Lingxiao Wang - RankMe

26 papers A* 9A 5Journal 8Unranked 3

Year	Rank	Type	Title / Venue / Authors
2024	J	`jnl`	Challenges of COVID-19 Case Forecasting in the US, 2020-2021. PLoS Comput. Biol. 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, Jade Xiao, Johannes Bracher, Alvaro J. Castro Rivadeneira, Aaron Gerding, Tilmann Gneiting, Yuxin Huang, Dasuni Jayawardena, Abdul H. Kanji, Khoa Le, Anja Mühlemann, Jarad Niemi, Evan L. Ray, Ariane Stark, Yijin Wang, Nutcha Wattanachit, Martha W. Zorn, Sen Pei, Jeffrey Shaman, Teresa K. Yamana, Samuel R. Tarasewicz, Daniel J. Wilson, Sid Baccam, Heidi Gurung, Steve Stage, Brad Suchoski, Lei Gao, Zhiling Gu, Myungjin Kim, Xinyi Li, Guannan Wang, Lily Wang, Yueying Wang, Shan Yu, Lauren Gardner, Sonia Jindal, Maximilian Marshall, Kristen Nixon, Juan Dent, Alison L. Hill, Joshua Kaminsky, Elizabeth C. Lee, Joseph Chadi Lemaitre, Justin Lessler, Claire P. Smith, Shaun Truelove, Matt Kinsey, Luke C. Mullany, Kaitlin Rainwater-Lovett, Lauren Shin, Katharine Tallaksen, Shelby Wilson, Dean Karlen, Lauren A. Castro, Geoffrey Fairchild, Isaac Michaud, Dave Osthus, Jiang Bian, Wei Cao, Zhifeng Gao, Juan M. Lavista Ferres, Chaozhuo Li, Tie-Yan Liu, Xing Xie, Shun Zhang, Shun Zheng, Matteo Chinazzi, Jessica T. Davis, Kunpeng Mu, Ana L. Pastore y Piontti, Alessandro Vespignani, Xinyue Xiong, Robert Walraven, Jinghui Chen, Quanquan Gu, Lingxiao Wang, Pan Xu, Weitong Zhang, Difan Zou, Graham Casey Gibson, Daniel Sheldon, Ajitesh Srivastava, Aniruddha Adiga, Benjamin Hurt, Gursharn Kaur, Bryan Lewis, Madhav V. Marathe, Akhil Sai Peddireddy, Przemyslaw J. Porebski, Srinivasan Venkatramanan, Lijing Wang, Pragati V. Prasad, Jo W. Walker, Alexander E. Webber, Rachel B. Slayton, Matthew Biggerstaff, Nicholas G. Reich, Michael A. Johansson
2023	A	`conf`	Efficient Privacy-Preserving Stochastic Nonconvex Optimization. UAI Lingxiao Wang, Bargav Jayaraman, David Evans, Quanquan Gu
2021	J	`jnl`	Adaptive Differentially Private Empirical Risk Minimization. CoRR Xiaoxia Wu, Lingxiao Wang, Irina Cristali, Quanquan Gu, Rebecca Willett
2021	J	`jnl`	Revisiting Membership Inference Under Realistic Assumptions. Proc. Priv. Enhancing Technol. Bargav Jayaraman, Lingxiao Wang, Katherine Knipmeyer, Quanquan Gu, David Evans
2021	—	`—`	Towards Efficient and Effective Privacy-Preserving Machine Learning. Lingxiao Wang
2021	—	`conf`	Variance-reduced First-order Meta-learning for Natural Language Processing Tasks. NAACL-HLT Lingxiao Wang, Kevin Huang, Tengyu Ma, Quanquan Gu, Jing Huang
2020	A*	`conf`	A Knowledge Transfer Framework for Differentially Private Sparse Learning. AAAI Lingxiao Wang, Quanquan Gu
