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# Efficient Co-Training of Linear Separators under Weak Dependence

*Proceedings of the 2017 Conference on Learning Theory*, PMLR 65:302-318, 2017.

#### Abstract

We develop the first polynomial-time algorithm for co-training of homogeneous linear separators under \em weak dependence, a relaxation of the condition of independence given the label. Our algorithm learns from purely unlabeled data, except for a single labeled example to break symmetry of the two classes, and works for any data distribution having an inverse-polynomial margin and with center of mass at the origin.