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ORCIDLingxiao Wang 0001
Person information
- affiliation: Toyota Technological Institute at Chicago, IL, USA
- affiliation: University of California, Los Angeles, UCLA, Department of Computer Science, LA, USA
- affiliation: University of Virginia, Department of Computer Science, Charlottesville, VA, USA
Other persons with the same name
- Lingxiao Wang — disambiguation page
- Lingxiao Wang 0002 — Grenoble Alpes University, France
- Lingxiao Wang 0003
![[0000-0002-1654-4681]](https://dblp.uni-trier.de./img/orcid-mark.12x12.png "0000-0002-1654-4681")
— Northwestern University, Department of Industrial Engineering and Management Sciences, Evanston, IL, USA - Lingxiao Wang 0004 — Auburn University, Department of Electrical and Computer Engineering, AL, USA
- Lingxiao Wang 0005 — Embry-Riddle Aeronautical University, Department of Electrical Engineering and Computer Science, Daytona Beach, FL, USA
- Lingxiao Wang 0006 — Frankfurt Institute for Advanced Studies, Xidian-FIAS International Joint Research Center, Frankfurt am Main, Germany (and 1 more)
- Lingxiao Wang 0007 — Henan Medical College, Department of Pathology, China
- Lingxiao Wang 0008 — Ludwig-Maximilians-Universität München, Department of Geography, Munich, Germany (and 1 more)
- Lingxiao Wang 0009 — Tsinghua University, Department of Electronic Engineering, Beijing, China
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2020 – today
- 2023
[c17]Lingxiao Wang, Bargav Jayaraman, David Evans, Quanquan Gu:
Efficient Privacy-Preserving Stochastic Nonconvex Optimization. UAI 2023: 2203-2213- 2021
- [b1]Lingxiao Wang:
Towards Efficient and Effective Privacy-Preserving Machine Learning. University of California, Los Angeles, USA, 2021 - [j1]Bargav Jayaraman, Lingxiao Wang, Katherine Knipmeyer, Quanquan Gu, David Evans:
Revisiting Membership Inference Under Realistic Assumptions. Proc. Priv. Enhancing Technol. 2021(2): 348-368 (2021) - [c16]Lingxiao Wang, Kevin Huang, Tengyu Ma, Quanquan Gu, Jing Huang:
Variance-reduced First-order Meta-learning for Natural Language Processing Tasks. NAACL-HLT 2021: 2609-2615 - [i6]Xiaoxia Wu, Lingxiao Wang, Irina Cristali, Quanquan Gu, Rebecca Willett:
Adaptive Differentially Private Empirical Risk Minimization. CoRR abs/2110.07435 (2021) - 2020
- [c15]Lingxiao Wang, Quanquan Gu:
A Knowledge Transfer Framework for Differentially Private Sparse Learning. AAAI 2020: 6235-6242 - [c14]Lingxiao Wang, Jing Huang, Kevin Huang, Ziniu Hu, Guangtao Wang, Quanquan Gu:
Improving Neural Language Generation with Spectrum Control. ICLR 2020 - [c13]Bao Wang, Quanquan Gu, March Boedihardjo, Lingxiao Wang, Farzin Barekat, Stanley J. Osher:
DP-LSSGD: A Stochastic Optimization Method to Lift the Utility in Privacy-Preserving ERM. MSML 2020: 328-351 - [c12]Fabrice Harel-Canada, Lingxiao Wang, Muhammad Ali Gulzar, Quanquan Gu, Miryung Kim:
Is neuron coverage a meaningful measure for testing deep neural networks? ESEC/SIGSOFT FSE 2020: 851-862 - [i5]Bargav Jayaraman, Lingxiao Wang, David Evans, Quanquan Gu:
Revisiting Membership Inference Under Realistic Assumptions. CoRR abs/2005.10881 (2020)
2010 – 2019
- 2019
- [c11]Xiao Zhang, Yaodong Yu, Lingxiao Wang, Quanquan Gu:
Learning One-hidden-layer ReLU Networks via Gradient Descent. AISTATS 2019: 1524-1534 - [c10]Lingxiao Wang, Quanquan Gu:
Differentially Private Iterative Gradient Hard Thresholding for Sparse Learning. IJCAI 2019: 3740-3747 - [i4]Lingxiao Wang, Quanquan Gu:
A Knowledge Transfer Framework for Differentially Private Sparse Learning. CoRR abs/1909.06322 (2019) - [i3]Lingxiao Wang, Bargav Jayaraman, David Evans, Quanquan Gu:
Efficient Privacy-Preserving Nonconvex Optimization. CoRR abs/1910.13659 (2019) - 2018
- [c9]Xiao Zhang, Lingxiao Wang, Quanquan Gu:
A Unified Framework for Nonconvex Low-Rank plus Sparse Matrix Recovery. AISTATS 2018: 1097-1107 - [c8]Jinghui Chen, Pan Xu, Lingxiao Wang, Jian Ma, Quanquan Gu:
Covariate Adjusted Precision Matrix Estimation via Nonconvex Optimization. ICML 2018: 921-930 - [c7]Xiao Zhang, Lingxiao Wang, Yaodong Yu, Quanquan Gu:
A Primal-Dual Analysis of Global Optimality in Nonconvex Low-Rank Matrix Recovery. ICML 2018: 5857-5866 - [c6]Bargav Jayaraman, Lingxiao Wang, David Evans, Quanquan Gu:
Distributed Learning without Distress: Privacy-Preserving Empirical Risk Minimization. NeurIPS 2018: 6346-6357 - [i2]Xiao Zhang, Yaodong Yu, Lingxiao Wang, Quanquan Gu:
Learning One-hidden-layer ReLU Networks via Gradient Descent. CoRR abs/1806.07808 (2018) - 2017
- [c5]Lingxiao Wang, Xiao Zhang, Quanquan Gu:
A Unified Computational and Statistical Framework for Nonconvex Low-rank Matrix Estimation. AISTATS 2017: 981-990 - [c4]Lingxiao Wang, Quanquan Gu:
Robust Gaussian Graphical Model Estimation with Arbitrary Corruption. ICML 2017: 3617-3626 - [c3]Lingxiao Wang, Xiao Zhang, Quanquan Gu:
A Unified Variance Reduction-Based Framework for Nonconvex Low-Rank Matrix Recovery. ICML 2017: 3712-3721 - [c2]Rongda Zhu, Lingxiao Wang, Chengxiang Zhai, Quanquan Gu:
High-Dimensional Variance-Reduced Stochastic Gradient Expectation-Maximization Algorithm. ICML 2017: 4180-4188 - [i1]Jinghui Chen, Lingxiao Wang, Xiao Zhang, Quanquan Gu:
Robust Wirtinger Flow for Phase Retrieval with Arbitrary Corruption. CoRR abs/1704.06256 (2017) - 2016
- [c1]Lingxiao Wang, Xiang Ren, Quanquan Gu:
Precision Matrix Estimation in High Dimensional Gaussian Graphical Models with Faster Rates. AISTATS 2016: 177-185
Coauthor Index
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