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.
Cite
Text
Iii. "Bayesian Multitask Learning with Latent Hierarchies." Conference on Uncertainty in Artificial Intelligence, 2009.Markdown
[Iii. "Bayesian Multitask Learning with Latent Hierarchies." Conference on Uncertainty in Artificial Intelligence, 2009.](https://mlanthology.org/uai/2009/iii2009uai-bayesian/)BibTeX
@inproceedings{iii2009uai-bayesian,
title = {{Bayesian Multitask Learning with Latent Hierarchies}},
author = {Iii, Hal Daumé},
booktitle = {Conference on Uncertainty in Artificial Intelligence},
year = {2009},
pages = {135-142},
url = {https://mlanthology.org/uai/2009/iii2009uai-bayesian/}
}