More Efficient Algorithms for Stochastic Diameter and Some Unapproximated Problems in Metric Space. - Daogao Liu (COCOON, 2019)

Algorithms and Adaptivity Gaps for Stochastic k-TSP. - Haotian Jiang; Jian Li 015; Daogao Liu; Sahil Singla 001 (ITCS, 2020)

A robust multi-dimensional sparse Fourier transform in the continuous setting. - Yaonan Jin; Daogao Liu; Zhao Song 002 (CoRR, 2020)

Private Non-smooth ERM and SCO in Subquadratic Steps. - Janardhan Kulkarni; Yin Tat Lee; Daogao Liu (NeurIPS, 2021)

Private Non-smooth Empirical Risk Minimization and Stochastic Convex Optimization in Subquadratic Steps. - Janardhan Kulkarni; Yin Tat Lee; Daogao Liu (CoRR, 2021)

Curse of Dimensionality in Unconstrained Private Convex ERM. - Daogao Liu; Zhou Lu (CoRR, 2021)

The Convergence Rate of SGD's Final Iterate: Analysis on Dimension Dependence. - Daogao Liu; Zhou Lu (CoRR, 2021)

Markov Game with Switching Costs. - Jian Li 015; Daogao Liu (CoRR, 2021)

Better Private Algorithms for Correlation Clustering. - Daogao Liu (COLT, 2022)

When Does Differentially Private Learning Not Suffer in High Dimensions? - Xuechen Li 005; Daogao Liu; Tatsunori B. Hashimoto; Huseyin A. Inan; Janardhan Kulkarni; Yin Tat Lee; Abhradeep Guha Thakurta (NeurIPS, 2022)

Multi-token Markov Game with Switching Costs. - Jian Li 015; Daogao Liu (SODA, 2022)

Pandora Box Problem with Nonobligatory Inspection: Hardness and Improved Approximation Algorithms. - Hu Fu 001; Jiawei Li; Daogao Liu (CoRR, 2022)

Algorithmic Aspects of the Log-Laplace Transform and a Non-Euclidean Proximal Sampler. - Sivakanth Gopi; Yin Tat Lee; Daogao Liu; Ruoqi Shen; Kevin Tian (COLT, 2023)

ReSQueing Parallel and Private Stochastic Convex Optimization. - Yair Carmon; Arun Jambulapati; Yujia Jin; Yin Tat Lee; Daogao Liu; Aaron Sidford; Kevin Tian (FOCS, 2023)

Augmentation with Projection: Towards an Effective and Efficient Data Augmentation Paradigm for Distillation. - Ziqi Wang 003; Yuexin Wu; Frederick Liu; Daogao Liu; Le Hou; Hongkun Yu 001; Jing Li 049; Heng Ji 001 (ICLR, 2023)

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

Private Convex Optimization in General Norms. - Sivakanth Gopi; Yin Tat Lee; Daogao Liu; Ruoqi Shen; Kevin Tian (SODA, 2023)

Super-resolution and Robust Sparse Continuous Fourier Transform in Any Constant Dimension: Nearly Linear Time and Sample Complexity. - Yaonan Jin; Daogao Liu; Zhao Song 002 (SODA, 2023)

Pandora Box Problem with Nonobligatory Inspection: Hardness and Approximation Scheme. - Hu Fu 001; Jiawei Li; Daogao Liu (STOC, 2023)

kNN-Adapter: Efficient Domain Adaptation for Black-Box Language Models. - Yangsibo Huang; Daogao Liu; Zexuan Zhong; Weijia Shi; Yin Tat Lee (CoRR, 2023)

Learning across Data Owners with Joint Differential Privacy. - Yangsibo Huang; Haotian Jiang; Daogao Liu; Mohammad Mahdian; Jieming Mao; Vahab Mirrokni (CoRR, 2023)

User-level Differentially Private Stochastic Convex Optimization: Efficient Algorithms with Optimal Rates. - Daogao Liu; Hilal Asi (AISTATS, 2024)

Detecting Pretraining Data from Large Language Models. - Weijia Shi; Anirudh Ajith; Mengzhou Xia; Yangsibo Huang; Daogao Liu; Terra Blevins; Danqi Chen 001; Luke Zettlemoyer (ICLR, 2024)

Private Gradient Descent for Linear Regression: Tighter Error Bounds and Instance-Specific Uncertainty Estimation. - Gavin Brown 003; Krishnamurthy Dj Dvijotham; Georgina Evans; Daogao Liu; Adam Smith 006; Abhradeep Guha Thakurta (ICML, 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 (NeurIPS, 2024)

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

Mind the Privacy Unit! User-Level Differential Privacy for Language Model Fine-Tuning. - Lynn Chua; Badih Ghazi; Yangsibo Huang; Pritish Kamath; Daogao Liu; Pasin Manurangsi; Amer Sinha; Chiyuan Zhang (CoRR, 2024)

Improved Sample Complexity for Private Nonsmooth Nonconvex Optimization. - Guy Kornowski; Daogao Liu; Kunal Talwar (CoRR, 2024)

Private Convex Optimization via Exponential Mechanism. - Sivakanth Gopi; Yin Tat Lee; Daogao Liu (J. Priv. Confidentiality, 2024)

Unlearn and Burn: Adversarial Machine Unlearning Requests Destroy Model Accuracy. - Yangsibo Huang; Daogao Liu; Lynn Chua; Badih Ghazi; Pritish Kamath; Ravi Kumar 001; Pasin Manurangsi; Milad Nasr; Amer Sinha; Chiyuan Zhang (ICLR, 2025)

Adaptive Batch Size for Privately Finding Second-Order Stationary Points. - 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 001; Ari Holtzman; Daogao Liu; Luke Zettlemoyer; Noah A. Smith; Chiyuan Zhang (ICLR, 2025)

Advancing Differentially Private Optimization: Efficiency, Utility, and Applications - Daogao Liu (2025)

Scaling Embedding Layers in Language Models. - Da Yu; Edith Cohen; Badih Ghazi; Yangsibo Huang; Pritish Kamath; Ravi Kumar 001; Daogao Liu; Chiyuan Zhang (CoRR, 2025)

Linear-Time User-Level DP-SCO via Robust Statistics. - Badih Ghazi; Ravi Kumar 001; Daogao Liu; Pasin Manurangsi (CoRR, 2025)

Private Geometric Median in Nearly-Linear Time. - Syamantak Kumar; Daogao Liu; Kevin Tian; Chutong Yang (CoRR, 2025)

Urania: Differentially Private Insights into AI Use. - Daogao Liu; Edith Cohen; Badih Ghazi; Peter Kairouz; Pritish Kamath; Alexander Knop; Ravi Kumar 001; Pasin Manurangsi; Adam Sealfon; Da Yu; Chiyuan Zhang (CoRR, 2025)

Differentially Private Bilevel Optimization: Efficient Algorithms with Near-Optimal Rates. - Andrew Lowy; Daogao Liu (CoRR, 2025)

FlexOlmo: Open Language Models for Flexible Data Use. - Weijia Shi; Akshita Bhagia; Kevin Farhat; Niklas Muennighoff; Pete Walsh; Jacob Morrison; Dustin Schwenk; Shayne Longpre; Jake Poznanski; Allyson Ettinger; Daogao Liu; Margaret Li; et al. (CoRR, 2025)