Bayesian Multitask Learning with Latent Hierarchies
Machine Learning
2014-08-12 v1 Machine Learning
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.
Cite
@article{arxiv.1408.2032,
title = {Bayesian Multitask Learning with Latent Hierarchies},
author = {Hal Daume},
journal= {arXiv preprint arXiv:1408.2032},
year = {2014}
}
Comments
Appears in Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence (UAI2009)