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Daogao Liu
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2020 – today
- 2024
- [j1]Sivakanth Gopi, Yin Tat Lee, Daogao Liu:
Private Convex Optimization via Exponential Mechanism. J. Priv. Confidentiality 14(1) (2024) - [c17]Daogao Liu, Hilal Asi:
User-level Differentially Private Stochastic Convex Optimization: Efficient Algorithms with Optimal Rates. AISTATS 2024: 4240-4248 - [c16]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 - [c15]Gavin Brown, Krishnamurthy Dj Dvijotham, Georgina Evans, Daogao Liu, Adam Smith, Abhradeep Guha Thakurta:
Private Gradient Descent for Linear Regression: Tighter Error Bounds and Instance-Specific Uncertainty Estimation. ICML 2024 - [i28]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 abs/2402.13531 (2024) - [i27]Hilal Asi, Daogao Liu, Kevin Tian:
Private Stochastic Convex Optimization with Heavy Tails: Near-Optimality from Simple Reductions. CoRR abs/2406.02789 (2024) - [i26]Hilal Asi, Tomer Koren, Daogao Liu, Kunal Talwar:
Private Online Learning via Lazy Algorithms. CoRR abs/2406.03620 (2024) - [i25]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 abs/2406.14322 (2024) - [i24]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 abs/2407.06460 (2024) - [i23]Guy Kornowski, Daogao Liu, Kunal Talwar:
Improved Sample Complexity for Private Nonsmooth Nonconvex Optimization. CoRR abs/2410.05880 (2024) - [i22]Daogao Liu, Kunal Talwar:
Adaptive Batch Size for Privately Finding Second-Order Stationary Points. CoRR abs/2410.07502 (2024) - [i21]Yangsibo Huang, Daogao Liu, Lynn Chua, Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Milad Nasr, Amer Sinha, Chiyuan Zhang:
Unlearn and Burn: Adversarial Machine Unlearning Requests Destroy Model Accuracy. CoRR abs/2410.09591 (2024) - [i20]Andrew Lowy, Daogao Liu, Hilal Asi:
Faster Algorithms for User-Level Private Stochastic Convex Optimization. CoRR abs/2410.18391 (2024) - 2023
- [c14]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: 2399-2439 - [c13]Yair Carmon, Arun Jambulapati, Yujia Jin, Yin Tat Lee, Daogao Liu, Aaron Sidford, Kevin Tian:
ReSQueing Parallel and Private Stochastic Convex Optimization. FOCS 2023: 2031-2058 - [c12]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 - [c11]Daogao Liu, Arun Ganesh, Sewoong Oh, Abhradeep Guha Thakurta:
Private (Stochastic) Non-Convex Optimization Revisited: Second-Order Stationary Points and Excess Risks. NeurIPS 2023 - [c10]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: 4667-4767 - [c9]Sivakanth Gopi, Yin Tat Lee, Daogao Liu, Ruoqi Shen, Kevin Tian:
Private Convex Optimization in General Norms. SODA 2023: 5068-5089 - [c8]Hu Fu, Jiawei Li, Daogao Liu:
Pandora Box Problem with Nonobligatory Inspection: Hardness and Approximation Scheme. STOC 2023: 789-802 - [i19]Yair Carmon, Arun Jambulapati, Yujia Jin, Yin Tat Lee, Daogao Liu, Aaron Sidford, Kevin Tian:
ReSQueing Parallel and Private Stochastic Convex Optimization. CoRR abs/2301.00457 (2023) - [i18]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 abs/2302.06085 (2023) - [i17]Arun Ganesh, Daogao Liu, Sewoong Oh, Abhradeep Thakurta:
