Iteratively Learning from the Best

arXiv: Learning, Volume abs/1810.11874, 2018.

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Abstract:

We study a simple generic framework to address the issue of bad training data; both bad labels in supervised problems, and bad samples in unsupervised ones. Our approach starts by fitting a model to the whole training dataset, but then iteratively improves it by alternating between (a) revisiting the training data to select samples with l...More

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