.contributor.authorLuedtke, James R.en
dc.date.accessioned2025-02-18T13:15:15Zen
dc.date.available2025-02-18T13:15:15Zen
dc.date.issued2023-09-26en
dc.description.abstractWe 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.en
dc.description.versionAccepted versionen
dc.format.extentPages 369-425en
dc.format.extent57 page(s)en
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.1007/s10107-023-02014-7en
dc.identifier.eissn1436-4646en
dc.identifier.issn0025-5610en
dc.identifier.issue1-2en
dc.identifier.orcidKannan, Rohit [0000-0002-7963-7682]en
dc.identifier.urihttps://hdl.handle.net/10919/124626en
dc.identifier.volume207en
dc.language.isoenen
dc.publisherSpringeren
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectData-driven stochastic programmingen
dc.subjectDistributionally robust optimizationen
dc.subjectWasserstein distanceen
dc.subjectPhi-divergencesen
dc.subjectCovariatesen
dc.subjectMachine learningen
dc.subjectConvergence rateen
dc.subjectLarge deviationsen
dc.titleResiduals-based distributionally robust optimization with covariate informationen
dc.title.serialMathematical Programmingen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten
dc.type.otherArticleen
dc.type.otherJournalen
pubs.organisational-groupVirginia Techen
pubs.organisational-groupVirginia Tech/Engineeringen
pubs.organisational-groupVirginia Tech/Engineering/Industrial and Systems Engineeringen
pubs.organisational-groupVirginia Tech/All T&R Facultyen
pubs.organisational-groupVirginia Tech/Engineering/COE T&R Facultyen

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