On the Asymptotic Ergodic Capacity of Correlated Multiantenna Channels using Finite Dimensional Statistics. - Mario Díaz; Víctor Pérez-Abreu (CoRR, 2014)

On the symmetries and the capacity achieving input covariance matrices of multiantenna channels. - Mario Díaz (ISIT, 2016)

Information Extraction Under Privacy Constraints. - Shahab Asoodeh; Mario Díaz; Fady Alajaji; Tamás Linder (Inf., 2016)

Privacy-aware guessing efficiency. - Shahab Asoodeh; Mario Díaz; Fady Alajaji; Tamás Linder (ISIT, 2017)

On the Capacity of Block Multiantenna Channels. - Mario Díaz; Víctor Pérez-Abreu (IEEE Trans. Inf. Theory, 2017)

The Utility Cost of Robust Privacy Guarantees. - Hao Wang 063; Mario Díaz; Flávio P. Calmon; Lalitha Sankar (ISIT, 2018)

On the Noise-Information Separation of a Private Principal Component Analysis Scheme. - Mario Díaz; Shahab Asoodeh; Fady Alajaji; Tamás Linder; Serban Teodor Belinschi; James A. Mingo (CoRR, 2018)

On the Contractivity of Privacy Mechanisms. - Mario Díaz; Lalitha Sankar; Peter Kairouz (CoRR, 2018)

A Tunable Loss Function for Binary Classification. - Tyler Sypherd; Mario Díaz; Lalitha Sankar; Peter Kairouz (ISIT, 2019)

An Information-Theoretic View of Generalization via Wasserstein Distance. - Hao Wang 063; Mario Díaz; José Cândido Silveira Santos Filho; Flávio P. Calmon (ISIT, 2019)

A Tunable Loss Function for Classification. - Tyler Sypherd; Mario Díaz; Harshit Laddha; Lalitha Sankar; Peter Kairouz; Gautam Dasarathy (CoRR, 2019)

Theoretical Guarantees for Model Auditing with Finite Adversaries. - Mario Díaz; Peter Kairouz; Jiachun Liao; Lalitha Sankar (CoRR, 2019)

Estimation Efficiency Under Privacy Constraints. - Shahab Asoodeh; Mario Díaz; Fady Alajaji; Tamás Linder (IEEE Trans. Inf. Theory, 2019)

Privacy Amplification of Iterative Algorithms via Contraction Coefficients. - Shahab Asoodeh; Mario Díaz; Flávio P. Calmon (ISIT, 2020)

On the α-loss Landscape in the Logistic Model. - Tyler Sypherd; Mario Díaz; Lalitha Sankar; Gautam Dasarathy (ISIT, 2020)

On the alpha-loss Landscape in the Logistic Model. - Tyler Sypherd; Mario Díaz; Lalitha Sankar; Gautam Dasarathy (CoRR, 2020)

Privacy Analysis of Online Learning Algorithms via Contraction Coefficients. - Shahab Asoodeh; Mario Díaz; Flávio P. Calmon (CoRR, 2020)

On the Robustness of Information-Theoretic Privacy Measures and Mechanisms. - Mario Díaz; Hao Wang 063; Flávio P. Calmon; Lalitha Sankar (IEEE Trans. Inf. Theory, 2020)

The Impact of Split Classifiers on Group Fairness. - Hao Wang 063; Hsiang Hsu; Mario Díaz; Flávio P. Calmon (ISIT, 2021)

Neural Network-based Estimation of the MMSE. - Mario Díaz; Peter Kairouz; Jiachun Liao; Lalitha Sankar (ISIT, 2021)

Lower Bounds for the Minimum Mean-Square Error via Neural Network-based Estimation. - Mario Díaz; Peter Kairouz; Lalitha Sankar (CoRR, 2021)

To Split or not to Split: The Impact of Disparate Treatment in Classification. - Hao Wang 063; Hsiang Hsu; Mario Díaz; Flávio P. Calmon (IEEE Trans. Inf. Theory, 2021)

A Tunable Loss Function for Robust Classification: Calibration, Landscape, and Generalization. - Tyler Sypherd; Mario Díaz; John Kevin Cava; Gautam Dasarathy; Peter Kairouz; Lalitha Sankar (IEEE Trans. Inf. Theory, 2022)

On the Inevitability of the Rashomon Effect. - Lucas Monteiro Paes; Rodrigo Cruz; Flávio P. Calmon; Mario Díaz (ISIT, 2023)

Privacy Loss of Noisy Stochastic Gradient Descent Might Converge Even for Non-Convex Losses. - Shahab Asoodeh; Mario Díaz (CoRR, 2023)

On the Privacy Guarantees of Differentially Private Stochastic Gradient Descent. - Shahab Asoodeh; Mario Díaz (ISIT, 2024)

$ mathrm{E}_{ gamma}$-Mixing Time. - Behnoosh Zamanlooy; Shahab Asoodeh; Mario Díaz; Flávio P. Calmon (ISIT, 2024)

Locally Private Sampling with Public Data. - Behnoosh Zamanlooy; Mario Díaz; Shahab Asoodeh (AISTATS, 2025)

Lower Bounds on the MMSE of Adversarially Inferring Sensitive Features. - Monica Welfert; Nathan Stromberg; Mario Díaz; Lalitha Sankar (CoRR, 2025)