On combining machine learning with decision making

Published

Conference Paper

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. © 2014 The Author(s).

Full Text

Duke Authors

Cited Authors

  • Tulabandhula, T; Rudin, C

Published Date

  • January 1, 2014

Published In

Volume / Issue

  • 97 / 1-2

Start / End Page

  • 33 - 64

Electronic International Standard Serial Number (EISSN)

  • 1573-0565

International Standard Serial Number (ISSN)

  • 0885-6125

Digital Object Identifier (DOI)

  • 10.1007/s10994-014-5459-7

Citation Source

  • Scopus