@conference {13060,
	title = {Unsupervised search-based structured prediction},
	booktitle = {Proceedings of the 26th Annual International Conference on Machine Learning},
	series = {ICML {\textquoteright}09},
	year = {2009},
	month = {2009///},
	pages = {209 - 216},
	publisher = {ACM},
	organization = {ACM},
	address = {New York, NY, USA},
	abstract = {We describe an adaptation and application of a search-based structured prediction algorithm "Searn" to unsupervised learning problems. We show that it is possible to reduce unsupervised learning to supervised learning and demonstrate a high-quality un-supervised shift-reduce parsing model. We additionally show a close connection between unsupervised Searn and expectation maximization. Finally, we demonstrate the efficacy of a semi-supervised extension. The key idea that enables this is an application of the predict-self idea for unsupervised learning.},
	isbn = {978-1-60558-516-1},
	doi = {10.1145/1553374.1553401},
	url = {http://doi.acm.org/10.1145/1553374.1553401},
	author = {Daum{\'e}, Hal}
}
