| Practical differentially private top-k selection with pay-what-you-get composition D Durfee, RM Rogers Advances in Neural Information Processing Systems 32, 2019 | 105 | 2019 |
| LinkedIn's Audience Engagements API: A privacy preserving data analytics system at scale R Rogers, S Subramaniam, S Peng, D Durfee, S Lee, SK Kancha, ... arXiv preprint arXiv:2002.05839, 2020 | 94 | 2020 |
| Sampling random spanning trees faster than matrix multiplication D Durfee, R Kyng, J Peebles, AB Rao, S Sachdeva Proceedings of the 49th Annual ACM SIGACT Symposium on Theory of Computing …, 2017 | 87 | 2017 |
| On fully dynamic graph sparsifiers I Abraham, D Durfee, I Koutis, S Krinninger, R Peng 2016 IEEE 57th Annual Symposium on Foundations of Computer Science (FOCS …, 2016 | 78 | 2016 |
| Optimal differential privacy composition for exponential mechanisms J Dong, D Durfee, R Rogers International Conference on Machine Learning, 2597-2606, 2020 | 67 | 2020 |
| Fully dynamic spectral vertex sparsifiers and applications D Durfee, Y Gao, G Goranci, R Peng Proceedings of the 51st Annual ACM SIGACT Symposium on Theory of Computing …, 2019 | 48 | 2019 |
| On the complexity of nash equilibria in anonymous games X Chen, D Durfee, A Orfanou Proceedings of the forty-seventh annual ACM symposium on Theory of computing …, 2015 | 37 | 2015 |
| Determinant-preserving sparsification of SDDM matrices with applications to counting and sampling spanning trees D Durfee, J Peebles, R Peng, AB Rao 2017 IEEE 58th Annual Symposium on Foundations of Computer Science (FOCS …, 2017 | 36 | 2017 |
| Individual sensitivity preprocessing for data privacy R Cummings, D Durfee Proceedings of the Fourteenth Annual ACM-SIAM Symposium on Discrete …, 2020 | 33 | 2020 |
| Parallel batch-dynamic graphs: Algorithms and lower bounds L Dhulipala, D Durfee, J Kulkarni, R Peng, S Sawlani, X Sun Proceedings of the Fourteenth Annual ACM-SIAM Symposium on Discrete …, 2020 | 33 | 2020 |
| Regression using Lewis Weights Preconditioning and Stochastic Gradient Descent D Durfee, KA Lai, S Sawlani Conference On Learning Theory, 1626-1656, 2018 | 19 | 2018 |
| msam: Micro-batch-averaged sharpness-aware minimization K Behdin, Q Song, A Gupta, S Keerthi, A Acharya, B Ocejo, G Dexter, ... arXiv preprint arXiv:2302.09693, 2023 | 10 | 2023 |
| Fully dynamic effective resistances D Durfee, Y Gao, G Goranci, R Peng arXiv preprint arXiv:1804.04038, 2018 | 9 | 2018 |
| Determinant-preserving sparsification of SDDM matrices D Durfee, J Peebles, R Peng, AB Rao SIAM Journal on Computing 49 (4), FOCS17-350-FOCS17-408, 2020 | 7 | 2020 |
| Improved deep neural network generalization using m-sharpness-aware minimization K Behdin, Q Song, A Gupta, D Durfee, A Acharya, S Keerthi, R Mazumder arXiv preprint arXiv:2212.04343, 2022 | 6 | 2022 |
| A privacy preserving data analytics system at scale R Rogers, S Subramaniam, S Peng, D Durfee, S Lee, SK Kancha, ... Linkedin’s audience engagements api, 2020 | 5 | 2020 |
| Nearly tight bounds for sandpile transience on the grid D Durfee, M Fahrbach, Y Gao, T Xiao Proceedings of the Twenty-Ninth Annual ACM-SIAM Symposium on Discrete …, 2018 | 5 | 2018 |
| One-shot dp top-k mechanisms. DifferentialPrivacy. org, 08 2021 D Durfee, R Rogers | 5 | |
| Unbounded differentially private quantile and maximum estimation D Durfee Advances in Neural Information Processing Systems 36, 2024 | 4 | 2024 |
| Differentially private top-k selection RM Rogers, DA Durfee, SS Peng, Y Xu US Patent 11,170,131, 2021 | 4 | 2021 |
