A private m-out-of-n bootstrap under Gaussian Differential Privacy gives asymptotically valid confidence intervals with less added noise and lower computation than the existing n-out-of-n private bootstrap.
Title resolution pending
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
stat.ML 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
Gaussian Differential Private Bootstrap by Subsampling
A private m-out-of-n bootstrap under Gaussian Differential Privacy gives asymptotically valid confidence intervals with less added noise and lower computation than the existing n-out-of-n private bootstrap.