Daogao Liu

Publication Activity (10 Years)

Years Active: 2019-2024
Publications (10 Years): 40

Top Topics

Convex Optimization

Top Venues

Publications

Hilal Asi, Tomer Koren, Daogao Liu, Kunal Talwar
Private Online Learning via Lazy Algorithms. CoRR (2024)
Gavin Brown, Krishnamurthy Dvijotham, Georgina Evans, Daogao Liu, Adam Smith, Abhradeep Thakurta
Private Gradient Descent for Linear Regression: Tighter Error Bounds and Instance-Specific Uncertainty Estimation. CoRR (2024)
Daogao Liu, Hilal Asi
User-level Differentially Private Stochastic Convex Optimization: Efficient Algorithms with Optimal Rates. AISTATS (2024)
Lynn Chua, Badih Ghazi, Yangsibo Huang, Pritish Kamath, Daogao Liu, Pasin Manurangsi, Amer Sinha, Chiyuan Zhang
Mind the Privacy Unit! User-Level Differential Privacy for Language Model Fine-Tuning. CoRR (2024)
Hilal Asi, Daogao Liu, Kevin Tian
Private Stochastic Convex Optimization with Heavy Tails: Near-Optimality from Simple Reductions. CoRR (2024)
Weijia Shi, Jaechan Lee, Yangsibo Huang, Sadhika Malladi, Jieyu Zhao, Ari Holtzman, Daogao Liu, Luke Zettlemoyer, Noah A. Smith, Chiyuan Zhang
MUSE: Machine Unlearning Six-Way Evaluation for Language Models. CoRR (2024)
Weijia Shi, Anirudh Ajith, Mengzhou Xia, Yangsibo Huang, Daogao Liu, Terra Blevins, Danqi Chen, Luke Zettlemoyer
Detecting Pretraining Data from Large Language Models. ICLR (2024)
Arun Ganesh, Daogao Liu, Sewoong Oh, Abhradeep Thakurta
Private (Stochastic) Non-Convex Optimization Revisited: Second-Order Stationary Points and Excess Risks. CoRR (2023)
Hu Fu, Jiawei Li, Daogao Liu
Pandora Box Problem with Nonobligatory Inspection: Hardness and Approximation Scheme. STOC (2023)
Yair Carmon, Arun Jambulapati, Yujia Jin, Yin Tat Lee, Daogao Liu, Aaron Sidford, Kevin Tian
ReSQueing Parallel and Private Stochastic Convex Optimization. CoRR (2023)
Yangsibo Huang, Daogao Liu, Zexuan Zhong, Weijia Shi, Yin Tat Lee
kNN-Adapter: Efficient Domain Adaptation for Black-Box Language Models. CoRR (2023)
Ziqi Wang, Yuexin Wu, Frederick Liu, Daogao Liu, Le Hou, Hongkun Yu, Jing Li, Heng Ji
Augmentation with Projection: Towards an Effective and Efficient Data Augmentation Paradigm for Distillation. ICLR (2023)
Yaonan Jin, Daogao Liu, Zhao Song
Super-resolution and Robust Sparse Continuous Fourier Transform in Any Constant Dimension: Nearly Linear Time and Sample Complexity. SODA (2023)
Yair Carmon, Arun Jambulapati, Yujia Jin, Yin Tat Lee, Daogao Liu, Aaron Sidford, Kevin Tian
ReSQueing Parallel and Private Stochastic Convex Optimization. FOCS (2023)
Sivakanth Gopi, Yin Tat Lee, Daogao Liu, Ruoqi Shen, Kevin Tian
Algorithmic Aspects of the Log-Laplace Transform and a Non-Euclidean Proximal Sampler. CoRR (2023)
Weijia Shi, Anirudh Ajith, Mengzhou Xia, Yangsibo Huang, Daogao Liu, Terra Blevins, Danqi Chen, Luke Zettlemoyer
Detecting Pretraining Data from Large Language Models. CoRR (2023)
Daogao Liu, Arun Ganesh, Sewoong Oh, Abhradeep Guha Thakurta
