Multi-Task Averaging
Abstract
We present a multi-task learning approach to jointly estimate the means of multiple independent data sets. The proposed multi-task averaging (MTA) algorithm results in a convex combination of the single-task averages. We derive the optimal amount of regularization, and show that it can be effectively estimated. Simulations and real data experiments demonstrate that MTA both maximum likelihood and James-Stein estimators, and that our approach to estimating the amount of regularization rivals cross-validation in performance but is more computationally efficient.
Cite
Text
Feldman et al. "Multi-Task Averaging." Neural Information Processing Systems, 2012.Markdown
[Feldman et al. "Multi-Task Averaging." Neural Information Processing Systems, 2012.](https://mlanthology.org/neurips/2012/feldman2012neurips-multitask/)BibTeX
@inproceedings{feldman2012neurips-multitask,
title = {{Multi-Task Averaging}},
author = {Feldman, Sergey and Gupta, Maya and Frigyik, Bela},
booktitle = {Neural Information Processing Systems},
year = {2012},
pages = {1169-1177},
url = {https://mlanthology.org/neurips/2012/feldman2012neurips-multitask/}
}