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Title: Data pruning via Machine Teaching

Abstract: In this talk, I discuss the problem of data pruning: given a fully labeled dataset and a training procedure, select a subset such that training on that subset yields approximately the same test performance as training on the full dataset. I present our algorithm, inspired by recent work in machine teaching. Through experimental results and theoretical analysis, we find that machine teaching is an effective paradigm for data pruning.

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