MmWave Vehicular Beam Training with Situational Awareness by Machine Learning. - Yuyang Wang 004; Aldebaro Klautau; Mónica Ribero; Murali Narasimha; Robert W. Heath Jr. (GLOBECOM Workshops, 2018)

Deep Learning Propagation Models over Irregular Terrain. - Mónica Ribero; Robert W. Heath Jr.; Haris Vikalo; Dmitry Chizhik; Reinaldo A. Valenzuela (ICASSP, 2019)

MmWave Vehicular Beam Selection With Situational Awareness Using Machine Learning. - Yuyang Wang 004; Aldebaro Klautau; Mónica Ribero; Anthony C. K. Soong; Robert W. Heath Jr. (IEEE Access, 2019)

Communication-Efficient Federated Learning via Optimal Client Sampling. - Mónica Ribero; Haris Vikalo (CoRR, 2020)

Dimension Independence in Unconstrained Private ERM via Adaptive Preconditioning. - Peter Kairouz; Mónica Ribero; Keith Rush; Abhradeep Thakurta (CoRR, 2020)

(Nearly) Dimension Independent Private ERM with AdaGrad Ratesvia Publicly Estimated Subspaces. - Peter Kairouz; Mónica Ribero Diaz; Keith Rush; Abhradeep Thakurta (COLT, 2021)

A Joint Exponential Mechanism For Differentially Private Top-k. - Jennifer Gillenwater; Matthew Joseph; Andres Muñoz Medina; Mónica Ribero Diaz (ICML, 2022)

Federating recommendations using differentially private prototypes. - Mónica Ribero; Jette Henderson; Sinead Williamson; Haris Vikalo (Pattern Recognit., 2022)

Easy Differentially Private Linear Regression. - Kareem Amin 002; Matthew Joseph; Mónica Ribero; Sergei Vassilvitskii (ICLR, 2023)

RényiTester: A Variational Approach to Testing Differential Privacy. - William Kong; Andrés Muñoz Medina; Mónica Ribero (CoRR, 2023)

DP-SGD for non-decomposable objective functions. - William Kong; Andrés Muñoz Medina; Mónica Ribero (CoRR, 2023)

Federated Learning Under Intermittent Client Availability and Time-Varying Communication Constraints. - Mónica Ribero; Haris Vikalo; Gustavo de Veciana (IEEE J. Sel. Top. Signal Process., 2023)

DP-Auditorium: A Large-Scale Library for Auditing Differential Privacy. - William Kong; Andrés Muñoz Medina; Mónica Ribero; Umar Syed (SP, 2024)

Privacy of the last iterate in cyclically-sampled DP-SGD on nonconvex composite losses. - Weiwei Kong; Mónica Ribero (CoRR, 2024)

Reducing communication in federated learning via efficient client sampling. - Mónica Ribero; Haris Vikalo (Pattern Recognit., 2024)

Privately Counting Partially Ordered Data. - Matthew Joseph; Mónica Ribero; Alexander Yu (ICLR, 2025)

Differentially private optimization for non-decomposable objective functions. - Weiwei Kong; Andrés Muñoz Medina; Mónica Ribero (ICLR, 2025)

Federated Learning at Scale: Addressing Client Intermittency and Resource Constraints. - Mónica Ribero; Haris Vikalo; Gustavo de Veciana (IEEE J. Sel. Top. Signal Process., 2025)