Convergence rates of sub-sampled Newton methods

NeurIPS 2015 Kamalika ChaudhuriSham M. KakadePraneeth NetrapalliSujay Sanghavi

An active learner is given a class of models, a large set of unlabeled examples, and the ability to interactively query labels of a subset of these examples; the goal of the learner is to learn a model in the class that fits the data well. Previous theoretical work has rigorously characterized label complexity of active learning, but most of this work has focused on the PAC or the agnostic PAC model... (read more)

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