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ORCIDZheng Xu 0002
许正
Person information
- unicode name: 许正
- affiliation: Google Research
- affiliation (former, PhD 2019): University of Maryland, Department of Computer Science, College Park, MD, USA
Other persons with the same name
- Zheng Xu — disambiguation page
- Zheng Xu 0001
![[0000-0002-8362-5991]](https://dblp.dagstuhl.de/img/orcid-mark.12x12.png "0000-0002-8362-5991")
— The Third Research Institute of the Ministry of Public Security, Shanghai, China - Zheng Xu 0003 — Columbia University, Department of Electrical Engineering, New York, USA
- Zheng Xu 0004 — The University of Texas at Austin, TX, USA (and 1 more)
- Zheng Xu 0005 — The University of Texas at Arlington, USA
- Zheng Xu 0006 — Texas A&M University, College Station, TX
- Zheng Xu 0007 — Chongqing University, School of Electrical Engineering, China
- Zheng Xu 0008 — Rensselaer Polytechnic Institute, Troy, NY, USA
- Zheng Xu 0009 — University of Victoria, BC, Canada
- Zheng Xu 0010 — University of Nebraska-Lincoln, Statistics and Economics, NE, USA (and 2 more)
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2020 – today
- 2024
[j5]Dezhong Yao, Wanning Pan, Yutong Dai, Yao Wan, Xiaofeng Ding, Chen Yu, Hai Jin, Zheng Xu, Lichao Sun:
FedGKD: Toward Heterogeneous Federated Learning via Global Knowledge Distillation. IEEE Trans. Computers 73(1): 3-17 (2024)- [i40]Yae Jee Cho, Luyang Liu, Zheng Xu, Aldi Fahrezi, Gauri Joshi:
Heterogeneous Low-Rank Approximation for Federated Fine-tuning of On-Device Foundation Models. CoRR abs/2401.06432 (2024) - [i39]Da Yu, Peter Kairouz, Sewoong Oh, Zheng Xu:
Privacy-Preserving Instructions for Aligning Large Language Models. CoRR abs/2402.13659 (2024) - [i38]Jae Hun Ro, Srinadh Bhojanapalli, Zheng Xu, Yanxiang Zhang, Ananda Theertha Suresh:
Efficient Language Model Architectures for Differentially Private Federated Learning. CoRR abs/2403.08100 (2024) - 2023
- [j4]Natalia Ponomareva, Hussein Hazimeh, Alex Kurakin, Zheng Xu, Carson Denison, H. Brendan McMahan, Sergei Vassilvitskii, Steve Chien, Abhradeep Guha Thakurta:
How to DP-fy ML: A Practical Guide to Machine Learning with Differential Privacy. J. Artif. Intell. Res. 77: 1113-1201 (2023) - [c33]Zheng Xu, Yanxiang Zhang, Galen Andrew, Christopher A. Choquette-Choo, Peter Kairouz, H. Brendan McMahan, Jesse Rosenstock, Yuanbo Zhang:
Federated Learning of Gboard Language Models with Differential Privacy. ACL (industry) 2023: 629-639 - [c32]Zheng Xu, Maxwell D. Collins, Yuxiao Wang, Liviu Panait, Sewoong Oh, Sean Augenstein, Ting Liu, Florian Schroff, H. Brendan McMahan:
Learning to Generate Image Embeddings with User-Level Differential Privacy. CVPR 2023: 7969-7980 - [c31]Yae Jee Cho, Pranay Sharma, Gauri Joshi, Zheng Xu, Satyen Kale, Tong Zhang:
On the Convergence of Federated Averaging with Cyclic Client Participation. ICML 2023: 5677-5721 - [c30]Rudrajit Das, Satyen Kale, Zheng Xu, Tong Zhang, Sujay Sanghavi:
Beyond Uniform Lipschitz Condition in Differentially Private Optimization. ICML 2023: 7066-7101 - [c29]Natalia Ponomareva, Sergei Vassilvitskii, Zheng Xu, Brendan McMahan, Alexey Kurakin, Chiyaun Zhang:
How to DP-fy ML: A Practical Tutorial to Machine Learning with Differential Privacy. KDD 2023: 5823-5824 - [c28]Christopher A. Choquette-Choo, Arun Ganesh, Ryan McKenna, H. Brendan McMahan, John Rush, Abhradeep Guha Thakurta, Zheng Xu:
(Amplified) Banded Matrix Factorization: A unified approach to private training. NeurIPS 2023 - [i37]Yae Jee Cho, Pranay Sharma, Gauri Joshi, Zheng Xu, Satyen Kale, Tong Zhang:
On the Convergence of Federated Averaging with Cyclic Client Participation. CoRR abs/2302.03109 (2023) - [i36]Natalia Ponomareva, Hussein Hazimeh, Alex Kurakin, Zheng Xu, Carson Denison, H. Brendan McMahan, Sergei Vassilvitskii, Steve Chien, Abhradeep Thakurta:
How to DP-fy ML: A Practical Guide to Machine Learning with Differential Privacy. CoRR abs/2303.00654 (2023) - [i35]Alekh Agarwal, H. Brendan McMahan, Zheng Xu:
