| Differentially private linear sketches: Efficient implementations and applications F Zhao, D Qiao, R Redberg, D Agrawal, A El Abbadi, YX Wang Advances in Neural Information Processing Systems 35, 12691-12704, 2022 | 30 | 2022 |
| Tree++: Truncated tree based graph kernels W Ye, Z Wang, R Redberg, A Singh IEEE Transactions on Knowledge and Data Engineering 33 (4), 1778-1789, 2019 | 23 | 2019 |
| Privately publishable per-instance privacy R Redberg, YX Wang Advances in Neural Information Processing Systems 34, 17335-17346, 2021 | 22 | 2021 |
| Improving the privacy and practicality of objective perturbation for differentially private linear learners R Redberg, A Koskela, YX Wang Advances in Neural Information Processing Systems 36, 13819-13853, 2023 | 8 | 2023 |
| Generalized ptr: User-friendly recipes for data-adaptive algorithms with differential privacy R Redberg, Y Zhu, YX Wang International Conference on Artificial Intelligence and Statistics, 3977-4005, 2023 | 8 | 2023 |
| Tractable mcmc for private learning with pure and gaussian differential privacy Y Lin, YA Ma, YX Wang, R Redberg, Z Bu arXiv preprint arXiv:2310.14661, 2023 | 4 | 2023 |
| Privacy profiles for private selection A Koskela, R Redberg, YX Wang arXiv preprint arXiv:2402.06701, 2024 | 1 | 2024 |
| Noise-adding and beyond: A study in data-adaptive methods for differential privacy R Redberg University of California, Santa Barbara, 2023 | | 2023 |