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+ | https://arxiv.org/pdf/1705.10470v1.pdf Iterative Machine Teaching | ||
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+ | In this paper, we consider the problem of machine | ||
+ | teaching, the inverse problem of machine | ||
+ | learning. Different from traditional machine | ||
+ | teaching which views the learners as batch algorithms, | ||
+ | we study a new paradigm where the | ||
+ | learner uses an iterative algorithm and a teacher | ||
+ | can feed examples sequentially and intelligently | ||
+ | based on the current performance of the learner. | ||
+ | We show that the teaching complexity in the iterative | ||
+ | case is very different from that in the batch | ||
+ | case. Instead of constructing a minimal training | ||
+ | set for learners, our iterative machine teaching | ||
+ | focuses on achieving fast convergence in the | ||
+ | learner model. Depending on the level of information | ||
+ | the teacher has from the learner model, | ||
+ | we design teaching algorithms which can provably | ||
+ | reduce the number of teaching examples and | ||
+ | achieve faster convergence than learning without | ||
+ | teachers. We also validate our theoretical findings | ||
+ | with extensive experiments on different data | ||
+ | distribution and real image datasets. |