11 citations · 17 across the 5 of their papers we have counts for
5 papers
Empirical Privacy Variance
Yuzheng Hu, Fan Wu, Ruicheng Xian +5
We propose the notion of empirical privacy variance and study it in the context of differentially private fine-tuning of language models. Specifically, we show that models calibrat…
Tukey Depth Mechanisms for Practical Private Mean Estimation
Gavin Brown, Lydia Zakynthinou
Mean estimation is a fundamental task in statistics and a focus within differentially private statistical estimation. While univariate methods based on the Gaussian mechanism are w…
Dimension-free Private Mean Estimation for Anisotropic Distributions
Yuval Dagan, Michael I. Jordan, Xuelin Yang +2
We present differentially private algorithms for high-dimensional mean estimation. Previous private estimators on distributions over suffer from a curse of dimension…
PAC-Bayes, MAC-Bayes and Conditional Mutual Information: Fast rate bounds that handle general VC classes
Peter Grünwald, Thomas Steinke, Lydia Zakynthinou
We give a novel, unified derivation of conditional PAC-Bayesian and mutual information (MI) generalization bounds. We derive conditional MI bounds as an instance, with special choi…
Covariance-Aware Private Mean Estimation Without Private Covariance Estimation
Gavin Brown, Marco Gaboardi, Adam Smith +2
We present two sample-efficient differentially private mean estimators for -dimensional (sub)Gaussian distributions with unknown covariance. Informally, given …