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Decision-Theoretic Sparsification for Gaussian Process Preference Learning

Abbasnejad, Ehsan; Bonilla, Edwin; Sanner, Scott


We propose a decision-theoretic sparsification method for Gaussian process preference learning. This method overcomes the loss-insensitive nature of popular sparsification approaches such as the Informative Vector Machine (IVM). Instead of selecting a sub

CollectionsANU Research Publications
Date published: 2013
Type: Journal article
Source: Lecture Notes in Artificial Intelligence
DOI: 10.1007/978-3-642-40991-2_33


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