Efficient Agnostic Learning of Univariate Densities Under Local Differential Privacy
arXiv:2610.21033v1 Announce Type: new Abstract: We propose a general framework for non-interactive locally differentially private (LDP) learning of structured univariate distributions that can be approximated by piece-wise polynomials. This gives the first sample-efficient LDP learners for several…
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- 2026-10-16 04:00 · arXiv stat.ML
Efficient Agnostic Learning of Univariate Densities Under Local Differential Privacy