Private (Stochastic) Non-Convex Optimization Revisited: Second-Order Stationary Points and Excess Risks. NeurIPS (2023)
Sivakanth Gopi, Yin Tat Lee, Daogao Liu, Ruoqi Shen, Kevin Tian
Algorithmic Aspects of the Log-Laplace Transform and a Non-Euclidean Proximal Sampler. COLT (2023)
Sivakanth Gopi, Yin Tat Lee, Daogao Liu, Ruoqi Shen, Kevin Tian
Private Convex Optimization in General Norms. SODA (2023)
Yangsibo Huang, Haotian Jiang, Daogao Liu, Mohammad Mahdian, Jieming Mao, Vahab Mirrokni
Learning across Data Owners with Joint Differential Privacy. CoRR (2023)
Hilal Asi, Daogao Liu
User-level Differentially Private Stochastic Convex Optimization: Efficient Algorithms with Optimal Rates. CoRR (2023)
Sivakanth Gopi, Yin Tat Lee, Daogao Liu
Private Convex Optimization via Exponential Mechanism. COLT (2022)
Xuechen Li, Daogao Liu, Tatsunori B. Hashimoto, Huseyin A. Inan, Janardhan Kulkarni, Yin-Tat Lee, Abhradeep Guha Thakurta
When Does Differentially Private Learning Not Suffer in High Dimensions? NeurIPS (2022)
Xuechen Li, Daogao Liu, Tatsunori Hashimoto, Huseyin A. Inan, Janardhan Kulkarni, Yin Tat Lee, Abhradeep Guha Thakurta
When Does Differentially Private Learning Not Suffer in High Dimensions? CoRR (2022)
Jian Li, Daogao Liu
Multi-token Markov Game with Switching Costs. SODA (2022)
Hu Fu, Jiawei Li, Daogao Liu
Pandora Box Problem with Nonobligatory Inspection: Hardness and Improved Approximation Algorithms. CoRR (2022)
Daogao Liu
Better Private Algorithms for Correlation Clustering. COLT (2022)
Ziqi Wang, Yuexin Wu, Frederick Liu, Daogao Liu, Le Hou, Hongkun Yu, Jing Li, Heng Ji
Augmentation with Projection: Towards an Effective and Efficient Data Augmentation Paradigm for Distillation. CoRR (2022)
Sivakanth Gopi, Yin Tat Lee, Daogao Liu
Private Convex Optimization via Exponential Mechanism. CoRR (2022)
Daogao Liu
Better Private Algorithms for Correlation Clustering. CoRR (2022)
Sivakanth Gopi, Yin Tat Lee, Daogao Liu, Ruoqi Shen, Kevin Tian
Private Convex Optimization in General Norms. CoRR (2022)
Janardhan Kulkarni, Yin Tat Lee, Daogao Liu
Private Non-smooth ERM and SCO in Subquadratic Steps. NeurIPS (2021)
Janardhan Kulkarni, Yin Tat Lee, Daogao Liu
Private Non-smooth Empirical Risk Minimization and Stochastic Convex Optimization in Subquadratic Steps. CoRR (2021)
Daogao Liu, Zhou Lu
Curse of Dimensionality in Unconstrained Private Convex ERM. CoRR (2021)
Jian Li, Daogao Liu
Markov Game with Switching Costs. CoRR (2021)
Daogao Liu, Zhou Lu
The Convergence Rate of SGD's Final Iterate: Analysis on Dimension Dependence. CoRR (2021)
Haotian Jiang, Jian Li, Daogao Liu, Sahil Singla
Algorithms and Adaptivity Gaps for Stochastic k-TSP. ITCS (2020)
Yaonan Jin, Daogao Liu, Zhao Song
A robust multi-dimensional sparse Fourier transform in the continuous setting. CoRR (2020)
Haotian Jiang, Jian Li, Daogao Liu, Sahil Singla
Algorithms and Adaptivity Gaps for Stochastic k-TSP. CoRR (2019)
Daogao Liu
More Efficient Algorithms for Stochastic Diameter and Some Unapproximated Problems in Metric Space. COCOON (2019)