| Algorithmic stability for adaptive data analysis R Bassily, K Nissim, A Smith, T Steinke, U Stemmer, J Ullman Proceedings of the forty-eighth annual ACM symposium on Theory of Computing …, 2016 | 293 | 2016 |
| Practical locally private heavy hitters R Bassily, K Nissim, U Stemmer, A Guha Thakurta Advances in Neural Information Processing Systems 30, 2017 | 280 | 2017 |
| Private learning and sanitization: Pure vs. approximate differential privacy A Beimel, K Nissim, U Stemmer International Workshop on Approximation Algorithms for Combinatorial …, 2013 | 216 | 2013 |
| Differentially private release and learning of threshold functions M Bun, K Nissim, U Stemmer, S Vadhan 2015 IEEE 56th Annual Symposium on Foundations of Computer Science, 634-649, 2015 | 206 | 2015 |
| Heavy hitters and the structure of local privacy M Bun, J Nelson, U Stemmer ACM Transactions on Algorithms (TALG) 15 (4), 1-40, 2019 | 173 | 2019 |
| Characterizing the sample complexity of pure private learners A Beimel, K Nissim, U Stemmer Journal of Machine Learning Research 20 (146), 1-33, 2019 | 108* | 2019 |
| Simultaneous private learning of multiple concepts M Bun, K Nissim, U Stemmer Journal of Machine Learning Research 20 (94), 1-34, 2019 | 92 | 2019 |
| Clustering algorithms for the centralized and local models K Nissim, U Stemmer Algorithmic Learning Theory, 619-653, 2018 | 79 | 2018 |
| Adversarially robust streaming algorithms via differential privacy A Hassidim, H Kaplan, Y Mansour, Y Matias, U Stemmer Journal of the ACM 69 (6), 1-14, 2022 | 68 | 2022 |
| Locally private k-means clustering U Stemmer Journal of Machine Learning Research 22 (176), 1-30, 2021 | 63 | 2021 |
| Learning and evaluating a differentially private pre-trained language model S Hoory, A Feder, A Tendler, S Erell, A Peled-Cohen, I Laish, H Nakhost, ... Findings of the Association for Computational Linguistics: EMNLP 2021, 1178-1189, 2021 | 60 | 2021 |
| Privately learning thresholds: Closing the exponential gap H Kaplan, K Ligett, Y Mansour, M Naor, U Stemmer Conference on Learning Theory, 2263-2285, 2020 | 59 | 2020 |
| Locating a small cluster privately K Nissim, U Stemmer, S Vadhan Proceedings of the 35th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of …, 2016 | 57 | 2016 |
| Differentially private k-means with constant multiplicative error U Stemmer, H Kaplan Advances in Neural Information Processing Systems 31, 2018 | 50 | 2018 |
| Separating adaptive streaming from oblivious streaming using the bounded storage model H Kaplan, Y Mansour, K Nissim, U Stemmer Annual International Cryptology Conference, 94-121, 2021 | 40* | 2021 |
| Friendlycore: Practical differentially private aggregation E Tsfadia, E Cohen, H Kaplan, Y Mansour, U Stemmer International Conference on Machine Learning, 21828-21863, 2022 | 39 | 2022 |
| On the generalization properties of differential privacy K Nissim, U Stemmer arXiv preprint arXiv:1504.05800, 2015 | 37 | 2015 |
| Dynamic algorithms against an adaptive adversary: generic constructions and lower bounds A Beimel, H Kaplan, Y Mansour, K Nissim, T Saranurak, U Stemmer Proceedings of the 54th Annual ACM SIGACT Symposium on Theory of Computing …, 2022 | 35 | 2022 |
| Private center points and learning of halfspaces A Beimel, S Moran, K Nissim, U Stemmer Conference on Learning Theory, 269-282, 2019 | 34 | 2019 |
| Learning privately with labeled and unlabeled examples A Beimel, K Nissim, U Stemmer Proceedings of the twenty-sixth annual ACM-SIAM symposium on Discrete …, 2014 | 32* | 2014 |
Kobbi NissimDept. of Computer Science (Georgetown)在 georgetown.edu 的电子邮件经过验证
