Learning Gaussian Processes from Multiple Tasks
Abstract
We consider the problem of multi-task learning, that is, learning multiple related functions. Our approach is based on a hierarchical Bayesian framework, that exploits the equivalence between parametric linear models and nonparametric Gaussian processes (GPs). The resulting models can be learned easily via an EM-algorithm. Empirical studies on multi-label text categorization suggest that the presented models allow accurate solutions of these multi-task problems.
Cite
Text
Yu et al. "Learning Gaussian Processes from Multiple Tasks." International Conference on Machine Learning, 2005. doi:10.1145/1102351.1102479Markdown
[Yu et al. "Learning Gaussian Processes from Multiple Tasks." International Conference on Machine Learning, 2005.](https://mlanthology.org/icml/2005/yu2005icml-learning/) doi:10.1145/1102351.1102479BibTeX
@inproceedings{yu2005icml-learning,
title = {{Learning Gaussian Processes from Multiple Tasks}},
author = {Yu, Kai and Tresp, Volker and Schwaighofer, Anton},
booktitle = {International Conference on Machine Learning},
year = {2005},
pages = {1012-1019},
doi = {10.1145/1102351.1102479},
url = {https://mlanthology.org/icml/2005/yu2005icml-learning/}
}