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Publication search results
found 38 matches
- 2024
- Daogao Liu, Hilal Asi:
User-level Differentially Private Stochastic Convex Optimization: Efficient Algorithms with Optimal Rates. AISTATS 2024: 4240-4248 - 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) - Hilal Asi, Daogao Liu, Kevin Tian:
Private Stochastic Convex Optimization with Heavy Tails: Near-Optimality from Simple Reductions. CoRR abs/2406.02789 (2024) - Hilal Asi, Tomer Koren, Daogao Liu, Kunal Talwar:
Private Online Learning via Lazy Algorithms. CoRR abs/2406.03620 (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 abs/2406.14322 (2024) - 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: 2399-2439 - 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 - 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 - 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:
Private Convex Optimization in General Norms. SODA 2023: 5068-5089 - 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 - Hu Fu, Jiawei Li, Daogao Liu:
Pandora Box Problem with Nonobligatory Inspection: Hardness and Approximation Scheme. STOC 2023: 789-802 - 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) - 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) - 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) - 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) - 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) - 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) - Hilal Asi, Daogao Liu:
User-level Differentially Private Stochastic Convex Optimization: Efficient Algorithms with Optimal Rates. CoRR abs/2311.03797 (2023) - 2022
- Sivakanth Gopi, Yin Tat Lee, Daogao Liu:
Private Convex Optimization via Exponential Mechanism. COLT 2022: 1948-1989 - Daogao Liu:
Better Private Algorithms for Correlation Clustering. COLT 2022: 5391-5412 - 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 - Jian Li, Daogao Liu:
Multi-token Markov Game with Switching Costs. SODA 2022: 1780-1807 - Daogao Liu:
Better Private Algorithms for Correlation Clustering. CoRR abs/2202.10747 (2022) - Sivakanth Gopi, Yin Tat Lee, Daogao Liu:
Private Convex Optimization via Exponential Mechanism. CoRR abs/2203.00263 (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 abs/2207.00160 (2022) - Sivakanth Gopi, Yin Tat Lee, Daogao Liu, Ruoqi Shen, Kevin Tian:
Private Convex Optimization in General Norms. CoRR abs/2207.08347 (2022) - Hu Fu, Jiawei Li, Daogao Liu:
Pandora Box Problem with Nonobligatory Inspection: Hardness and Improved Approximation Algorithms. CoRR abs/2207.09545 (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 abs/2210.11768 (2022) - 2021
- Janardhan Kulkarni, Yin Tat Lee, Daogao Liu:
Private Non-smooth ERM and SCO in Subquadratic Steps. NeurIPS 2021: 4053-4064
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