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Teaching a black-box learner

Sanjoy Dasgupta · Daniel Hsu · Stefanos Poulis · Jerry Zhu

Pacific Ballroom #183

Keywords: [ Unsupervised and Semi-supervised Learning ] [ Semi-supervised learning ] [ Computational Learning Theory ] [ Active Learning ]


One widely-studied model of {\it teaching} calls for a teacher to provide the minimal set of labeled examples that uniquely specifies a target concept. The assumption is that the teacher knows the learner's hypothesis class, which is often not true of real-life teaching scenarios. We consider the problem of teaching a learner whose representation and hypothesis class are unknown---that is, the learner is a black box. We show that a teacher who does not interact with the learner can do no better than providing random examples. We then prove, however, that with interaction, a teacher can efficiently find a set of teaching examples that is a provably good approximation to the optimal set. As an illustration, we show how this scheme can be used to {\it shrink} training sets for any family of classifiers: that is, to find an approximately-minimal subset of training instances that yields the same classifier as the entire set.

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