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• W2950682392 abstract "We present a new application and covering number bound for the framework of 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 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." @default.
• W2950682392 created "2019-06-27" @default.
• W2950682392 creator A5040468715 @default.
• W2950682392 creator A5062751346 @default.
• W2950682392 date "2011-04-27" @default.
• W2950682392 modified "2023-09-27" @default.
• W2950682392 title "On Combining Machine Learning with Decision Making" @default.
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• W2950682392 hasPublicationYear "2011" @default.
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