2020	—	`conf`	DP-LSSGD: A Stochastic Optimization Method to Lift the Utility in Privacy-Preserving ERM. MSML Bao Wang, Quanquan Gu, March Boedihardjo, Lingxiao Wang, Farzin Barekat, Stanley J. Osher
2020	A*	`conf`	Improving Neural Language Generation with Spectrum Control. ICLR Lingxiao Wang, Jing Huang, Kevin Huang, Ziniu Hu, Guangtao Wang, Quanquan Gu
2020	—	`conf`	Is neuron coverage a meaningful measure for testing deep neural networks? ESEC/SIGSOFT FSE Fabrice Harel-Canada, Lingxiao Wang, Muhammad Ali Gulzar, Quanquan Gu, Miryung Kim
2020	J	`jnl`	Revisiting Membership Inference Under Realistic Assumptions. CoRR Bargav Jayaraman, Lingxiao Wang, David Evans, Quanquan Gu
2019	J	`jnl`	A Knowledge Transfer Framework for Differentially Private Sparse Learning. CoRR Lingxiao Wang, Quanquan Gu
2019	A*	`conf`	Differentially Private Iterative Gradient Hard Thresholding for Sparse Learning. IJCAI Lingxiao Wang, Quanquan Gu
2019	J	`jnl`	Efficient Privacy-Preserving Nonconvex Optimization. CoRR Lingxiao Wang, Bargav Jayaraman, David Evans, Quanquan Gu
2019	A	`conf`	Learning One-hidden-layer ReLU Networks via Gradient Descent. AISTATS Xiao Zhang, Yaodong Yu, Lingxiao Wang, Quanquan Gu
2018	A*	`conf`	A Primal-Dual Analysis of Global Optimality in Nonconvex Low-Rank Matrix Recovery. ICML Xiao Zhang, Lingxiao Wang, Yaodong Yu, Quanquan Gu
2018	A	`conf`	A Unified Framework for Nonconvex Low-Rank plus Sparse Matrix Recovery. AISTATS Xiao Zhang, Lingxiao Wang, Quanquan Gu
2018	A*	`conf`	Covariate Adjusted Precision Matrix Estimation via Nonconvex Optimization. ICML Jinghui Chen, Pan Xu, Lingxiao Wang, Jian Ma, Quanquan Gu
2018	A*	`conf`	Distributed Learning without Distress: Privacy-Preserving Empirical Risk Minimization. NeurIPS Bargav Jayaraman, Lingxiao Wang, David Evans, Quanquan Gu
2018	J	`jnl`	Learning One-hidden-layer ReLU Networks via Gradient Descent. CoRR Xiao Zhang, Yaodong Yu, Lingxiao Wang, Quanquan Gu
2017	A	`conf`	A Unified Computational and Statistical Framework for Nonconvex Low-rank Matrix Estimation. AISTATS Lingxiao Wang, Xiao Zhang, Quanquan Gu
2017	A*	`conf`	A Unified Variance Reduction-Based Framework for Nonconvex Low-Rank Matrix Recovery. ICML Lingxiao Wang, Xiao Zhang, Quanquan Gu
2017	A*	`conf`	High-Dimensional Variance-Reduced Stochastic Gradient Expectation-Maximization Algorithm. ICML Rongda Zhu, Lingxiao Wang, ChengXiang Zhai, Quanquan Gu
2017	A*	`conf`	Robust Gaussian Graphical Model Estimation with Arbitrary Corruption. ICML Lingxiao Wang, Quanquan Gu
2017	J	`jnl`	Robust Wirtinger Flow for Phase Retrieval with Arbitrary Corruption. CoRR Jinghui Chen, Lingxiao Wang, Xiao Zhang, Quanquan Gu
2016	A	`conf`	Precision Matrix Estimation in High Dimensional Gaussian Graphical Models with Faster Rates. AISTATS Lingxiao Wang, Xiang Ren, Quanquan Gu