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Learning Predictive Models for Optimization

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dc.contributor.advisor Dr. Matthew Dailey (Co-chair) Prof. Peter Haddawy (Co-chair)
dc.contributor.author Noor, Waheed
dc.contributor.other Dr. Poompat Saengudomlert (Member)
dc.date.accessioned 2015-01-20T06:49:01Z
dc.date.available 2015-01-20T06:49:01Z
dc.date.issued 2013-12
dc.identifier.other AIT Diss no.CS-13-06 en_US
dc.identifier.uri http://www.cs.ait.ac.th/xmlui/handle/123456789/668
dc.description.abstract Probabilistic predictive models are often used in decision optimization applications. Optimal decision making in these applications critically depends on the performance of the predictive models, especially the accuracy of their probability estimates. In this paper, we propose a probabilistic model for revenue maximization and cost min- imization across applications in which a decision making agent is faced with a group of possible customers and either o ers a variable discount on a product or service or expends a variable cost to attract positive responses. The model is based directly on optimizing expected revenue and makes explicit the relationship between revenue and the customer's response behavior. We derive an expectation maximization (EM) procedure for learning the parameters of the model from historical data, prove that the model is asymptotically insensitive to selection bias in historical decisions, and demonstrate in a series of experiments the method's utility for optimizing nancial aid decisions at an international institute of higher learning. en_US
dc.description.sponsorship University of Balochistan Quetta, Pakistan AIT Fellowship en_US
dc.publisher AIT en_US
dc.title Learning Predictive Models for Optimization en_US
dc.type Dissertation en_US


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