Daogao Liu

According to our database, Daogao Liu authored at least 31 papers between 2019 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Adaptive Batch Size for Privately Finding Second-Order Stationary Points.

Daogao Liu, Kunal Talwar

CoRR, 2024

Improved Sample Complexity for Private Nonsmooth Nonconvex Optimization.

Guy Kornowski, Daogao Liu, Kunal Talwar

CoRR, 2024

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

CoRR, 2024

Mind the Privacy Unit! User-Level Differential Privacy for Language Model Fine-Tuning.

Lynn Chua, Badih Ghazi, Yangsibo Huang, Pritish Kamath, Daogao Liu, Pasin Manurangsi, Amer Sinha, Chiyuan Zhang

CoRR, 2024

Private Online Learning via Lazy Algorithms.

Hilal Asi, Tomer Koren, Daogao Liu, Kunal Talwar

CoRR, 2024

Private Stochastic Convex Optimization with Heavy Tails: Near-Optimality from Simple Reductions.

Hilal Asi, Daogao Liu, Kevin Tian

CoRR, 2024

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

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Detecting Pretraining Data from Large Language Models.

Weijia Shi, Anirudh Ajith, Mengzhou Xia, Yangsibo Huang, Daogao Liu, Terra Blevins, Danqi Chen, Luke Zettlemoyer

Proceedings of the Twelfth International Conference on Learning Representations, 2024

User-level Differentially Private Stochastic Convex Optimization: Efficient Algorithms with Optimal Rates.

Daogao Liu, Hilal Asi

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

2023

Learning across Data Owners with Joint Differential Privacy.

Yangsibo Huang, Haotian Jiang, Daogao Liu, Mohammad Mahdian, Jieming Mao, Vahab Mirrokni

CoRR, 2023

kNN-Adapter: Efficient Domain Adaptation for Black-Box Language Models.

Yangsibo Huang, Daogao Liu, Zexuan Zhong, Weijia Shi, Yin Tat Lee

CoRR, 2023

Pandora Box Problem with Nonobligatory Inspection: Hardness and Approximation Scheme.

Hu Fu, Jiawei Li, Daogao Liu

Proceedings of the 55th Annual ACM Symposium on Theory of Computing, 2023

Super-resolution and Robust Sparse Continuous Fourier Transform in Any Constant Dimension: Nearly Linear Time and Sample Complexity.

Yaonan Jin, Daogao Liu, Zhao Song

Proceedings of the 2023 ACM-SIAM Symposium on Discrete Algorithms, 2023

Private Convex Optimization in General Norms.

Sivakanth Gopi, Yin Tat Lee, Daogao Liu, Ruoqi Shen, Kevin Tian

Proceedings of the 2023 ACM-SIAM Symposium on Discrete Algorithms, 2023

Private (Stochastic) Non-Convex Optimization Revisited: Second-Order Stationary Points and Excess Risks.

Daogao Liu, Arun Ganesh, Sewoong Oh, Abhradeep Guha Thakurta

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

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

Proceedings of the Eleventh International Conference on Learning Representations, 2023

ReSQueing Parallel and Private Stochastic Convex Optimization.

Yair Carmon, Arun Jambulapati, Yujia Jin, Yin Tat Lee, Daogao Liu, Aaron Sidford, Kevin Tian

Proceedings of the 64th IEEE Annual Symposium on Foundations of Computer Science, 2023

Algorithmic Aspects of the Log-Laplace Transform and a Non-Euclidean Proximal Sampler.

Sivakanth Gopi, Yin Tat Lee, Daogao Liu, Ruoqi Shen, Kevin Tian

Proceedings of the Thirty Sixth Annual Conference on Learning Theory, 2023

2022

Pandora Box Problem with Nonobligatory Inspection: Hardness and Improved Approximation Algorithms.

Hu Fu, Jiawei Li, Daogao Liu

CoRR, 2022

Multi-token Markov Game with Switching Costs.

Jian Li, Daogao Liu

Proceedings of the 2022 ACM-SIAM Symposium on Discrete Algorithms, 2022

When Does Differentially Private Learning Not Suffer in High Dimensions?

Xuechen Li, Daogao Liu, Tatsunori B. Hashimoto, Huseyin A. Inan, Janardhan Kulkarni, Yin Tat Lee, Abhradeep Guha Thakurta

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Better Private Algorithms for Correlation Clustering.

Daogao Liu

Proceedings of the Conference on Learning Theory, 2-5 July 2022, London, UK., 2022

Private Convex Optimization via Exponential Mechanism.

Sivakanth Gopi, Yin Tat Lee, Daogao Liu

Proceedings of the Conference on Learning Theory, 2-5 July 2022, London, UK., 2022

2021

Markov Game with Switching Costs.

Jian Li, Daogao Liu

CoRR, 2021

The Convergence Rate of SGD's Final Iterate: Analysis on Dimension Dependence.

Daogao Liu, Zhou Lu

CoRR, 2021

Curse of Dimensionality in Unconstrained Private Convex ERM.

Daogao Liu, Zhou Lu

CoRR, 2021

Private Non-smooth Empirical Risk Minimization and Stochastic Convex Optimization in Subquadratic Steps.

Janardhan Kulkarni, Yin Tat Lee, Daogao Liu

CoRR, 2021

Private Non-smooth ERM and SCO in Subquadratic Steps.

Janardhan Kulkarni, Yin Tat Lee, Daogao Liu

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

2020

A robust multi-dimensional sparse Fourier transform in the continuous setting.

Yaonan Jin, Daogao Liu, Zhao Song

CoRR, 2020

Algorithms and Adaptivity Gaps for Stochastic k-TSP.

Haotian Jiang, Jian Li, Daogao Liu, Sahil Singla

Proceedings of the 11th Innovations in Theoretical Computer Science Conference, 2020

2019

More Efficient Algorithms for Stochastic Diameter and Some Unapproximated Problems in Metric Space.

Daogao Liu

Proceedings of the Computing and Combinatorics - 25th International Conference, 2019