Centrality measures for graphons. - Marco Avella-Medina; Francesca Parise; Michael T. Schaub; Santiago Segarra (CoRR, 2017)

Privacy-preserving parametric inference: a case for robust statistics. - Marco Avella-Medina (CoRR, 2019)

Propose, Test, Release: Differentially private estimation with high probability. - Victor-Emmanuel Brunel; Marco Avella-Medina (CoRR, 2020)

Centrality Measures for Graphons: Accounting for Uncertainty in Networks. - Marco Avella-Medina; Francesca Parise; Michael T. Schaub; Santiago Segarra (IEEE Trans. Netw. Sci. Eng., 2020)

Differentially private inference via noisy optimization. - Marco Avella-Medina; Casey Bradshaw; Po-Ling Loh (CoRR, 2021)

Kernel PCA for multivariate extremes. - Marco Avella-Medina; Richard A. Davis; Gennady Samorodnitsky (CoRR, 2022)

On the Robustness to Misspecification of α-posteriors and Their Variational Approximations. - Marco Avella-Medina; José Luis Montiel Olea; Cynthia Rush; Amilcar Velez (J. Mach. Learn. Res., 2022)

Asymptotics for power posterior mean estimation. - Ruchira Ray; Marco Avella-Medina; Cynthia Rush (Allerton, 2023)

Thompson Sampling Itself is Differentially Private. - Tingting Ou; Rachel Cummings; Marco Avella Medina (AISTATS, 2024)

Differential Privacy Under Class Imbalance: Methods and Empirical Insights. - Lucas Rosenblatt; Yuliia Lut; Eitan Turok; Marco Avella-Medina; Rachel Cummings (CoRR, 2024)

Spectral learning of multivariate extremes. - Marco Avella Medina; Richard A. Davis; Gennady Samorodnitsky (J. Mach. Learn. Res., 2024)

Scalable Differentially Private Bayesian Optimization. - Getoar Sopa; Juraj Marusic; Marco Avella-Medina; John P. Cunningham (CoRR, 2025)