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Scalable Loss-calibrated Bayesian Decision Theory and Preference Learning

Abbasnejad, Ehsan

Description

Bayesian decision theory provides a framework for optimal action selection under uncertainty given a utility function over actions and world states and a distribution over world states. The application of Bayesian decision theory in practice is often limited by two problems: (1) in application domains such as recommendation, the true utility function of a user is a priori unknown and must be learned from user interactions; and (2) computing...[Show more]

CollectionsOpen Access Theses
Date published: 2017
Type: Thesis (PhD)
URI: http://hdl.handle.net/1885/118285

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