Machine Learning with Operational Costs
This work proposes a way to align statistical modeling with decision making. We provide a method that propagates the uncertainty in predictive modeling to the uncertainty in operational cost, where operational cost is the amount spent by the practitioner in solving the problem. The method allows us...
| Main Authors: | , |
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| Other Authors: | |
| Format: | Article |
| Language: | en_US |
| Published: |
Association for Computing Machinery (ACM)
2013
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| Online Access: | http://hdl.handle.net/1721.1/81426 |
| _version_ | 1826201234244108288 |
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| author | Tulabandhula, Theja Rudin, Cynthia |
| author2 | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science |
| author_facet | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Tulabandhula, Theja Rudin, Cynthia |
| author_sort | Tulabandhula, Theja |
| collection | MIT |
| description | This work proposes a way to align statistical modeling with decision making. We provide a method that propagates the uncertainty in predictive modeling to the uncertainty in operational cost, where operational cost is the amount spent by the practitioner in solving the problem. The method allows us to explore the range of operational costs associated with the set of reasonable statistical models, so as to provide a useful way for practitioners to understand uncertainty. To do this, the operational cost is cast as a regularization term in a learning algorithm’s objective function, allowing either an optimistic or pessimistic view of possible costs, depending on the regularization parameter. From another perspective, if we have prior knowledge about the operational cost, for instance that it should be low, this knowledge can help to restrict the hypothesis space, and can help with generalization. We provide a theoretical generalization bound for this scenario. We also show that learning with operational costs is related to robust optimization. |
| first_indexed | 2024-09-23T11:48:20Z |
| format | Article |
| id | mit-1721.1/81426 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T11:48:20Z |
| publishDate | 2013 |
| publisher | Association for Computing Machinery (ACM) |
| record_format | dspace |
| spelling | mit-1721.1/814262022-10-01T06:09:17Z Machine Learning with Operational Costs Tulabandhula, Theja Rudin, Cynthia Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology. Operations Research Center Sloan School of Management Rudin, Cynthia Tulabandhula, Theja This work proposes a way to align statistical modeling with decision making. We provide a method that propagates the uncertainty in predictive modeling to the uncertainty in operational cost, where operational cost is the amount spent by the practitioner in solving the problem. The method allows us to explore the range of operational costs associated with the set of reasonable statistical models, so as to provide a useful way for practitioners to understand uncertainty. To do this, the operational cost is cast as a regularization term in a learning algorithm’s objective function, allowing either an optimistic or pessimistic view of possible costs, depending on the regularization parameter. From another perspective, if we have prior knowledge about the operational cost, for instance that it should be low, this knowledge can help to restrict the hypothesis space, and can help with generalization. We provide a theoretical generalization bound for this scenario. We also show that learning with operational costs is related to robust optimization. Fulbright Program (Science and Technology Fellowship) Solomon Buchsbaum Research Fund National Science Foundation (U.S.) (Grant IIS-1053407) 2013-10-18T13:26:17Z 2013-10-18T13:26:17Z 2013-07 2012-08 Article http://purl.org/eprint/type/JournalArticle 1532-4435 1533-7928 http://hdl.handle.net/1721.1/81426 Tulabandhula, Theja, and Cynthia Rudin. “Machine Learning with Operational Costs.” Journal of Machine Learning Research 14 (2013): 1989–2028. en_US http://jmlr.org/papers/volume14/tulabandhula13a/tulabandhula13a.pdf Journal of Machine Learning Research Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use. application/pdf Association for Computing Machinery (ACM) MIT Press |
| spellingShingle | Tulabandhula, Theja Rudin, Cynthia Machine Learning with Operational Costs |
| title | Machine Learning with Operational Costs |
| title_full | Machine Learning with Operational Costs |
| title_fullStr | Machine Learning with Operational Costs |
| title_full_unstemmed | Machine Learning with Operational Costs |
| title_short | Machine Learning with Operational Costs |
| title_sort | machine learning with operational costs |
| url | http://hdl.handle.net/1721.1/81426 |
| work_keys_str_mv | AT tulabandhulatheja machinelearningwithoperationalcosts AT rudincynthia machinelearningwithoperationalcosts |