1 citations · 1 across the 3 of their papers we have counts for
8 papers
Metric geometry of the privacy-utility tradeoff
March Boedihardjo, Thomas Strohmer, Roman Vershynin
Synthetic data are an attractive concept to enable privacy in data sharing. A fundamental question is how similar the privacy-preserving synthetic data are compared to the true dat…
Privacy of synthetic data: a statistical framework
March Boedihardjo, Thomas Strohmer, Roman Vershynin
Privacy-preserving data analysis is emerging as a challenging problem with far-reaching impact. In particular, synthetic data are a promising concept toward solving the aporetic co…
The spectral norm of Gaussian matrices with correlated entries
Afonso S. Bandeira, March T. Boedihardjo
We give a non-asymptotic bound on the spectral norm of a matrix with centered jointly Gaussian entries in terms of the covariance matrix of the entries. In some cas…
A Performance Guarantee for Spectral Clustering
March Boedihardjo, Shaofeng Deng, Thomas Strohmer
The two-step spectral clustering method, which consists of the Laplacian eigenmap and a rounding step, is a widely used method for graph partitioning. It can be seen as a natural r…
Estimation of expected value of function of i.i.d. Bernoulli random variables
March T. Boedihardjo
We estimate the expected value of certain function . For example, with computer assistance, we show that if is the Laplacian of the Cayley graph of…
DP-LSSGD: A Stochastic Optimization Method to Lift the Utility in Privacy-Preserving ERM
Bao Wang, Quanquan Gu, March Boedihardjo +2
Machine learning (ML) models trained by differentially private stochastic gradient descent (DP-SGD) have much lower utility than the non-private ones. To mitigate this degradation,…