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The price of multi-group transductive learning

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Computer Science > Machine Learning

arXiv:2606.04423 (cs)
[Submitted on 3 Jun 2026]

Title:The price of multi-group transductive learning

View a PDF of the paper titled The price of multi-group transductive learning, by Noah Bergam and Samuel Deng and Daniel Hsu
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Abstract:We show every multi-group learner in the transductive setting may incur a multiplicative penalty in its error rate on some group relative to the error rate achievable in the single-group setting, and the penalty can increasing linearly with the number of groups, up to roughly the square-root of the sample size. This stands in stark contrast to optimal multi-group learners in an analogous (group-realizable) statistical setting, where the penalty is always at most logarithmic in the sample size and independent of the number of groups.
Subjects: Machine Learning (cs.LG); Machine Learning (stat.ML)
Cite as: arXiv:2606.04423 [cs.LG]
  (or arXiv:2606.04423v1 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.2606.04423
arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Daniel Hsu [view email]
[v1] Wed, 3 Jun 2026 04:07:24 UTC (19 KB)
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