| Graphical-model based estimation and inference for differential privacy R McKenna, D Sheldon, G Miklau International Conference on Machine Learning, 4435-4444, 2019 | 175 | 2019 |
| HDMM: Optimizing error of high-dimensional statistical queries under differential privacy R McKenna, G Miklau, M Hay, A Machanavajjhala arXiv preprint arXiv:2106.12118, 2021 | 157 | 2021 |
| Fair decision making using privacy-protected data D Pujol, R McKenna, S Kuppam, M Hay, A Machanavajjhala, G Miklau Proceedings of the 2020 Conference on Fairness, Accountability, and …, 2020 | 138 | 2020 |
| Winning the nist contest: A scalable and general approach to differentially private synthetic data R McKenna, G Miklau, D Sheldon arXiv preprint arXiv:2108.04978, 2021 | 129 | 2021 |
| How does code obfuscation impact energy usage? C Sahin, P Tornquist, R McKenna, Z Pearson, J Clause 2014 IEEE international conference on software maintenance and evolution …, 2014 | 121 | 2014 |
| Benchmarking differentially private synthetic data generation algorithms Y Tao, R McKenna, M Hay, A Machanavajjhala, G Miklau arXiv preprint arXiv:2112.09238, 2021 | 108 | 2021 |
| Ektelo: A framework for defining differentially-private computations D Zhang, R McKenna, I Kotsogiannis, M Hay, A Machanavajjhala, ... Proceedings of the 2018 International Conference on Management of Data, 115-130, 2018 | 83 | 2018 |
| Aim: An adaptive and iterative mechanism for differentially private synthetic data R McKenna, B Mullins, D Sheldon, G Miklau arXiv preprint arXiv:2201.12677, 2022 | 76 | 2022 |
| Permute-and-flip: A new mechanism for differentially private selection R McKenna, DR Sheldon Advances in Neural Information Processing Systems 33, 193-203, 2020 | 69 | 2020 |
| Machine learning predictions of runtime and IO traffic on high-end clusters R McKenna, S Herbein, A Moody, T Gamblin, M Taufer 2016 IEEE International Conference on Cluster Computing (CLUSTER), 255-258, 2016 | 63 | 2016 |
| Differentially private learning of undirected graphical models using collective graphical models G Bernstein, R McKenna, T Sun, D Sheldon, M Hay, G Miklau International Conference on Machine Learning, 478-487, 2017 | 37 | 2017 |
| (Amplified) Banded Matrix Factorization: A unified approach to private training CA Choquette-Choo, A Ganesh, R McKenna, HB McMahan, J Rush, ... Advances in Neural Information Processing Systems 36, 74856-74889, 2023 | 32 | 2023 |
| Gradient descent with linearly correlated noise: Theory and applications to differential privacy A Koloskova, R McKenna, Z Charles, J Rush, HB McMahan Advances in Neural Information Processing Systems 36, 35761-35773, 2023 | 17 | 2023 |
| A workload-adaptive mechanism for linear queries under local differential privacy R McKenna, RK Maity, A Mazumdar, G Miklau arXiv preprint arXiv:2002.01582, 2020 | 14 | 2020 |
| Fine-tuning large language models with user-level differential privacy Z Charles, A Ganesh, R McKenna, HB McMahan, N Mitchell, K Pillutla, ... arXiv preprint arXiv:2407.07737, 2024 | 13 | 2024 |
| Relaxed marginal consistency for differentially private query answering R McKenna, S Pradhan, DR Sheldon, G Miklau Advances in Neural Information Processing Systems 34, 20696-20707, 2021 | 11 | 2021 |
| From HPC performance to climate modeling: Transforming methods for HPC predictions into models of extreme climate conditions R McKinney, VK Pallipuram, R Vargas, M Taufer 2015 IEEE 11th International Conference on e-Science, 108-117, 2015 | 10 | 2015 |
| PSynDB: accurate and accessible private data generation Z Huang, R McKenna, G Bissias, G Miklau, M Hay, A Machanavajjhala Proceedings of the VLDB Endowment 12 (12), 1918-1921, 2019 | 8 | 2019 |
| Joint selection: Adaptively incorporating public information for private synthetic data M Fuentes, BC Mullins, R McKenna, G Miklau, D Sheldon International Conference on Artificial Intelligence and Statistics, 2404-2412, 2024 | 3 | 2024 |
| Scaling up the Banded Matrix Factorization Mechanism for Differentially Private ML R McKenna arXiv preprint arXiv:2405.15913, 2024 | 1 | 2024 |