Lingxiao Wang

Affiliations:

Toyota Technological Institute at Chicago, IL, USA
University of California, Los Angeles, UCLA, Department of Computer Science, LA, USA
University of Virginia, Department of Computer Science, Charlottesville, VA, USA

According to our database¹, Lingxiao Wang authored at least 23 papers between 2016 and 2023.

Collaborative distances:

Dijkstra number² of four.
Erdős number³ of four.

Timeline

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In proceedings

Article

PhD thesis

Dataset

Other

Bibliography

2023

Efficient Privacy-Preserving Stochastic Nonconvex Optimization.

[BibT_eX]

[DOI]

Proceedings of the Uncertainty in Artificial Intelligence, 2023

2021

Towards Efficient and Effective Privacy-Preserving Machine Learning.

[BibT_eX]

[DOI]

Lingxiao Wang

PhD thesis, 2021

Revisiting Membership Inference Under Realistic Assumptions.

[BibT_eX]

[DOI]

Proc. Priv. Enhancing Technol., 2021

Adaptive Differentially Private Empirical Risk Minimization.

[BibT_eX]

[DOI]

CoRR, 2021

Variance-reduced First-order Meta-learning for Natural Language Processing Tasks.

[BibT_eX]

[DOI]

Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2021

2020

Revisiting Membership Inference Under Realistic Assumptions.

[BibT_eX]

[DOI]

CoRR, 2020

Is neuron coverage a meaningful measure for testing deep neural networks?

[BibT_eX]

[DOI]

Proceedings of the ESEC/FSE '20: 28th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering, 2020

DP-LSSGD: A Stochastic Optimization Method to Lift the Utility in Privacy-Preserving ERM.

[BibT_eX]

[DOI]

Proceedings of Mathematical and Scientific Machine Learning, 2020

Improving Neural Language Generation with Spectrum Control.

[BibT_eX]

[DOI]

Proceedings of the 8th International Conference on Learning Representations, 2020

A Knowledge Transfer Framework for Differentially Private Sparse Learning.

[BibT_eX]

[DOI]

Lingxiao Wang

Quanquan Gu

Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

2019

Efficient Privacy-Preserving Nonconvex Optimization.

[BibT_eX]

[DOI]

CoRR, 2019

Differentially Private Iterative Gradient Hard Thresholding for Sparse Learning.

[BibT_eX]

[DOI]

Lingxiao Wang

Quanquan Gu

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, 2019

Learning One-hidden-layer ReLU Networks via Gradient Descent.

[BibT_eX]

[DOI]

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

2018

Distributed Learning without Distress: Privacy-Preserving Empirical Risk Minimization.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

A Primal-Dual Analysis of Global Optimality in Nonconvex Low-Rank Matrix Recovery.

[BibT_eX]

[DOI]

Proceedings of the 35th International Conference on Machine Learning, 2018

Covariate Adjusted Precision Matrix Estimation via Nonconvex Optimization.

[BibT_eX]

[DOI]

Proceedings of the 35th International Conference on Machine Learning, 2018

A Unified Framework for Nonconvex Low-Rank plus Sparse Matrix Recovery.

[BibT_eX]

[DOI]

Xiao Zhang

Lingxiao Wang

Quanquan Gu

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2018

2017

Robust Wirtinger Flow for Phase Retrieval with Arbitrary Corruption.

[BibT_eX]

[DOI]

CoRR, 2017

High-Dimensional Variance-Reduced Stochastic Gradient Expectation-Maximization Algorithm.

[BibT_eX]

[DOI]

Proceedings of the 34th International Conference on Machine Learning, 2017

A Unified Variance Reduction-Based Framework for Nonconvex Low-Rank Matrix Recovery.

[BibT_eX]

[DOI]

Lingxiao Wang

Xiao Zhang

Quanquan Gu

Proceedings of the 34th International Conference on Machine Learning, 2017

Robust Gaussian Graphical Model Estimation with Arbitrary Corruption.

[BibT_eX]

[DOI]

Lingxiao Wang

Quanquan Gu

Proceedings of the 34th International Conference on Machine Learning, 2017

A Unified Computational and Statistical Framework for Nonconvex Low-rank Matrix Estimation.

[BibT_eX]

[DOI]

Lingxiao Wang

Xiao Zhang

Quanquan Gu

Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, 2017

2016

Precision Matrix Estimation in High Dimensional Gaussian Graphical Models with Faster Rates.

[BibT_eX]

[DOI]

Lingxiao Wang

Xiang Ren

Quanquan Gu

Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, 2016

Lingxiao Wang

Timeline

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