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Useful to Whom? The Easy-versus-Coverage Boundary Moves with the Learner

Liu, Yangze et al.
arXiv (OAI Expanded) · Papers
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machine-learning
Is the same data equally useful to every learner? The question is whether the value of a training sample belongs to the sample or arises in its interaction with the learner. We test it with a selection experiment. Under a per-class budget, two kinds of samples compete for the slots: easy samples, wh
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