Liu, Daogao

22 publications

ICLR 2025 Adaptive Batch Size for Privately Finding Second-Order Stationary Points Daogao Liu, Kunal Talwar

NeurIPS 2025 Differentially Private Bilevel Optimization: Efficient Algorithms with Near-Optimal Rates Andrew Lowy, Daogao Liu

NeurIPS 2025 FlexOLMo: Open Language Models for Flexible Data Use Weijia Shi, Akshita Bhagia, Kevin Farhat, Niklas Muennighoff, Jacob Morrison, Evan Pete Walsh, Dustin Schwenk, Shayne Longpre, Jake Poznanski, Allyson Ettinger, Daogao Liu, Margaret Li, Mike Lewis, Wen-tau Yih, Dirk Groeneveld, Luca Soldaini, Kyle Lo, Noah A. Smith, Luke Zettlemoyer, Pang Wei Koh, Hannaneh Hajishirzi, Ali Farhadi, Sewon Min

ICML 2025 Improved Sample Complexity for Private Nonsmooth Nonconvex Optimization Guy Kornowski, Daogao Liu, Kunal Talwar

ICLR 2025 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

NeurIPS 2025 Private Geometric Median in Nearly-Linear Time Syamantak Kumar, Daogao Liu, Kevin Tian, Chutong Yang

NeurIPS 2025 Scaling Embedding Layers in Language Models Da Yu, Edith Cohen, Badih Ghazi, Yangsibo Huang, Pritish Kamath, Ravi Kumar, Daogao Liu, Chiyuan Zhang

ICLR 2025 Unlearn and Burn: Adversarial Machine Unlearning Requests Destroy Model Accuracy Yangsibo Huang, Daogao Liu, Lynn Chua, Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Milad Nasr, Amer Sinha, Chiyuan Zhang

ICLR 2024 Detecting Pretraining Data from Large Language Models Weijia Shi, Anirudh Ajith, Mengzhou Xia, Yangsibo Huang, Daogao Liu, Terra Blevins, Danqi Chen, Luke Zettlemoyer

NeurIPS 2024 Faster Algorithms for User-Level Private Stochastic Convex Optimization Andrew Lowy, Daogao Liu, Hilal Asi

ICML 2024 Private Gradient Descent for Linear Regression: Tighter Error Bounds and Instance-Specific Uncertainty Estimation Gavin R Brown, Krishnamurthy Dj Dvijotham, Georgina Evans, Daogao Liu, Adam Smith, Abhradeep Guha Thakurta

NeurIPS 2024 Private Online Learning via Lazy Algorithms Hilal Asi, Tomer Koren, Daogao Liu, Kunal Talwar

NeurIPS 2024 Private Stochastic Convex Optimization with Heavy Tails: Near-Optimality from Simple Reductions Hilal Asi, Daogao Liu, Kevin Tian

AISTATS 2024 User-Level Differentially Private Stochastic Convex Optimization: Efficient Algorithms with Optimal Rates Daogao Liu, Hilal Asi

COLT 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

ICLR 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

NeurIPSW 2023 Detecting Pretraining Data from Large Language Models Weijia Shi, Anirudh Ajith, Mengzhou Xia, Yangsibo Huang, Daogao Liu, Terra Blevins, Danqi Chen, Luke Zettlemoyer

NeurIPS 2023 Private (Stochastic) Non-Convex Optimization Revisited: Second-Order Stationary Points and Excess Risks Daogao Liu, Arun Ganesh, Sewoong Oh, Abhradeep Guha Thakurta

COLT 2022 Better Private Algorithms for Correlation Clustering Daogao Liu

COLT 2022 Private Convex Optimization via Exponential Mechanism Sivakanth Gopi, Yin Tat Lee, Daogao Liu

NeurIPS 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

NeurIPS 2021 Private Non-Smooth ERM and SCO in Subquadratic Steps Janardhan Kulkarni, Yin Tat Lee, Daogao Liu