| Stochastic (approximate) proximal point methods: Convergence, optimality, and adaptivity H Asi, JC Duchi SIAM Journal on Optimization 29 (3), 2257-2290, 2019 | 140 | 2019 |
| The importance of better models in stochastic optimization H Asi, JC Duchi Proceedings of the National Academy of Sciences 116 (46), 22924-22930, 2019 | 96 | 2019 |
| Private Stochastic Convex Optimization: Optimal Rates in Geometry H Asi, V Feldman, T Koren, K Talwar ICML 2021 (Oral), 2021 | 85 | 2021 |
| Instance-optimality in differential privacy via approximate inverse sensitivity mechanisms H Asi, JC Duchi Advances in neural information processing systems 33, 14106-14117, 2020 | 59 | 2020 |
| Private adaptive gradient methods for convex optimization H Asi, J Duchi, A Fallah, O Javidbakht, K Talwar International Conference on Machine Learning, 383-392, 2021 | 46 | 2021 |
| Nearly optimal constructions of PIR and batch codes H Asi, E Yaakobi IEEE Transactions on Information Theory 65 (2), 947-964, 2018 | 39 | 2018 |
| Near instance-optimality in differential privacy H Asi, JC Duchi arXiv preprint arXiv:2005.10630, 2020 | 37 | 2020 |
| Optimal algorithms for mean estimation under local differential privacy H Asi, V Feldman, K Talwar International Conference on Machine Learning, 1046-1056, 2022 | 35 | 2022 |
| Stochastic bias-reduced gradient methods H Asi, Y Carmon, A Jambulapati, Y Jin, A Sidford Advances in Neural Information Processing Systems 34, 10810-10822, 2021 | 31 | 2021 |
| Adapting to function difficulty and growth conditions in private optimization H Asi, D Levy, JC Duchi Advances in Neural Information Processing Systems 34, 19069-19081, 2021 | 23 | 2021 |
| Element level differential privacy: The right granularity of privacy H Asi, J Duchi, O Javidbakht arXiv preprint arXiv:1912.04042, 2019 | 19 | 2019 |
| From robustness to privacy and back H Asi, J Ullman, L Zakynthinou International Conference on Machine Learning, 1121-1146, 2023 | 18 | 2023 |
| Minibatch stochastic approximate proximal point methods H Asi, K Chadha, G Cheng, JC Duchi Advances in neural information processing systems 33, 21958-21968, 2020 | 16 | 2020 |
| Private online prediction from experts: Separations and faster rates H Asi, V Feldman, T Koren, K Talwar The Thirty Sixth Annual Conference on Learning Theory, 674-699, 2023 | 14 | 2023 |
| Finding planted cliques in sublinear time J Mardia, H Asi, KA Chandrasekher arXiv preprint arXiv:2004.12002, 2020 | 9 | 2020 |
| Fast optimal locally private mean estimation via random projections H Asi, V Feldman, J Nelson, H Nguyen, K Talwar Advances in Neural Information Processing Systems 36, 2024 | 8 | 2024 |
| Near-optimal algorithms for private online optimization in the realizable regime H Asi, V Feldman, T Koren, K Talwar International Conference on Machine Learning, 1107-1120, 2023 | 7 | 2023 |
| How many labelers do you have? A closer look at gold-standard labels C Cheng, H Asi, J Duchi arXiv preprint arXiv:2206.12041, 2022 | 7 | 2022 |
| Modeling simple structures and geometry for better stochastic optimization algorithms H Asi, JC Duchi The 22nd International Conference on Artificial Intelligence and Statistics …, 2019 | 6 | 2019 |
| Private optimization in the interpolation regime: faster rates and hardness results H Asi, K Chadha, G Cheng, J Duchi International Conference on Machine Learning, 1025-1045, 2022 | 4 | 2022 |
John DuchiAssociate Professor of Statistics and Electrical Engineering, Stanford University在 stanford.edu 的电子邮件经过验证
