Detecting pretraining data from large language models W Shi, A Ajith, M Xia, Y Huang, D Liu, T Blevins, D Chen, L Zettlemoyer
ICLR 2024, 2023
238 2023 Private convex optimization via exponential mechanism S Gopi, YT Lee, D Liu
COLT 2022, 2022
66 2022 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
62 * 2021 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
61 2022 Muse: Machine unlearning six-way evaluation for language models W Shi, J Lee, Y Huang, S Malladi, J Zhao, A Holtzman, D Liu, ...
arXiv preprint arXiv:2407.06460, 2024
35 2024 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
21 * 2023 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
20 * 2023 Private Convex Optimization in General Norms S Gopi, YT Lee, D Liu, R Shen, K Tian
SODA 2023, 2022
18 2022 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, 65618-65641, 2023
15 2023 Y Huang, D Liu, Z Zhong, W Shi, YT Lee
arXiv preprint arXiv:2302.10879, 2023
15 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
15 2023 Algorithms and Adaptivity Gaps for Stochastic H Jiang, J Li, D Liu, S Singla
ITCS 2020, 2019
15 2019 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
14 2023 User-level differentially private stochastic convex optimization: Efficient algorithms with optimal rates D Liu, H Asi
International Conference on Artificial Intelligence and Statistics, 4240-4248, 2024
11 2024 Better Private Algorithms for Correlation Clustering D Liu
COLT 2022, 2022
9 2022 Mind the Privacy Unit! User-Level Differential Privacy for Language Model Fine-Tuning L Chua, B Ghazi, Y Huang, P Kamath, D Liu, P Manurangsi, A Sinha, ...
COLM 2024, 2024
8 2024 The Power of Sampling: Dimension-free Risk Bounds in Private ERM YT Lee, D Liu, Z Lu
arXiv preprint arXiv:2105.13637, 2021
8 * 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
ICML 2024, 2024
4 2024 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
4 2022 Private stochastic convex optimization with heavy tails: Near-optimality from simple reductions H Asi, D Liu, K Tian
Advances in Neural Information Processing Systems 37, 59174-59215, 2025
3 2025 