@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} }