Daogao Liu - RankMe

61 papers A* 21A 2Misc 1Journal 36

Year	Rank	Type	Title / Venue / Authors
2025	A*	`conf`	Adaptive Batch Size for Privately Finding Second-Order Stationary Points. ICLR Daogao Liu, Kunal Talwar
2025	—	`—`	Advancing Differentially Private Optimization: Efficiency, Utility, and Applications Daogao Liu
2025	J	`jnl`	Differentially Private Bilevel Optimization: Efficient Algorithms with Near-Optimal Rates. CoRR Andrew Lowy, Daogao Liu
2025	J	`jnl`	FlexOlmo: Open Language Models for Flexible Data Use. CoRR Weijia Shi, Akshita Bhagia, Kevin Farhat, Niklas Muennighoff, Pete Walsh, Jacob Morrison, Dustin Schwenk, Shayne Longpre, Jake Poznanski, Allyson Ettinger, Daogao Liu, Margaret Li, Dirk Groeneveld, Mike Lewis, Wen-tau Yih, Luca Soldaini, Kyle Lo, Noah A. Smith, Luke Zettlemoyer, Pang Wei Koh, Hannaneh Hajishirzi, Ali Farhadi, Sewon Min
2025	A*	`conf`	Improved Sample Complexity for Private Nonsmooth Nonconvex Optimization. ICML Guy Kornowski, Daogao Liu, Kunal Talwar
2025	J	`jnl`	Linear-Time User-Level DP-SCO via Robust Statistics. CoRR Badih Ghazi, Ravi Kumar, Daogao Liu, Pasin Manurangsi
2025	A*	`conf`	MUSE: Machine Unlearning Six-Way Evaluation for Language Models. ICLR Weijia Shi, Jaechan Lee, Yangsibo Huang, Sadhika Malladi, Jieyu Zhao, Ari Holtzman, Daogao Liu, Luke Zettlemoyer, Noah A. Smith, Chiyuan Zhang
2025	J	`jnl`	Private Geometric Median in Nearly-Linear Time. CoRR Syamantak Kumar, Daogao Liu, Kevin Tian, Chutong Yang
2025	J	`jnl`	Scaling Embedding Layers in Language Models. CoRR Da Yu, Edith Cohen, Badih Ghazi, Yangsibo Huang, Pritish Kamath, Ravi Kumar, Daogao Liu, Chiyuan Zhang
2025	A*	`conf`	Unlearn and Burn: Adversarial Machine Unlearning Requests Destroy Model Accuracy. ICLR Yangsibo Huang, Daogao Liu, Lynn Chua, Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Milad Nasr, Amer Sinha, Chiyuan Zhang
2025	J	`jnl`	Urania: Differentially Private Insights into AI Use. CoRR Daogao Liu, Edith Cohen, Badih Ghazi, Peter Kairouz, Pritish Kamath, Alexander Knop, Ravi Kumar, Pasin Manurangsi, Adam Sealfon, Da Yu, Chiyuan Zhang
2025	J	`jnl`	VaultGemma: A Differentially Private Gemma Model. CoRR Amer Sinha, Thomas Mesnard, Ryan McKenna, Daogao Liu, Christopher A. Choquette-Choo, Yangsibo Huang, Da Yu, Georgios Kaissis, Zachary Charles, Ruibo Liu, Lynn Chua, Pritish Kamath, Pasin Manurangsi, Steve He, Chiyuan Zhang, Badih Ghazi, Borja de Balle Pigem, Prem Eruvbetine, Tris Warkentin, Armand Joulin, Ravi Kumar
2024	J	`jnl`	Adaptive Batch Size for Privately Finding Second-Order Stationary Points. CoRR Daogao Liu, Kunal Talwar
2024	A*	`conf`	Detecting Pretraining Data from Large Language Models. ICLR Weijia Shi, Anirudh Ajith, Mengzhou Xia, Yangsibo Huang, Daogao Liu, Terra Blevins, Danqi Chen, Luke Zettlemoyer
2024	A*	`conf`	Faster Algorithms for User-Level Private Stochastic Convex Optimization. NeurIPS Andrew Lowy, Daogao Liu, Hilal Asi
2024	J	`jnl`	Faster Algorithms for User-Level Private Stochastic Convex Optimization. CoRR Andrew Lowy, Daogao Liu, Hilal Asi
2024	J	`jnl`	Improved Sample Complexity for Private Nonsmooth Nonconvex Optimization. CoRR Guy Kornowski, Daogao Liu, Kunal Talwar
2024	J	`jnl`	MUSE: Machine Unlearning Six-Way Evaluation for Language Models. CoRR Weijia Shi, Jaechan Lee, Yangsibo Huang, Sadhika Malladi, Jieyu Zhao, Ari Holtzman, Daogao Liu, Luke Zettlemoyer, Noah A. Smith, Chiyuan Zhang
