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ORCIDMónica Ribero
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2020 – today
- 2024
[j4]Mónica Ribero, Haris Vikalo
:
Reducing communication in federated learning via efficient client sampling. Pattern Recognit. 148: 110122 (2024)- [c6]William Kong, Andrés Muñoz Medina, Mónica Ribero, Umar Syed:
DP-Auditorium: A Large-Scale Library for Auditing Differential Privacy. SP 2024: 110-126 - [i9]Weiwei Kong, Mónica Ribero:
Privacy of the last iterate in cyclically-sampled DP-SGD on nonconvex composite losses. CoRR abs/2407.05237 (2024) - 2023
- [j3]Mónica Ribero, Haris Vikalo, Gustavo de Veciana:
Federated Learning Under Intermittent Client Availability and Time-Varying Communication Constraints. IEEE J. Sel. Top. Signal Process. 17(1): 98-111 (2023) - [c5]Kareem Amin, Matthew Joseph, Mónica Ribero, Sergei Vassilvitskii:
Easy Differentially Private Linear Regression. ICLR 2023 - [i8]William Kong, Andrés Muñoz Medina, Mónica Ribero:
RényiTester: A Variational Approach to Testing Differential Privacy. CoRR abs/2307.05608 (2023) - [i7]William Kong, Andrés Muñoz Medina, Mónica Ribero:
DP-SGD for non-decomposable objective functions. CoRR abs/2310.03104 (2023) - 2022
- [j2]Mónica Ribero, Jette Henderson, Sinead Williamson, Haris Vikalo:
Federating recommendations using differentially private prototypes. Pattern Recognit. 129: 108746 (2022) - [c4]Jennifer Gillenwater, Matthew Joseph, Andres Muñoz Medina, Mónica Ribero Diaz:
A Joint Exponential Mechanism For Differentially Private Top-k. ICML 2022: 7570-7582 - [i6]Jennifer Gillenwater, Matthew Joseph, Andrés Muñoz Medina, Mónica Ribero:
A Joint Exponential Mechanism For Differentially Private Top-k. CoRR abs/2201.12333 (2022) - [i5]Mónica Ribero, Haris Vikalo, Gustavo de Veciana:
Federated Learning Under Intermittent Client Availability and Time-Varying Communication Constraints. CoRR abs/2205.06730 (2022) - [i4]Kareem Amin, Matthew Joseph, Mónica Ribero, Sergei Vassilvitskii:
Easy Differentially Private Linear Regression. CoRR abs/2208.07353 (2022) - 2021
- [c3]Peter Kairouz, Mónica Ribero Diaz, Keith Rush, Abhradeep Thakurta:
(Nearly) Dimension Independent Private ERM with AdaGrad Ratesvia Publicly Estimated Subspaces. COLT 2021: 2717-2746 - 2020
- [i3]Mónica Ribero, Jette Henderson, Sinead Williamson, Haris Vikalo:
Federating Recommendations Using Differentially Private Prototypes. CoRR abs/2003.00602 (2020) - [i2]Mónica Ribero, Haris Vikalo:
Communication-Efficient Federated Learning via Optimal Client Sampling. CoRR abs/2007.15197 (2020) - [i1]Peter Kairouz, Mónica Ribero, Keith Rush, Abhradeep Thakurta:
Dimension Independence in Unconstrained Private ERM via Adaptive Preconditioning. CoRR abs/2008.06570 (2020)
2010 – 2019
- 2019
- [j1]Yuyang Wang, Aldebaro Klautau, Mónica Ribero, Anthony C. K. Soong, Robert W. Heath Jr.:
MmWave Vehicular Beam Selection With Situational Awareness Using Machine Learning. IEEE Access 7: 87479-87493 (2019) - [c2]Mónica Ribero, Robert W. Heath Jr., Haris Vikalo, Dmitry Chizhik, Reinaldo A. Valenzuela:
Deep Learning Propagation Models over Irregular Terrain. ICASSP 2019: 4519-4523 - 2018
- [c1]Yuyang Wang, Aldebaro Klautau, Mónica Ribero, Murali Narasimha, Robert W. Heath Jr.:
MmWave Vehicular Beam Training with Situational Awareness by Machine Learning. GLOBECOM Workshops 2018: 1-6
Coauthor Index
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