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|a Tulabandhula, Theja
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|a Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
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|a Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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|a Sloan School of Management
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|a Tulabandhula, Theja
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|a Rudin, Cynthia
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|a Rudin, Cynthia
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|a On combining machine learning with decision making
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|b Springer US,
|c 2016-06-16T21:04:26Z.
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|z Get fulltext
|u http://hdl.handle.net/1721.1/103133
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|a 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.
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|a Fulbright U.S. Student Program
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|a Xerox Fellowship Program
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|a Consolidated Edison Company of New York, inc.
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|a MIT Energy Initiative (Seed Fund Program)
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|a National Science Foundation (U.S.) (grant IIS-1053407)
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|a en
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|a Article
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|t Machine Learning
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