Delay Complexity Dimension: Learning with Delayed and Censored Labels
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arXiv:2610.18935v1 Announce Type: new Abstract: We study PAC learning of binary classifiers when labels arrive after random delays and are censored beyond an observation window $W$. How many samples does this observation model require? We propose a distribution-dependent answer, the Delay Complexity Dimension (DCD). The DCD combines the VC dimension with the exact label-information rate of the observation channel.
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