Precision Matrix Estimation in High Dimensional Gaussian Graphical Models with Faster Rates. - Lingxiao Wang 001; Xiang Ren 001; Quanquan Gu (AISTATS, 2016)

A Unified Computational and Statistical Framework for Nonconvex Low-rank Matrix Estimation. - Lingxiao Wang 001; Xiao Zhang 016; Quanquan Gu (AISTATS, 2017)

Robust Gaussian Graphical Model Estimation with Arbitrary Corruption. - Lingxiao Wang 001; Quanquan Gu (ICML, 2017)

A Unified Variance Reduction-Based Framework for Nonconvex Low-Rank Matrix Recovery. - Lingxiao Wang 001; Xiao Zhang 016; Quanquan Gu (ICML, 2017)

High-Dimensional Variance-Reduced Stochastic Gradient Expectation-Maximization Algorithm. - Rongda Zhu; Lingxiao Wang 001; Chengxiang Zhai; Quanquan Gu (ICML, 2017)

Robust Wirtinger Flow for Phase Retrieval with Arbitrary Corruption. - Jinghui Chen; Lingxiao Wang 001; Xiao Zhang 016; Quanquan Gu (CoRR, 2017)

A Unified Framework for Nonconvex Low-Rank plus Sparse Matrix Recovery. - Xiao Zhang 016; Lingxiao Wang 001; Quanquan Gu (AISTATS, 2018)

Covariate Adjusted Precision Matrix Estimation via Nonconvex Optimization. - Jinghui Chen; Pan Xu 002; Lingxiao Wang 001; Jian Ma 004; Quanquan Gu (ICML, 2018)

A Primal-Dual Analysis of Global Optimality in Nonconvex Low-Rank Matrix Recovery. - Xiao Zhang 016; Lingxiao Wang 001; Yaodong Yu; Quanquan Gu (ICML, 2018)

Distributed Learning without Distress: Privacy-Preserving Empirical Risk Minimization. - Bargav Jayaraman; Lingxiao Wang 001; David Evans 001; Quanquan Gu (NeurIPS, 2018)

Learning One-hidden-layer ReLU Networks via Gradient Descent. - Xiao Zhang 016; Yaodong Yu; Lingxiao Wang 001; Quanquan Gu (AISTATS, 2019)

Differentially Private Iterative Gradient Hard Thresholding for Sparse Learning. - Lingxiao Wang 001; Quanquan Gu (IJCAI, 2019)

Efficient Privacy-Preserving Nonconvex Optimization. - Lingxiao Wang 001; Bargav Jayaraman; David Evans 001; Quanquan Gu (CoRR, 2019)

A Knowledge Transfer Framework for Differentially Private Sparse Learning. - Lingxiao Wang 001; Quanquan Gu (AAAI, 2020)

Improving Neural Language Generation with Spectrum Control. - Lingxiao Wang 001; Jing Huang 019; Kevin Huang 002; Ziniu Hu; Guangtao Wang; Quanquan Gu (ICLR, 2020)

DP-LSSGD: A Stochastic Optimization Method to Lift the Utility in Privacy-Preserving ERM. - Bao Wang 001; Quanquan Gu; March Boedihardjo; Lingxiao Wang 001; Farzin Barekat; Stanley J. Osher (MSML, 2020)

Is neuron coverage a meaningful measure for testing deep neural networks? - Fabrice Harel-Canada; Lingxiao Wang 001; Muhammad Ali Gulzar; Quanquan Gu; Miryung Kim (ESEC/SIGSOFT FSE, 2020)

Revisiting Membership Inference Under Realistic Assumptions. - Bargav Jayaraman; Lingxiao Wang 001; David Evans 001; Quanquan Gu (CoRR, 2020)

Variance-reduced First-order Meta-learning for Natural Language Processing Tasks. - Lingxiao Wang 001; Kevin Huang 002; Tengyu Ma 001; Quanquan Gu; Jing Huang 019 (NAACL-HLT, 2021)

Adaptive Differentially Private Empirical Risk Minimization. - Xiaoxia Wu; Lingxiao Wang 001; Irina Cristali; Quanquan Gu; Rebecca Willett (CoRR, 2021)

Revisiting Membership Inference Under Realistic Assumptions. - Bargav Jayaraman; Lingxiao Wang 001; Katherine Knipmeyer; Quanquan Gu; David Evans 001 (Proc. Priv. Enhancing Technol., 2021)

Towards Efficient and Effective Privacy-Preserving Machine Learning. - Lingxiao Wang 001 (2021)

Efficient Privacy-Preserving Stochastic Nonconvex Optimization. - Lingxiao Wang 001; Bargav Jayaraman; David Evans 001; Quanquan Gu (UAI, 2023)

Challenges of COVID-19 Case Forecasting in the US, 2020-2021. - Velma K. Lopez; Estee Y. Cramer; Robert Pagano; John M. Drake; Eamon B. O'Dea; Madeline Adee; Turgay Ayer; Jagpreet Chhatwal; Ozden O. Dalgic; Mary A. Ladd; Benjamin P. Linas; Peter P. Mueller; et al. (PLoS Comput. Biol., 2024)