Efficient active learning of sparse halfspaces
conference on learning theory, 2018.
We study the problem of efficient PAC active learning of d-dimensional linear classifiers, where the goal is to learn a classifier with low error, using as few label queries as possible. Given the extra assumption that there is a t-sparse linear separator that peforms well on the data (t
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