| 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 | 183 | 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 | 92 | 2018 |
| Differentially private assouad, fano, and le cam J Acharya, Z Sun, H Zhang Algorithmic Learning Theory, 48-78, 2021 | 69 | 2021 |
| Improved rates for differentially private stochastic convex optimization with heavy-tailed data G Kamath, X Liu, H Zhang International Conference on Machine Learning, 10633-10660, 2022 | 53 | 2022 |
| Challenges towards the next frontier in privacy R Cummings, D Desfontaines, D Evans, R Geambasu, M Jagielski, ... arXiv preprint arXiv:2304.06929 1, 2023 | 36 | 2023 |
| Estimating smooth glm in non-interactive local differential privacy model with public unlabeled data D Wang, H Zhang, M Gaboardi, J Xu Algorithmic Learning Theory, 1207-1213, 2021 | 35 | 2021 |
| Locally private hypothesis selection S Gopi, G Kamath, J Kulkarni, A Nikolov, ZS Wu, H Zhang Conference on Learning Theory, 1785-1816, 2020 | 35 | 2020 |
| Privately learning Markov random fields H Zhang, G Kamath, J Kulkarni, S Wu International conference on machine learning, 11129-11140, 2020 | 29 | 2020 |
| Wide network learning with differential privacy H Zhang, I Mironov, M Hejazinia arXiv preprint arXiv:2103.01294, 2021 | 28 | 2021 |
| Analytical composition of differential privacy via the edgeworth accountant H Wang, S Gao, H Zhang, M Shen, WJ Su arXiv preprint arXiv:2206.04236, 2022 | 27 | 2022 |
| Inspectre: Privately estimating the unseen J Acharya, G Kamath, Z Sun, H Zhang International Conference on Machine Learning, 30-39, 2018 | 26 | 2018 |
| Communication efficient, sample optimal, linear time locally private discrete distribution estimation J Acharya, Z Sun, H Zhang arXiv preprint arXiv:1802.04705, 2018 | 18 | 2018 |
| Federated linear contextual bandits with user-level differential privacy R Huang, H Zhang, L Melis, M Shen, M Hejazinia, J Yang International Conference on Machine Learning, 14060-14095, 2023 | 15 | 2023 |
| Design and implementation of device-to-device software-defined networks M Zhou, H Zhang, S Zhang, L Song, Y Li, Z Han 2016 IEEE International Conference on Communications (ICC), 1-6, 2016 | 15 | 2016 |
| Advancing differential privacy: Where we are now and future directions for real-world deployment R Cummings, D Desfontaines, D Evans, R Geambasu, Y Huang, ... arXiv preprint arXiv:2304.06929, 2023 | 14 | 2023 |
| Contraction of locally differentially private mechanisms S Asoodeh, H Zhang IEEE Journal on Selected Areas in Information Theory, 2024 | 13 | 2024 |
| Robust testing and estimation under manipulation attacks J Acharya, Z Sun, H Zhang International Conference on Machine Learning, 43-53, 2021 | 12 | 2021 |
| Robust estimation for random graphs J Acharya, A Jain, G Kamath, AT Suresh, H Zhang Conference on Learning Theory, 130-166, 2022 | 10 | 2022 |
| Generalized linear models in non-interactive local differential privacy with public data D Wang, L Hu, H Zhang, M Gaboardi, J Xu Journal of Machine Learning Research 24 (132), 1-57, 2023 | 9 | 2023 |
| Reliability and longer range for low power transmitters with on demand network MIMO MA Weitnauer, Q Lin, H Zhang, H Tian, S Nowlan, G Nyengele, ... 2016 IEEE International Conference on RFID (RFID), 1-10, 2016 | 8 | 2016 |
Jayadev AcharyaAssistant Professor, Cornell UniversityVerified email at cornell.edu
