12 citations · 13 across the 5 of their papers we have counts for
4 papers · 1 filter
Differentially Private Inference for Longitudinal Linear Regression
Getoar Sopa, Marco Avella Medina, Cynthia Rush
Differential Privacy (DP) provides a rigorous framework for releasing statistics while protecting individual information present in a dataset. Although substantial progress has bee…
Statistical Guarantees for Data-driven Posterior Tempering
Ruchira Ray, Marco Avella Medina, Cynthia Rush
Posterior tempering reduces the influence of the likelihood in the calculation of the posterior by raising the likelihood to a fractional power . The resulting power posterior -…
A theoretical framework for M-posteriors: frequentist guarantees and robustness properties
Juraj Marusic, Marco Avella Medina, Cynthia Rush
We provide a theoretical framework for a wide class of generalized posteriors that can be viewed as the natural Bayesian posterior counterpart of the class of M-estimators in the f…
Asymptotics for power posterior mean estimation
Ruchira Ray, Marco Avella Medina, Cynthia Rush
Power posteriors "robustify" standard Bayesian inference by raising the likelihood to a constant fractional power, effectively downweighting its influence in the calculation of the…