2024	J	`jnl`	Mind the Privacy Unit! User-Level Differential Privacy for Language Model Fine-Tuning. CoRR Lynn Chua, Badih Ghazi, Yangsibo Huang, Pritish Kamath, Daogao Liu, Pasin Manurangsi, Amer Sinha, Chiyuan Zhang
2024	J	`jnl`	Private Convex Optimization via Exponential Mechanism. J. Priv. Confidentiality Sivakanth Gopi, Yin Tat Lee, Daogao Liu
2024	A*	`conf`	Private Gradient Descent for Linear Regression: Tighter Error Bounds and Instance-Specific Uncertainty Estimation. ICML Gavin Brown, Krishnamurthy Dj Dvijotham, Georgina Evans, Daogao Liu, Adam Smith, Abhradeep Guha Thakurta
2024	J	`jnl`	Private Gradient Descent for Linear Regression: Tighter Error Bounds and Instance-Specific Uncertainty Estimation. CoRR Gavin Brown, Krishnamurthy Dvijotham, Georgina Evans, Daogao Liu, Adam Smith, Abhradeep Thakurta
2024	A*	`conf`	Private Online Learning via Lazy Algorithms. NeurIPS Hilal Asi, Tomer Koren, Daogao Liu, Kunal Talwar
2024	J	`jnl`	Private Online Learning via Lazy Algorithms. CoRR Hilal Asi, Tomer Koren, Daogao Liu, Kunal Talwar
2024	A*	`conf`	Private Stochastic Convex Optimization with Heavy Tails: Near-Optimality from Simple Reductions. NeurIPS Hilal Asi, Daogao Liu, Kevin Tian
2024	J	`jnl`	Private Stochastic Convex Optimization with Heavy Tails: Near-Optimality from Simple Reductions. CoRR Hilal Asi, Daogao Liu, Kevin Tian
2024	J	`jnl`	Unlearn and Burn: Adversarial Machine Unlearning Requests Destroy Model Accuracy. CoRR Yangsibo Huang, Daogao Liu, Lynn Chua, Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Milad Nasr, Amer Sinha, Chiyuan Zhang
2024	A	`conf`	User-level Differentially Private Stochastic Convex Optimization: Efficient Algorithms with Optimal Rates. AISTATS Daogao Liu, Hilal Asi
2023	A*	`conf`	Algorithmic Aspects of the Log-Laplace Transform and a Non-Euclidean Proximal Sampler. COLT Sivakanth Gopi, Yin Tat Lee, Daogao Liu, Ruoqi Shen, Kevin Tian
2023	J	`jnl`	Algorithmic Aspects of the Log-Laplace Transform and a Non-Euclidean Proximal Sampler. CoRR Sivakanth Gopi, Yin Tat Lee, Daogao Liu, Ruoqi Shen, Kevin Tian
2023	A*	`conf`	Augmentation with Projection: Towards an Effective and Efficient Data Augmentation Paradigm for Distillation. ICLR Ziqi Wang, Yuexin Wu, Frederick Liu, Daogao Liu, Le Hou, Hongkun Yu, Jing Li, Heng Ji
2023	J	`jnl`	Detecting Pretraining Data from Large Language Models. CoRR Weijia Shi, Anirudh Ajith, Mengzhou Xia, Yangsibo Huang, Daogao Liu, Terra Blevins, Danqi Chen, Luke Zettlemoyer
2023	J	`jnl`	Learning across Data Owners with Joint Differential Privacy. CoRR Yangsibo Huang, Haotian Jiang, Daogao Liu, Mohammad Mahdian, Jieming Mao, Vahab Mirrokni
2023	A*	`conf`	Pandora Box Problem with Nonobligatory Inspection: Hardness and Approximation Scheme. STOC Hu Fu, Jiawei Li, Daogao Liu
2023	A*	`conf`	Private (Stochastic) Non-Convex Optimization Revisited: Second-Order Stationary Points and Excess Risks. NeurIPS Daogao Liu, Arun Ganesh, Sewoong Oh, Abhradeep Guha Thakurta
2023	J	`jnl`	Private (Stochastic) Non-Convex Optimization Revisited: Second-Order Stationary Points and Excess Risks. CoRR Arun Ganesh, Daogao Liu, Sewoong Oh, Abhradeep Thakurta
2023	A*	`conf`	Private Convex Optimization in General Norms. SODA Sivakanth Gopi, Yin Tat Lee, Daogao Liu, Ruoqi Shen, Kevin Tian
