| Advances and open problems in federated learning P Kairouz, HB McMahan, B Avent, A Bellet, M Bennis, AN Bhagoji, ... Foundations and trends® in machine learning 14 (1–2), 1-210, 2021 | 6135 | 2021 |
| Mariana Raykova, Dawn Song, Weikang Song, Sebastian U P Kairouz, HB McMahan, B Avent, A Bellet, M Bennis, AN Bhagoji, ... Stich, Ziteng Sun, Ananda Theertha Suresh, Florian Tramèr, Praneeth …, 2021 | 192 | 2021 |
| Deep learning with label differential privacy B Ghazi, N Golowich, R Kumar, P Manurangsi, C Zhang Advances in neural information processing systems 34, 27131-27145, 2021 | 155 | 2021 |
| Large-scale differentially private BERT R Anil, B Ghazi, V Gupta, R Kumar, P Manurangsi arXiv preprint arXiv:2108.01624, 2021 | 124 | 2021 |
| Sample-optimal average-case sparse fourier transform in two dimensions B Ghazi, H Hassanieh, P Indyk, D Katabi, E Price, L Shi 2013 51st Annual Allerton Conference on Communication, Control, and …, 2013 | 114 | 2013 |
| Scalable and differentially private distributed aggregation in the shuffled model B Ghazi, R Pagh, A Velingker arXiv preprint arXiv:1906.08320, 2019 | 106 | 2019 |
| On the power of multiple anonymous messages: Frequency estimation and selection in the shuffle model of differential privacy B Ghazi, N Golowich, R Kumar, R Pagh, A Velingker Annual International Conference on the Theory and Applications of …, 2021 | 97 | 2021 |
| Advances and open problems in federated learning. arXiv 2019 P Kairouz, HB McMahan, B Avent, A Bellet, M Bennis, AN Bhagoji, ... arXiv preprint arXiv:1912.04977, 1912 | 66 | 1912 |
| Private aggregation from fewer anonymous messages B Ghazi, P Manurangsi, R Pagh, A Velingker Advances in Cryptology–EUROCRYPT 2020: 39th Annual International Conference …, 2020 | 59 | 2020 |
| Differentially private clustering: Tight approximation ratios B Ghazi, R Kumar, P Manurangsi Advances in Neural Information Processing Systems 33, 4040-4054, 2020 | 54 | 2020 |
| Pure differentially private summation from anonymous messages B Ghazi, N Golowich, R Kumar, P Manurangsi, R Pagh, A Velingker arXiv preprint arXiv:2002.01919, 2020 | 50 | 2020 |
| Private counting from anonymous messages: Near-optimal accuracy with vanishing communication overhead B Ghazi, R Kumar, P Manurangsi, R Pagh International Conference on Machine Learning, 3505-3514, 2020 | 45 | 2020 |
| User-level differentially private learning via correlated sampling B Ghazi, R Kumar, P Manurangsi Advances in Neural Information Processing Systems 34, 20172-20184, 2021 | 44 | 2021 |
| Decidability of non-interactive simulation of joint distributions B Ghazi, P Kamath, M Sudan 2016 IEEE 57th Annual Symposium on Foundations of Computer Science (FOCS …, 2016 | 38 | 2016 |
| Differentially private aggregation in the shuffle model: Almost central accuracy in almost a single message B Ghazi, R Kumar, P Manurangsi, R Pagh, A Sinha International Conference on Machine Learning, 3692-3701, 2021 | 37 | 2021 |
| Locally private k-means in one round A Chang, B Ghazi, R Kumar, P Manurangsi International conference on machine learning, 1441-1451, 2021 | 34 | 2021 |
| On distributed differential privacy and counting distinct elements L Chen, B Ghazi, R Kumar, P Manurangsi arXiv preprint arXiv:2009.09604, 2020 | 31 | 2020 |
| Connect the dots: Tighter discrete approximations of privacy loss distributions V Doroshenko, B Ghazi, P Kamath, R Kumar, P Manurangsi arXiv preprint arXiv:2207.04380, 2022 | 29 | 2022 |
| Sample-efficient proper PAC learning with approximate differential privacy B Ghazi, N Golowich, R Kumar, P Manurangsi Proceedings of the 53rd Annual ACM SIGACT Symposium on Theory of Computing …, 2021 | 28 | 2021 |
| Recursive sketches for modular deep learning B Ghazi, R Panigrahy, J Wang International Conference on Machine Learning, 2211-2220, 2019 | 27 | 2019 |
