| 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 | 3119 | 2021 |
| Can you really backdoor federated learning? Z Sun, P Kairouz, AT Suresh, HB McMahan arXiv preprint arXiv:1911.07963, 2019 | 310 | 2019 |
| Hadamard response: Estimating distributions privately, efficiently, and with little communication J Acharya, Z Sun, H Zhang The 22nd International Conference on Artificial Intelligence and Statistics …, 2019 | 134* | 2019 |
| Differentially private testing of identity and closeness of discrete distributions J Acharya, Z Sun, H Zhang Advances in Neural Information Processing Systems 31, 2018 | 65 | 2018 |
| Communication complexity in locally private distribution estimation and heavy hitters J Acharya, Z Sun International Conference on Machine Learning, 51-60, 2019 | 43 | 2019 |
| Learning with user-level privacy D Levy, Z Sun, K Amin, S Kale, A Kulesza, M Mohri, AT Suresh Advances in Neural Information Processing Systems 34, 12466-12479, 2021 | 35 | 2021 |
| Context aware local differential privacy J Acharya, K Bonawitz, P Kairouz, D Ramage, Z Sun International Conference on Machine Learning, 52-62, 2020 | 33 | 2020 |
| Social distancing has merely stabilized COVID-19 in the US AB Wagner, EL Hill, SE Ryan, Z Sun, G Deng, S Bhadane, VH Martinez, ... Stat, 2020 | 31* | 2020 |
| Interactive inference under information constraints J Acharya, CL Canonne, Y Liu, Z Sun, H Tyagi IEEE Transactions on Information Theory 68 (1), 502-516, 2021 | 28 | 2021 |
| Differentially private assouad, fano, and le cam J Acharya, Z Sun, H Zhang Algorithmic Learning Theory, 48-78, 2021 | 28 | 2021 |
| Inspectre: Privately estimating the unseen J Acharya, G Kamath, Z Sun, H Zhang International Conference on Machine Learning, 30-39, 2018 | 24 | 2018 |
| Domain compression and its application to randomness-optimal distributed goodness-of-fit J Acharya, CL Canonne, Y Han, Z Sun, H Tyagi Conference on Learning Theory, 3-40, 2020 | 20 | 2020 |
| Improved bounds for minimax risk of estimating missing mass J Acharya, Y Bao, Y Kang, Z Sun 2018 IEEE International Symposium on Information Theory (ISIT), 326-330, 2018 | 19 | 2018 |
| Inference under information constraints III: Local privacy constraints J Acharya, CL Canonne, C Freitag, Z Sun, H Tyagi IEEE Journal on Selected Areas in Information Theory 2 (1), 253-267, 2021 | 17 | 2021 |
| Estimating sparse discrete distributions under privacy and communication constraints J Acharya, P Kairouz, Y Liu, Z Sun Algorithmic Learning Theory, 79-98, 2021 | 15* | 2021 |
| Unified lower bounds for interactive high-dimensional estimation under information constraints J Acharya, CL Canonne, Z Sun, H Tyagi arXiv preprint arXiv:2010.06562, 2020 | 15 | 2020 |
| Estimating entropy of distributions in constant space J Acharya, S Bhadane, P Indyk, Z Sun Advances in Neural Information Processing Systems 32, 2019 | 7 | 2019 |
| Distributed estimation with multiple samples per user: Sharp rates and phase transition J Acharya, C Canonne, Y Liu, Z Sun, H Tyagi Advances in Neural Information Processing Systems 34, 18920-18931, 2021 | 5 | 2021 |
| Robust testing and estimation under manipulation attacks J Acharya, Z Sun, H Zhang International Conference on Machine Learning, 43-53, 2021 | 5 | 2021 |
| Optimal Byzantine attack and Byzantine identification in distributed sensor networks Z Sun, C Zhang, P Fan 2016 IEEE Globecom Workshops (GC Wkshps), 1-6, 2016 | 5 | 2016 |
Jayadev AcharyaAssistant Professor, Cornell UniversityVerified email at cornell.edu
