Convex point estimation using undirected Bayesian transfer hierarchies

Gal Elidan*, Ben Packer, Geremy Heitz, Daphne Koller

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Scopus citations

Abstract

When related learning tasks are naturally arranged in a hierarchy, an appealing approach for coping with scarcity of instances is that of transfer learning using a hierarchical Bayes framework. As fully Bayesian computations can be difficult and computationally demanding, it is often desirable to use posterior point estimates that facilitate (relatively) efficient prediction. However, the hierarchical Bayes framework does not always lend itself naturally to this maximum aposteriori goal. In this work we propose an undirected reformulation of hierarchical Bayes that relies on priors in the form of similarity measures. We introduce the notion of "degree of transfer" weights on components of these similarity measures, and show how they can be automatically learned within a joint probabilistic framework. Importantly, our reformulation results in a convex objective for many learning problems, thus facilitating optimal posterior point estimation using standard optimization techniques. In addition, we no longer require proper priors, allowing for flexible and straightforward specification of joint distributions over transfer hierarchies. We show that our framework is effective for learning models that are part of transfer hierarchies for two real-life tasks: object shape modeling using Gaussian density estimation and document classification.

Original languageEnglish
Title of host publicationProceedings of the 24th Conference on Uncertainty in Artificial Intelligence, UAI 2008
Pages179-186
Number of pages8
StatePublished - 2008
Externally publishedYes
Event24th Conference on Uncertainty in Artificial Intelligence, UAI 2008 - Helsinki, Finland
Duration: 9 Jul 200812 Jul 2008

Publication series

NameProceedings of the 24th Conference on Uncertainty in Artificial Intelligence, UAI 2008

Conference

Conference24th Conference on Uncertainty in Artificial Intelligence, UAI 2008
Country/TerritoryFinland
CityHelsinki
Period9/07/0812/07/08

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