Noise-tolerant, Reliable Active Classification with Comparison Queries
COLT, pp. 1957-2006, 2020.
With the explosion of massive, widely available unlabeled data in the past years, finding label and time efficient, robust learning algorithms has become ever more important in theory and in practice. We study the paradigm of active learning, in which algorithms with access to large pools of data may adaptively choose what samples to la...More
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