| Detecting pretraining data from large language models W Shi, A Ajith, M Xia, Y Huang, D Liu, T Blevins, D Chen, L Zettlemoyer arXiv preprint arXiv:2310.16789, 2023 | 44 | 2023 |
| Private non-smooth erm and sco in subquadratic steps J Kulkarni, YT Lee, D Liu Advances in Neural Information Processing Systems 34, 4053-4064, 2021 | 43* | 2021 |
| Private convex optimization via exponential mechanism S Gopi, YT Lee, D Liu COLT 2022, 2022 | 42 | 2022 |
| When Does Differentially Private Learning Not Suffer in High Dimensions? X Li, D Liu, T Hashimoto, HA Inan, J Kulkarni, YT Lee, AG Thakurta Neurips 2022, 2022 | 41 | 2022 |
| Super-resolution and robust sparse continuous fourier transform in any constant dimension: Nearly linear time and sample complexity Y Jin, D Liu, Z Song Proceedings of the 2023 Annual ACM-SIAM Symposium on Discrete Algorithms …, 2023 | 19* | 2023 |
| Private Convex Optimization in General Norms S Gopi, YT Lee, D Liu, R Shen, K Tian SODA 2023, 2022 | 10 | 2022 |
| Algorithms and Adaptivity Gaps for Stochastic -TSP H Jiang, J Li, D Liu, S Singla ITCS 2020, 2019 | 9 | 2019 |
| Pandora box problem with nonobligatory inspection: Hardness and approximation scheme H Fu, J Li, D Liu Proceedings of the 55th Annual ACM Symposium on Theory of Computing, 789-802, 2023 | 8* | 2023 |
| Private (Stochastic) Non-Convex Optimization Revisited: Second-Order Stationary Points and Excess Risks D Liu, A Ganesh, S Oh, A Guha Thakurta Advances in Neural Information Processing Systems 36, 2024 | 6* | 2024 |
| NN-Adapter: Efficient Domain Adaptation for Black-Box Language Models Y Huang, D Liu, Z Zhong, W Shi, YT Lee arXiv preprint arXiv:2302.10879, 2023 | 6 | 2023 |
| Algorithmic aspects of the log-Laplace transform and a non-Euclidean proximal sampler S Gopi, YT Lee, D Liu, R Shen, K Tian COLT 2023, 2023 | 6 | 2023 |
| Resqueing parallel and private stochastic convex optimization Y Carmon, A Jambulapati, Y Jin, YT Lee, D Liu, A Sidford, K Tian FOCS 2023, 2023 | 6 | 2023 |
| Augmentation with Projection: Towards an Effective and Efficient Data Augmentation Paradigm for Distillation Z Wang, Y Wu, F Liu, D Liu, L Hou, H Yu, J Li, H Ji ICLR 2023, 2022 | 5 | 2022 |
| Better Private Algorithms for Correlation Clustering D Liu COLT 2022, 2022 | 5 | 2022 |
| Lower Bounds for Differentially Private ERM: Unconstrained and Non-Euclidean D Liu, Z Lu arXiv preprint arXiv:2105.13637, 2021 | 3* | 2021 |
| The Convergence Rate of SGD's Final Iterate: Analysis on Dimension Dependence D Liu, Z Lu arXiv preprint arXiv:2106.14588, 2021 | 2 | 2021 |
| Private Gradient Descent for Linear Regression: Tighter Error Bounds and Instance-Specific Uncertainty Estimation G Brown, K Dvijotham, G Evans, D Liu, A Smith, A Thakurta arXiv preprint arXiv:2402.13531, 2024 | | 2024 |
| Variable Neighborhood Searching Rerandomization J Lu, D Liu arXiv preprint arXiv:2312.17230, 2023 | | 2023 |
| User-level Differentially Private Stochastic Convex Optimization: Efficient Algorithms with Optimal Rates H Asi, D Liu arXiv preprint arXiv:2311.03797, 2023 | | 2023 |
| Learning across Data Owners with Joint Differential Privacy Y Huang, H Jiang, D Liu, M Mahdian, J Mao, V Mirrokni arXiv preprint arXiv:2305.15723, 2023 | | 2023 |
