AISTATS 2016poster90 citations
Distributed Multi-Task Learning
Jialei Wang, Mladen Kolar, Nathan Srerbo
Abstract
We consider the problem of distributed multi-task learning, where each machine learns a separate, but related, task. Specifically, each machine learns a linear predictor in high-dimensional space, where all tasks share the same small support. We present a communication-efficient estimator based on the debiased lasso and show that it is comparable with the optimal centralized method.
BibTeX
@InProceedings{pmlr-v51-wang16d,
title = {Distributed Multi-Task Learning},
author = {Wang, Jialei and Kolar, Mladen and Srerbo, Nathan},
booktitle = {Proceedings of the 19th International Conference on Artificial Intelligence and Statistics},
pages = {751--760},
year = {2016},
editor = {Gretton, Arthur and Robert, Christian C.},
volume = {51},
series = {Proceedings of Machine Learning Research},
address = {Cadiz, Spain},
month = {09--11 May},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v51/wang16d.pdf},
url = {https://proceedings.mlr.press/v51/wang16d.html},
abstract = {We consider the problem of distributed multi-task learning, where each machine learns a separate, but related, task. Specifically, each machine learns a linear predictor in high-dimensional space, where all tasks share the same small support. We present a communication-efficient estimator based on the debiased lasso and show that it is comparable with the optimal centralized method.}
}