Learning with monotone corruptions beyond binary classification: clean data can hurt performance
Read the original at arxiv.org→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...
Original headline: "When Clean Data Hurts: Learning with Monotone Corruptions Beyond Binary Classification"
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- Aug 24, 04:00 UTC arXiv cs.LG lead source When Clean Data Hurts: Learning with Monotone Corruptions Beyond Binary Classification