Bayesian Multitask Learning with Latent Hierarchies
This work addresses multitask learning and domain adaptation by sharing classifier and covariance structures, but it is incremental as it builds on prior hierarchical models.
The paper tackled the problem of learning multiple hypotheses for related tasks by proposing a Bayesian multitask learning model with latent hierarchies, which subsumes several existing models and performs well on three real-world datasets.
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.