2023	A*	`conf`	ReSQueing Parallel and Private Stochastic Convex Optimization. FOCS Yair Carmon, Arun Jambulapati, Yujia Jin, Yin Tat Lee, Daogao Liu, Aaron Sidford, Kevin Tian
2023	J	`jnl`	ReSQueing Parallel and Private Stochastic Convex Optimization. CoRR Yair Carmon, Arun Jambulapati, Yujia Jin, Yin Tat Lee, Daogao Liu, Aaron Sidford, Kevin Tian
2023	A*	`conf`	Super-resolution and Robust Sparse Continuous Fourier Transform in Any Constant Dimension: Nearly Linear Time and Sample Complexity. SODA Yaonan Jin, Daogao Liu, Zhao Song
2023	J	`jnl`	User-level Differentially Private Stochastic Convex Optimization: Efficient Algorithms with Optimal Rates. CoRR Hilal Asi, Daogao Liu
2023	J	`jnl`	kNN-Adapter: Efficient Domain Adaptation for Black-Box Language Models. CoRR Yangsibo Huang, Daogao Liu, Zexuan Zhong, Weijia Shi, Yin Tat Lee
2022	J	`jnl`	Augmentation with Projection: Towards an Effective and Efficient Data Augmentation Paradigm for Distillation. CoRR Ziqi Wang, Yuexin Wu, Frederick Liu, Daogao Liu, Le Hou, Hongkun Yu, Jing Li, Heng Ji
2022	A*	`conf`	Better Private Algorithms for Correlation Clustering. COLT Daogao Liu
2022	J	`jnl`	Better Private Algorithms for Correlation Clustering. CoRR Daogao Liu
2022	A*	`conf`	Multi-token Markov Game with Switching Costs. SODA Jian Li, Daogao Liu
2022	J	`jnl`	Pandora Box Problem with Nonobligatory Inspection: Hardness and Improved Approximation Algorithms. CoRR Hu Fu, Jiawei Li, Daogao Liu
2022	J	`jnl`	Private Convex Optimization in General Norms. CoRR Sivakanth Gopi, Yin Tat Lee, Daogao Liu, Ruoqi Shen, Kevin Tian
2022	A*	`conf`	Private Convex Optimization via Exponential Mechanism. COLT Sivakanth Gopi, Yin Tat Lee, Daogao Liu
2022	J	`jnl`	Private Convex Optimization via Exponential Mechanism. CoRR Sivakanth Gopi, Yin Tat Lee, Daogao Liu
2022	A*	`conf`	When Does Differentially Private Learning Not Suffer in High Dimensions? NeurIPS Xuechen Li, Daogao Liu, Tatsunori B. Hashimoto, Huseyin A. Inan, Janardhan Kulkarni, Yin Tat Lee, Abhradeep Guha Thakurta
2022	J	`jnl`	When Does Differentially Private Learning Not Suffer in High Dimensions? CoRR Xuechen Li, Daogao Liu, Tatsunori Hashimoto, Huseyin A. Inan, Janardhan Kulkarni, Yin Tat Lee, Abhradeep Guha Thakurta
2021	J	`jnl`	Curse of Dimensionality in Unconstrained Private Convex ERM. CoRR Daogao Liu, Zhou Lu
2021	J	`jnl`	Markov Game with Switching Costs. CoRR Jian Li, Daogao Liu
2021	A*	`conf`	Private Non-smooth ERM and SCO in Subquadratic Steps. NeurIPS Janardhan Kulkarni, Yin Tat Lee, Daogao Liu
2021	J	`jnl`	Private Non-smooth Empirical Risk Minimization and Stochastic Convex Optimization in Subquadratic Steps. CoRR Janardhan Kulkarni, Yin Tat Lee, Daogao Liu
2021	J	`jnl`	The Convergence Rate of SGD's Final Iterate: Analysis on Dimension Dependence. CoRR Daogao Liu, Zhou Lu
2020	J	`jnl`	A robust multi-dimensional sparse Fourier transform in the continuous setting. CoRR Yaonan Jin, Daogao Liu, Zhao Song
2020	A	`conf`	Algorithms and Adaptivity Gaps for Stochastic k-TSP. ITCS Haotian Jiang, Jian Li, Daogao Liu, Sahil Singla
2019	J	`jnl`	Algorithms and Adaptivity Gaps for Stochastic k-TSP. CoRR Haotian Jiang, Jian Li, Daogao Liu, Sahil Singla
2019	Misc	`conf`	More Efficient Algorithms for Stochastic Diameter and Some Unapproximated Problems in Metric Space. COCOON Daogao Liu