66 citations · 141 across the 8 of their papers we have counts for
8 papers
Estimating quantile treatments without strict overlap
Marco Avella-Medina, Richard Davis, Gennady Samorodnitsky
We consider the problem of estimating quantile treatment effects without assuming strict overlap, i.e., we do not assume that the propensity score is bounded away from zero. More s…
Kernel PCA for multivariate extremes
Marco Avella-Medina, Richard A. Davis, Gennady Samorodnitsky
We propose kernel PCA as a method for analyzing the dependence structure of multivariate extremes and demonstrate that it can be a powerful tool for clustering and dimension reduct…
Differentially private inference via noisy optimization
Marco Avella-Medina, Casey Bradshaw, Po-Ling Loh
We propose a general optimization-based framework for computing differentially private M-estimators and a new method for constructing differentially private confidence regions. Fir…
Propose, Test, Release: Differentially private estimation with high probability
Victor-Emmanuel Brunel, Marco Avella-Medina
We derive concentration inequalities for differentially private median and mean estimators building on the "Propose, Test, Release" (PTR) mechanism introduced by Dwork and Lei (200…
Privacy-preserving parametric inference: a case for robust statistics
Marco Avella-Medina
Differential privacy is a cryptographically-motivated approach to privacy that has become a very active field of research over the last decade in theoretical computer science and m…
Differentially private sub-Gaussian location estimators
Marco Avella-Medina, Victor-Emmanuel Brunel
We tackle the problem of estimating a location parameter with differential privacy guarantees and sub-Gaussian deviations. Recent work in statistics has focused on the study of est…
Centrality measures for graphons: Accounting for uncertainty in networks
Marco Avella-Medina, Francesca Parise, Michael T. Schaub +1
As relational datasets modeled as graphs keep increasing in size and their data-acquisition is permeated by uncertainty, graph-based analysis techniques can become computationally…
A Paradigmatic Regression Algorithm for Gene Selection Problems
Stéphane Guerrier, Nabil Mili, Roberto Molinari +3
Motivation: Gene selection has become a common task in most gene expression studies. The objective of such research is often to identify the smallest possible set of genes that can…