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When Clean Data Hurts: Learning with Monotone Corruptions Beyond Binary Classification

This story is from 2026-08-24. It is preserved in the archive; the latest stories are on the live feed.

arXiv:2608.20480v1 Announce Type: new Abstract: Optimal learners are tailored to exploit the i.i.d.\ data assumption underlying the classic PAC model. What if an i.i.d.\ training sample were corrupted with correctly labeled examples drawn from an otherwise unrelated, even adversarial source? This m…

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  1. 2026-08-24 04:00 · arXiv cs.LG
    When Clean Data Hurts: Learning with Monotone Corruptions Beyond Binary Classification

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