Bayesian Multitask Learning with Latent Hierarchies

Abstract

We learn multiple hypotheses for related tasks under a latent hierarchical relationship between tasks. We exploit the intuition that for domain adaptation, we wish to share classifier structure, but for multitask learning, we wish to share covariance structure. Our hierarchical model is seen to subsume several previously proposed multitask learning models and performs well on three distinct real-world data sets.

Appears in Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence (UAI2009)

References in corpus (2)

• Frustratingly Easy Domain Adaptation
• Bayesian Agglomerative Clustering with Coalescents

Cited by in corpus (4)

• Learning Task Grouping and Overlap in Multi-task Learning
• A Convex Formulation for Learning Task Relationships in Multi-Task Learning
• Tree-Structured Stick Breaking Processes for Hierarchical Data
• Multi-Task Metric Learning on Network Data