ICML 2017poster215 citations
Learning Algorithms for Active Learning
Philip Bachman, Alessandro Sordoni, Adam Trischler
Abstract
We introduce a model that learns active learning algorithms via metalearning. For a distribution of related tasks, our model jointly learns: a data representation, an item selection heuristic, and a prediction function. Our model uses the item selection heuristic to construct a labeled support set for training the prediction function. Using the Omniglot and MovieLens datasets, we test our model in synthetic and practical settings.
BibTeX
@InProceedings{pmlr-v70-bachman17a,
title = {Learning Algorithms for Active Learning},
author = {Philip Bachman and Alessandro Sordoni and Adam Trischler},
booktitle = {Proceedings of the 34th International Conference on Machine Learning},
pages = {301--310},
year = {2017},
editor = {Precup, Doina and Teh, Yee Whye},
volume = {70},
series = {Proceedings of Machine Learning Research},
month = {06--11 Aug},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v70/bachman17a/bachman17a.pdf},
url = {https://proceedings.mlr.press/v70/bachman17a.html},
abstract = {We introduce a model that learns active learning algorithms via metalearning. For a distribution of related tasks, our model jointly learns: a data representation, an item selection heuristic, and a prediction function. Our model uses the item selection heuristic to construct a labeled support set for training the prediction function. Using the Omniglot and MovieLens datasets, we test our model in synthetic and practical settings.}
}