Distribution-Independent Reliable Learning

@inproceedings{Kanade2014DistributionIndependentRL,
  title={Distribution-Independent Reliable Learning},
  author={Varun Kanade and Justin Thaler},
  booktitle={COLT},
  year={2014}
}
We study several questions in the reliable agnosticlearning framework of Kalai et al. (2009), which captures learning tasks in which one type of error is costlie r than other types. A positive reliable classifier is one that makes no false positive errors. The goal in the positive reliableagnostic framework is to output a hypothesis with the following properties: (i) its f alse positive error rate is at most ǫ, (ii) its false negative error rate is at most ǫ more than that of the best positive… CONTINUE READING
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