13 citations · 35 across the 7 of their papers we have counts for
5 papers · 1 filter
Instance-Optimality for Private KL Distribution Estimation
Jiayuan Ye, Vitaly Feldman, Kunal Talwar
We study the fundamental problem of estimating an unknown discrete distribution over symbols, given i.i.d. samples from the distribution. We are interested in minimizin…
Unified Enhancement of Privacy Bounds for Mixture Mechanisms via -Differential Privacy
Chendi Wang, Buxin Su, Jiayuan Ye +2
Differentially private (DP) machine learning algorithms incur many sources of randomness, such as random initialization, random batch subsampling, and shuffling. However, such rand…
Initialization Matters: Privacy-Utility Analysis of Overparameterized Neural Networks
Jiayuan Ye, Zhenyu Zhu, Fanghui Liu +2
We analytically investigate how over-parameterization of models in randomized machine learning algorithms impacts the information leakage about their training data. Specifically, w…
Differentially Private Learning Needs Hidden State (Or Much Faster Convergence)
Jiayuan Ye, Reza Shokri
Prior work on differential privacy analysis of randomized SGD algorithms relies on composition theorems, where the implicit (unrealistic) assumption is that the internal state of t…
Differential Privacy Dynamics of Langevin Diffusion and Noisy Gradient Descent
Rishav Chourasia, Jiayuan Ye, Reza Shokri
What is the information leakage of an iterative randomized learning algorithm about its training data, when the internal state of the algorithm is \emph{private}? How much is the c…