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Monica Ribero
Monica Ribero
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Title
Cited by
Cited by
Year
MmWave vehicular beam selection with situational awareness using machine learning
Y Wang, A Klautau, M Ribero, ACK Soong, RW Heath
IEEE Access 7, 87479-87493, 2019
922019
Reducing communication in federated learning via efficient client sampling
M Ribero, H Vikalo
Pattern Recognition 148, 110122, 2024
88*2024
(Nearly) Dimension Independent Private ERM with AdaGrad Rates\{via Publicly Estimated Subspaces
P Kairouz, MR Diaz, K Rush, A Thakurta
Conference on Learning Theory, 2717-2746, 2021
37*2021
Mmwave vehicular beam training with situational awareness by machine learning
Y Wang, A Klautau, M Ribero, M Narasimha, RW Heath
2018 IEEE Globecom Workshops (GC Wkshps), 1-6, 2018
342018
Federating recommendations using differentially private prototypes
M Ribero, J Henderson, S Williamson, H Vikalo
Pattern Recognition 129, 108746, 2022
302022
Deep learning propagation models over irregular terrain
M Ribero, RW Heath, H Vikalo, D Chizhik, RA Valenzuela
ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and …, 2019
252019
Federated learning under intermittent client availability and time-varying communication constraints
M Ribero, H Vikalo, G De Veciana
IEEE Journal of Selected Topics in Signal Processing 17 (1), 98-111, 2022
152022
A comparison of different crime prediction models for Bogotá
F Barreras, C Díaz, Á Riascos, M Ribero
Documentos CEDE 34, 2016
142016
Comparación de diferentes modelos para la predicción del crimen en bogotá
F Barreras, C Díaz, ÁJ Riascos, M Ribero
Economía y seguridad en el posconflicto, 209, 2018
102018
A Joint Exponential Mechanism For Differentially Private Top-
J Gillenwater, M Joseph, A Munoz, MR Diaz
International Conference on Machine Learning, 7570-7582, 2022
82022
Easy differentially private linear regression
K Amin, M Joseph, M Ribero, S Vassilvitskii
arXiv preprint arXiv:2208.07353, 2022
7*2022
Una comparación de diferentes modelos para la predicción del crimen en bogotá (a comparison of different crime prediction models for bogotá)
F Barreras, C Diaz, A Riascos, M Ribero
Documento CEDE, 2016
22016
R\'enyiTester: A Variational Approach to Testing Differential Privacy
W Kong, AM Medina, M Ribero
arXiv preprint arXiv:2307.05608, 2023
12023
A joint exponential mechanism for differentially private top-k set
M Joseph, J Gillenwater, M Ribero
NeurIPS 2021 Workshop Privacy in Machine Learning, 2021
12021
Improving transparency of the Colombian Peace Treaty with NLP
F Barreras, M Ribero, F Suárez
Quantil Documentos de Trabajo, 2017
12017
DP-SGD for non-decomposable objective functions
W Kong, AM Medina, M Ribero
arXiv preprint arXiv:2310.03104, 2023
2023
Una comparación de diferentes modelos para la predicción del crimen en Bogotá
F Barreras, C Díaz, ÁJR Villegas, M Ribero
2016
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