On combining machine learning with decision making
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10994_2014_5459_ReferencePDF.pdf
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2.87 MB
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Author(s)
Tulabandhula, Theja
Rudin, Cynthia
Date Issued
June 2014
Journal
Machine Learning
Publisher
Springer US
Citation
Tulabandhula, Theja, and Cynthia Rudin. “On Combining Machine Learning with Decision Making.” Machine Learning 97, no. 1–2 (June 28, 2014): 33–64
Version
Author's final manuscript
Abstract
We present a new application and covering number bound for the framework of “Machine Learning with Operational Costs (MLOC),” which is an exploratory form of decision theory. The MLOC framework incorporates knowledge about how a predictive model will be used for a subsequent task, thus combining machine learning with the decision that is made afterwards. In this work, we use the MLOC framework to study a problem that has implications for power grid reliability and maintenance, called the Machine Learning and Traveling Repairman Problem (ML&TRP). The goal of the ML&TRP is to determine a route for a “repair crew,” which repairs nodes on a graph. The repair crew aims to minimize the cost of failures at the nodes, but as in many real situations, the failure probabilities are not known and must be estimated. The MLOC framework allows us to understand how this uncertainty influences the repair route. We also present new covering number generalization bounds for the MLOC framework.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Sloan School of Management
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1007/s10994-014-5459-7
