On combining machine learning with decision making

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 com...

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Bibliographic Details
Main Authors: Tulabandhula, Theja (Contributor), Rudin, Cynthia (Contributor)
Other Authors: Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory (Contributor), Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science (Contributor), Sloan School of Management (Contributor)
Format: Article
Language:English
Published: Springer US, 2016-06-16T21:04:26Z.
Subjects:
Online Access:Get fulltext
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100 1 0 |a Tulabandhula, Theja  |e author 
100 1 0 |a Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory  |e contributor 
100 1 0 |a Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science  |e contributor 
100 1 0 |a Sloan School of Management  |e contributor 
100 1 0 |a Tulabandhula, Theja  |e contributor 
100 1 0 |a Rudin, Cynthia  |e contributor 
700 1 0 |a Rudin, Cynthia  |e author 
245 0 0 |a On combining machine learning with decision making 
260 |b Springer US,   |c 2016-06-16T21:04:26Z. 
856 |z Get fulltext  |u http://hdl.handle.net/1721.1/103133 
520 |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. 
520 |a Fulbright U.S. Student Program 
520 |a Xerox Fellowship Program 
520 |a Consolidated Edison Company of New York, inc. 
520 |a MIT Energy Initiative (Seed Fund Program) 
520 |a National Science Foundation (U.S.) (grant IIS-1053407) 
546 |a en 
655 7 |a Article 
773 |t Machine Learning