4 papers · 1 filter
Large-Sample Bayesian Approximations for Privatized Data
Jordan Awan, Xi Chen, Roberto Molinari
The increased use of differential privacy (DP) has allowed the sharing of large amounts of data while reducing the risk of disclosure of sensitive information at the individual lev…
Differentially Private Conformal Prediction via Quantile Binary Search
Ogonnaya M. Romanus, Roberto Molinari
Most Differentially Private (DP) approaches focus on limiting privacy leakage from learners based on the data that they are trained on, there are fewer approaches that consider lea…
Fiducial Matching: Differentially Private Inference for Categorical Data
Ogonnaya Michael Romanus, Younes Boulaguiem, Roberto Molinari
The task of statistical inference, which includes the building of confidence intervals and tests for parameters and effects of interest to a researcher, is still an open area of in…
Inference for Large Scale Regression Models with Dependent Errors
Lionel Voirol, Haotian Xu, Yuming Zhang +3
The exponential growth in data sizes and storage costs has brought considerable challenges to the data science community, requiring solutions to run learning methods on such data.…