| 2026 | COLT | Nearly Linear-Time User-Level DP-SCO with Optimal Rates. | Badih Ghazi, Ravi Kumar, Daogao Liu, Pasin Manurangsi |
| 2025 | ICLR | Unlearn and Burn: Adversarial Machine Unlearning Requests Destroy Model Accuracy. | Yangsibo Huang, Daogao Liu, Lynn Chua, Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Milad Nasr, Amer Sinha, Chiyuan Zhang |
| 2025 | ICLR | Adaptive Batch Size for Privately Finding Second-Order Stationary Points. | Daogao Liu, Kunal Talwar |
| 2025 | ICLR | MUSE: Machine Unlearning Six-Way Evaluation for Language Models. | Weijia Shi, Jaechan Lee, Yangsibo Huang, Sadhika Malladi, Jieyu Zhao, Ari Holtzman, Daogao Liu, Luke Zettlemoyer, Noah A. Smith, Chiyuan Zhang |
| 2025 | ICML | Improved Sample Complexity for Private Nonsmooth Nonconvex Optimization. | Guy Kornowski, Daogao Liu, Kunal Talwar |
| 2024 | AISTATS | User-level Differentially Private Stochastic Convex Optimization: Efficient Algorithms with Optimal Rates. | Daogao Liu, Hilal Asi |
| 2024 | ICLR | Detecting Pretraining Data from Large Language Models. | Weijia Shi, Anirudh Ajith, Mengzhou Xia, Yangsibo Huang, Daogao Liu, Terra Blevins, Danqi Chen, Luke Zettlemoyer |
| 2024 | ICML | Private Gradient Descent for Linear Regression: Tighter Error Bounds and Instance-Specific Uncertainty Estimation. | Gavin Brown, Krishnamurthy Dj Dvijotham, Georgina Evans, Daogao Liu, Adam Smith, Abhradeep Guha Thakurta |
| 2023 | COLT | Algorithmic Aspects of the Log-Laplace Transform and a Non-Euclidean Proximal Sampler. | Sivakanth Gopi, Yin Tat Lee, Daogao Liu, Ruoqi Shen, Kevin Tian |
| 2023 | FOCS | ReSQueing Parallel and Private Stochastic Convex Optimization. | Yair Carmon, Arun Jambulapati, Yujia Jin, Yin Tat Lee, Daogao Liu, Aaron Sidford, Kevin Tian |
| 2023 | ICLR | Augmentation with Projection: Towards an Effective and Efficient Data Augmentation Paradigm for Distillation. | Ziqi Wang, Yuexin Wu, Frederick Liu, Daogao Liu, Le Hou, Hongkun Yu, Jing Li, Heng Ji |
| 2023 | SODA | Private Convex Optimization in General Norms. | Sivakanth Gopi, Yin Tat Lee, Daogao Liu, Ruoqi Shen, Kevin Tian |
| 2023 | SODA | Super-resolution and Robust Sparse Continuous Fourier Transform in Any Constant Dimension: Nearly Linear Time and Sample Complexity. | Yaonan Jin, Daogao Liu, Zhao Song |
| 2023 | STOC | Pandora Box Problem with Nonobligatory Inspection: Hardness and Approximation Scheme. | Hu Fu, Jiawei Li, Daogao Liu |
| 2022 | COLT | Private Convex Optimization via Exponential Mechanism. | Sivakanth Gopi, Yin Tat Lee, Daogao Liu |
| 2022 | COLT | Better Private Algorithms for Correlation Clustering. | Daogao Liu |
| 2022 | SODA | Multi-token Markov Game with Switching Costs. | Jian Li, Daogao Liu |
| 2019 | COCOON | More Efficient Algorithms for Stochastic Diameter and Some Unapproximated Problems in Metric Space. | Daogao Liu |