Nonlinear Decision Rules Made Scalable by Nonparametric Liftings

Eojin Han
Eojin Han
[email protected]
https://orcid.org/0000-0002-9455-1000
Mendoza College of Business, University of Notre Dame, Notre Dame, Indiana 46556
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Omid Nohadani
Corresponding Author
Omid Nohadani
[email protected]
https://orcid.org/0000-0001-6332-3403
Artificial Intelligence and Data Science, Benefits Science Technologies, Boston, Massachusetts 02108
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Eojin Han

[email protected]

https://orcid.org/0000-0002-9455-1000

Mendoza College of Business, University of Notre Dame, Notre Dame, Indiana 46556

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,

Corresponding Author

Omid Nohadani

[email protected]

https://orcid.org/0000-0001-6332-3403

Artificial Intelligence and Data Science, Benefits Science Technologies, Boston, Massachusetts 02108

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Published Online:30 Jul 2024https://doi.org/10.1287/mnsc.2024.4988

Supplemental Material

mnsc.2024.4988.sm1.pdf

The replication files for this article are available HERE.

cover image Management Science

Volume 71, Issue 4

April 2025

Pages iv-vi, 2751-3636

Article Information

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Received:October 13, 2022
Accepted:April 13, 2023
Published Online:July 30, 2024

Copyright © 2024, INFORMS

Cite as

Eojin Han; , Omid Nohadani (2024) Nonlinear Decision Rules Made Scalable by Nonparametric Liftings. Management Science 71(4):3449-3471.

https://doi.org/10.1287/mnsc.2024.4988

Keywords

Acknowledgments

The authors are grateful for insightful discussions with S. Pokutta on the extended formulation and with C. Bandi on lifting simplices. The authors also thank the anonymous reviewers whose comments strengthened this work.