| Reducing communication in federated learning via efficient client sampling M Ribero, H Vikalo Pattern Recognition 148, 110122, 2024 | 172* | 2024 |
| 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 | 129 | 2019 |
| 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 | 66 | 2022 |
| (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 | 52* | 2021 |
| Federating recommendations using differentially private prototypes M Ribero, J Henderson, S Williamson, H Vikalo Pattern Recognition 129, 108746, 2022 | 46 | 2022 |
| 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 | 39 | 2018 |
| 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 | 31 | 2019 |
| Easy differentially private linear regression K Amin, M Joseph, M Ribero, S Vassilvitskii arXiv preprint arXiv:2208.07353, 2022 | 23 | 2022 |
| A Joint Exponential Mechanism For Differentially Private Top- J Gillenwater, M Joseph, A Munoz, MR Diaz International Conference on Machine Learning, 7570-7582, 2022 | 16 | 2022 |
| A comparison of different crime prediction models for Bogotá F Barreras, C Díaz, Á Riascos, M Ribero Documentos CEDE 34, 2016 | 15 | 2016 |
| 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 | 12 | 2018 |
| DP-Auditorium: A Large-Scale Library for Auditing Differential Privacy W Kong, AM Medina, M Ribero, U Syed 2024 IEEE Symposium on Security and Privacy (SP), 110-126, 2024 | 10 | 2024 |
| Communication-efficient federated learning via optimal client sampling. arXiv 2020 M Ribero, H Vikalo arXiv preprint arXiv:2007.15197, 2020 | 5 | 2020 |
| Privacy of the last iterate in cyclically-sampled DP-SGD on nonconvex composite losses W Kong, M Ribero arXiv preprint arXiv:2407.05237, 2024 | 3 | 2024 |
| 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 | 3 | 2016 |
| Privately Counting Partially Ordered Data M Joseph, M Ribero, A Yu arXiv preprint arXiv:2410.06881, 2024 | 2 | 2024 |
| DP-SGD for non-decomposable objective functions W Kong, AM Medina, M Ribero arXiv preprint arXiv:2310.03104, 2023 | 2 | 2023 |
| Federated Learning at Scale: Addressing Client Intermittency and Resource Constraints M Ribero, H Vikalo, G de Veciana IEEE Journal of Selected Topics in Signal Processing, 2024 | 1 | 2024 |
| RényiTester: A Variational Approach to Testing Differential Privacy W Kong, M Ribero NeurIPS 2023 Workshop on Regulatable ML, 2023 | 1 | 2023 |
| A joint exponential mechanism for differentially private top-k set M Joseph, J Gillenwater, M Ribero NeurIPS 2021 Workshop Privacy in Machine Learning, 2021 | 1 | 2021 |
