Bayesian Multitask Learning with Latent Hierarchies

2014/08/09 by Hal Daumé, Hal Daume III, Daume, Hal
Computer Science · Mathematics · #Domain Adaptation and Few-Shot Learning #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning and Algorithms #Machine Learning and Data Classification #cs.LG #stat.ML

paper · pdf · doi:10.48550/arxiv.1408.2032

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

arxiv created 2014/08/09 · openalex publication_date 2014/08/09 · arxiv updated 2014/08/12 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

Citations

• Bayesian Agglomerative Clustering with Coalescents
• On the genealogy of large populations

Cited by

• Machine Learning Paradigms for Speech Recognition: An Overview

Related

• Bayesian Multitask Learning with Latent Hierarchies
• Flexible Modeling of Latent Task Structures in Multitask Learning
• Hierarchical Bayesian Bandits
• Hierarchical Latent Structure Learning through Online Inference
• Reconciling meta-learning and continual learning with online mixtures of tasks