From the 1 of 5 linked papers with an AI index.
5 papers
Large-Sample Bayesian Approximations for Privatized Data
Jordan Awan, Xi Chen, Roberto Molinari
The paper introduces an approximate Bayesian method that imputes confidential data and then samples from the non‑private posterior to enable valid inference on large, differentiall…
Towards Open Science: Monitoring Crustal Deformations in North America
Lionel Voirol, Haotian Xu, Yuming Zhang +3
The study of the Earth's behavior has greatly benefited from the widespread deployment of Global Navigation Satellite Systems (GNSS), enabling large-scale monitoring of crustal def…
Equivalence Testing Under Privacy Constraints
Savita Pareek, Luca Insolia, Roberto Molinari +1
Protecting individual privacy is essential across research domains, from socio-economic surveys to big-tech user data. This need is particularly acute in healthcare, where analyses…
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…