Convex Point Estimation using Undirected Bayesian Transfer Hierarchies

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 techniq...

• Type: Conference
• Year: 2008
• Where: UAI
• Authors: Gal Elidan, Benjamin Packer, Geremy Heitz, Daphne Koller
• Keywords: Artificial Intelligence, Hierarchical Bayes, Hierarchical Bayes Framework, Posterior Point, UAI 2008