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...

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Bibliographic Details
Main Authors: Tulabandhula, Theja (Contributor), Rudin, Cynthia (Contributor)
Other Authors: Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science (Contributor), Massachusetts Institute of Technology. Operations Research Center (Contributor), Sloan School of Management (Contributor)
Format: Article
Language:English
Published: Association for Computing Machinery (ACM), 2013-10-18T13:26:17Z.
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Online Access:Get fulltext
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100 1 0 |a Tulabandhula, Theja  |e author 
100 1 0 |a Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science  |e contributor 
100 1 0 |a Massachusetts Institute of Technology. Operations Research Center  |e contributor 
100 1 0 |a Sloan School of Management  |e contributor 
100 1 0 |a Rudin, Cynthia  |e contributor 
100 1 0 |a Tulabandhula, Theja  |e contributor 
700 1 0 |a Rudin, Cynthia  |e author 
245 0 0 |a Machine Learning with Operational Costs 
260 |b Association for Computing Machinery (ACM),   |c 2013-10-18T13:26:17Z. 
856 |z Get fulltext  |u http://hdl.handle.net/1721.1/81426 
520 |a 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. 
520 |a Fulbright Program (Science and Technology Fellowship) 
520 |a Solomon Buchsbaum Research Fund 
520 |a National Science Foundation (U.S.) (Grant IIS-1053407) 
546 |a en_US 
655 7 |a Article 
773 |t Journal of Machine Learning Research