3 citations · 4 across the 10 of their papers we have counts for
10 papers
The Influence Function of Transport-based Quantiles
Alberto González-Sanz, Shunan Sheng, Bohan Wu +1
Transport-based quantiles extend univariate quantiles to multivariate distributions via optimal transport. We study the influence function of the transport quantile map $\mathbf{Q}…
Contaminated Multi-task Learning with Heterogeneity: Fundamental Limits and Optimal Algorithms
Ye Tian, Mengchu Li, Marco Avella Medina
Integrating information across related tasks can improve estimation and prediction in transfer, multi-task, and federated learning, but contamination and heterogeneity make robust…
The Threshold Breakdown Point
Tianjun Ke, Marco Avella Medina
We introduce a novel approach to finite sample robustness that avoids the pessimism of traditional breakdown analyses. We define the threshold breakdown point, the smallest contami…
Breakdown properties of optimal transport maps: general transportation costs
Alberto Gonzalez-Sanz, Marco Avella Medina
Two recent works, Avella-Medina and González-Sanz (2026) and Passeggeri and Paindaveine (2026), studied the robustness of the optimal transport map through its breakdown point, i.e…
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…
Spectral learning of multivariate extremes
Marco Avella Medina, Richard A. Davis, Gennady Samorodnitsky
We propose a spectral clustering algorithm for analyzing the dependence structure of multivariate extremes. More specifically, we focus on the asymptotic dependence of multivariate…
On the Robustness to Misspecification of -Posteriors and Their Variational Approximations
Marco Avella Medina, José Luis Montiel Olea, Cynthia Rush +1
-posteriors and their variational approximations distort standard posterior inference by downweighting the likelihood and introducing variational approximation errors. We show t…