791 citations · 1.8k across the 42 of their papers we have counts for
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On-Average KL-Privacy and its equivalence to Generalization for Max-Entropy Mechanisms
Yu-Xiang Wang, Jing Lei, Stephen E. Fienberg
We define On-Average KL-Privacy and present its properties and connections to differential privacy, generalization and information-theoretic quantities including max-information an…
A Minimax Theory for Adaptive Data Analysis
Yu-Xiang Wang, Jing Lei, Stephen E. Fienberg
In adaptive data analysis, the user makes a sequence of queries on the data, where at each step the choice of query may depend on the results in previous steps. The releases are of…
Privacy for Free: Posterior Sampling and Stochastic Gradient Monte Carlo
Yu-Xiang Wang, Stephen E. Fienberg, Alex Smola
We consider the problem of Bayesian learning on sensitive datasets and present two simple but somewhat surprising results that connect Bayesian learning to "differential privacy:,…
Learning with Differential Privacy: Stability, Learnability and the Sufficiency and Necessity of ERM Principle
Yu-Xiang Wang, Jing Lei, Stephen E. Fienberg
While machine learning has proven to be a powerful data-driven solution to many real-life problems, its use in sensitive domains has been limited due to privacy concerns. A popular…
Differentially-Private Logistic Regression for Detecting Multiple-SNP Association in GWAS Databases
Fei Yu, Michal Rybar, Caroline Uhler +1
Following the publication of an attack on genome-wide association studies (GWAS) data proposed by Homer et al., considerable attention has been given to developing methods for rele…
On the Geometry of Discrete Exponential Families with Application to Exponential Random Graph Models
Stephen E. Fienberg, Alessandro Rinaldo, Yi Zhou
There has been an explosion of interest in statistical models for analyzing network data, and considerable interest in the class of exponential random graph (ERG) models, especiall…