Computer Science > Machine Learning
[Submitted on 13 Jun 2021 (v1), last revised 18 May 2023 (this version, v3)]
Title:Semi-verified PAC Learning from the Crowd
View PDFAbstract:We study the problem of crowdsourced PAC learning of threshold functions. This is a challenging problem and only recently have query-efficient algorithms been established under the assumption that a noticeable fraction of the workers are perfect. In this work, we investigate a more challenging case where the majority may behave adversarially and the rest behave as the Massart noise - a significant generalization of the perfectness assumption. We show that under the {semi-verified model} of Charikar et al. (2017), where we have (limited) access to a trusted oracle who always returns correct annotations, it is possible to PAC learn the underlying hypothesis class with a manageable amount of label queries. Moreover, we show that the labeling cost can be drastically mitigated via the more easily obtained comparison queries. Orthogonal to recent developments in semi-verified or list-decodable learning that crucially rely on data distributional assumptions, our PAC guarantee holds by exploring the wisdom of the crowd.
Submission history
From: Jie Shen [view email][v1] Sun, 13 Jun 2021 20:05:16 UTC (317 KB)
[v2] Fri, 4 Feb 2022 19:05:58 UTC (333 KB)
[v3] Thu, 18 May 2023 19:19:30 UTC (64 KB)
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