Private (Stochastic) Non-Convex Optimization Revisited: Second-Order Stationary Points and Excess Risks. CoRR abs/2302.09699 (2023) - [i16]Yangsibo Huang, Daogao Liu, Zexuan Zhong, Weijia Shi, Yin Tat Lee:
kNN-Adapter: Efficient Domain Adaptation for Black-Box Language Models. CoRR abs/2302.10879 (2023) - [i15]Yangsibo Huang, Haotian Jiang, Daogao Liu, Mohammad Mahdian, Jieming Mao, Vahab Mirrokni:
Learning across Data Owners with Joint Differential Privacy. CoRR abs/2305.15723 (2023) - [i14]Weijia Shi, Anirudh Ajith
, Mengzhou Xia, Yangsibo Huang, Daogao Liu, Terra Blevins, Danqi Chen, Luke Zettlemoyer:
Detecting Pretraining Data from Large Language Models. CoRR abs/2310.16789 (2023) - [i13]Hilal Asi, Daogao Liu:
User-level Differentially Private Stochastic Convex Optimization: Efficient Algorithms with Optimal Rates. CoRR abs/2311.03797 (2023) - 2022
- [c7]Sivakanth Gopi, Yin Tat Lee, Daogao Liu:
Private Convex Optimization via Exponential Mechanism. COLT 2022: 1948-1989 - [c6]Daogao Liu:
Better Private Algorithms for Correlation Clustering. COLT 2022: 5391-5412 - [c5]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 - [c4]Jian Li, Daogao Liu:
Multi-token Markov Game with Switching Costs. SODA 2022: 1780-1807 - [i12]Daogao Liu:
Better Private Algorithms for Correlation Clustering. CoRR abs/2202.10747 (2022) - [i11]Sivakanth Gopi, Yin Tat Lee, Daogao Liu:
Private Convex Optimization via Exponential Mechanism. CoRR abs/2203.00263 (2022) - [i10]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 abs/2207.00160 (2022) - [i9]Sivakanth Gopi, Yin Tat Lee, Daogao Liu, Ruoqi Shen, Kevin Tian:
Private Convex Optimization in General Norms. CoRR abs/2207.08347 (2022) - [i8]Hu Fu, Jiawei Li, Daogao Liu:
Pandora Box Problem with Nonobligatory Inspection: Hardness and Improved Approximation Algorithms. CoRR abs/2207.09545 (2022) - [i7]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 abs/2210.11768 (2022) - 2021
- [c3]Janardhan Kulkarni, Yin Tat Lee, Daogao Liu:
Private Non-smooth ERM and SCO in Subquadratic Steps. NeurIPS 2021: 4053-4064 - [i6]Janardhan Kulkarni, Yin Tat Lee, Daogao Liu:
Private Non-smooth Empirical Risk Minimization and Stochastic Convex Optimization in Subquadratic Steps. CoRR abs/2103.15352 (2021) - [i5]Daogao Liu, Zhou Lu:
Curse of Dimensionality in Unconstrained Private Convex ERM. CoRR abs/2105.13637 (2021) - [i4]Daogao Liu, Zhou Lu:
The Convergence Rate of SGD's Final Iterate: Analysis on Dimension Dependence. CoRR abs/2106.14588 (2021) - [i3]Jian Li, Daogao Liu:
Markov Game with Switching Costs. CoRR abs/2107.05822 (2021) - 2020
- [c2]Haotian Jiang, Jian Li, Daogao Liu, Sahil Singla:
Algorithms and Adaptivity Gaps for Stochastic k-TSP. ITCS 2020: 45:1-45:25 - [i2]Yaonan Jin, Daogao Liu, Zhao Song:
A robust multi-dimensional sparse Fourier transform in the continuous setting. CoRR abs/2005.06156 (2020)
2010 – 2019
- 2019
- [c1]Daogao Liu:
More Efficient Algorithms for Stochastic Diameter and Some Unapproximated Problems in Metric Space. COCOON 2019: 397-411 - [i1]Haotian Jiang, Jian Li, Daogao Liu, Sahil Singla:
Algorithms and Adaptivity Gaps for Stochastic k-TSP. CoRR abs/1911.02506 (2019)
Coauthor Index
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last updated on 2024-11-28 21:29 CET by the dblp team
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