arXiv Machine Learning June 15, 2026 By Bal\'azs Csan\'ad Cs\'aji, L\'aszl\'o Gy\"orfi, Ambrus Tam\'as, Harro Walk

On Rate-Optimal Partitioning Classification from Observable and from Privatised Data

Read the original on arXiv Machine Learning →

arXiv:2312. 14889v4 Announce Type: replace-cross Abstract: In this paper we revisit the classical method of partitioning classification and prove novel convergence rates under relaxed conditions, both for observable (non-privatised) and for privatised data.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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