An Empirical Evaluation of Federated Contextual Bandit Algorithms. CoRR abs/2303.10218 (2023) - [i34]Boxin Wang, Jacky Yibo Zhang, Yuan Cao, Bo Li, H. Brendan McMahan, Sewoong Oh, Zheng Xu, Manzil Zaheer:
Can Public Large Language Models Help Private Cross-device Federated Learning? CoRR abs/2305.12132 (2023) - [i33]Zheng Xu, Yanxiang Zhang, Galen Andrew, Christopher A. Choquette-Choo, Peter Kairouz, H. Brendan McMahan, Jesse Rosenstock, Yuanbo Zhang:
Federated Learning of Gboard Language Models with Differential Privacy. CoRR abs/2305.18465 (2023) - [i32]Christopher A. Choquette-Choo, Arun Ganesh, Ryan McKenna, H. Brendan McMahan, Keith Rush, Abhradeep Guha Thakurta, Zheng Xu:
(Amplified) Banded Matrix Factorization: A unified approach to private training. CoRR abs/2306.08153 (2023) - [i31]Yuanbo Zhang, Daniel Ramage, Zheng Xu, Yanxiang Zhang, Shumin Zhai, Peter Kairouz:
Private Federated Learning in Gboard. CoRR abs/2306.14793 (2023) - [i30]Nikhil Kandpal, Krishna Pillutla, Alina Oprea, Peter Kairouz, Christopher A. Choquette-Choo, Zheng Xu:
User Inference Attacks on Large Language Models. CoRR abs/2310.09266 (2023) - 2022
- [c27]Chen Zhu, Zheng Xu, Mingqing Chen, Jakub Konecný, Andrew Hard, Tom Goldstein:
Diurnal or Nocturnal? Federated Learning of Multi-branch Networks from Periodically Shifting Distributions. ICLR 2022 - [i29]Jianyu Wang, Rudrajit Das, Gauri Joshi, Satyen Kale, Zheng Xu, Tong Zhang:
On the Unreasonable Effectiveness of Federated Averaging with Heterogeneous Data. CoRR abs/2206.04723 (2022) - [i28]Shanshan Wu, Tian Li, Zachary Charles, Yu Xiao, Ken Ziyu Liu, Zheng Xu, Virginia Smith:
Motley: Benchmarking Heterogeneity and Personalization in Federated Learning. CoRR abs/2206.09262 (2022) - [i27]Rudrajit Das, Satyen Kale, Zheng Xu, Tong Zhang, Sujay Sanghavi:
Beyond Uniform Lipschitz Condition in Differentially Private Optimization. CoRR abs/2206.10713 (2022) - [i26]Zheng Xu, Maxwell D. Collins, Yuxiao Wang, Liviu Panait, Sewoong Oh, Sean Augenstein, Ting Liu, Florian Schroff, H. Brendan McMahan:
Learning to Generate Image Embeddings with User-level Differential Privacy. CoRR abs/2211.10844 (2022) - 2021
- [j3]Peter Kairouz, H. Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Kallista A. Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, Rafael G. L. D'Oliveira, Hubert Eichner, Salim El Rouayheb, David Evans, Josh Gardner, Zachary Garrett, Adrià Gascón, Badih Ghazi, Phillip B. Gibbons, Marco Gruteser, Zaïd Harchaoui, Chaoyang He, Lie He, Zhouyuan Huo, Ben Hutchinson, Justin Hsu, Martin Jaggi, Tara Javidi, Gauri Joshi, Mikhail Khodak, Jakub Konecný, Aleksandra Korolova, Farinaz Koushanfar, Sanmi Koyejo, Tancrède Lepoint, Yang Liu, Prateek Mittal, Mehryar Mohri, Richard Nock, Ayfer Özgür, Rasmus Pagh, Hang Qi, Daniel Ramage, Ramesh Raskar, Mariana Raykova, Dawn Song, Weikang Song, Sebastian U. Stich, Ziteng Sun, Ananda Theertha Suresh, Florian Tramèr, Praneeth Vepakomma, Jianyu Wang, Li Xiong, Zheng Xu, Qiang Yang, Felix X. Yu, Han Yu, Sen Zhao:
Advances and Open Problems in Federated Learning. Found. Trends Mach. Learn. 14(1-2): 1-210 (2021) - [c26]Peter Kairouz, Brendan McMahan, Shuang Song, Om Thakkar, Abhradeep Thakurta, Zheng Xu:
Practical and Private (Deep) Learning Without Sampling or Shuffling. ICML 2021: 5213-5225 - [c25]Chen Zhu, Renkun Ni, Zheng Xu, Kezhi Kong, W. Ronny Huang, Tom Goldstein:
GradInit: Learning to Initialize Neural Networks for Stable and Efficient Training. NeurIPS 2021: 16410-16422 - [i25]Chen Zhu, Renkun Ni, Zheng Xu, Kezhi Kong, W. Ronny Huang, Tom Goldstein:
GradInit: Learning to Initialize Neural Networks for Stable and Efficient Training. CoRR abs/2102.08098 (2021) - [i24]Peter Kairouz, Brendan McMahan, Shuang Song, Om Thakkar, Abhradeep Thakurta, Zheng Xu:
Practical and Private (Deep) Learning without Sampling or Shuffling. CoRR abs/2103.00039 (2021) - [i23]Jianyu Wang, Zheng Xu, Zachary Garrett, Zachary Charles, Luyang Liu, Gauri Joshi:
Local Adaptivity in Federated Learning: Convergence and Consistency. CoRR abs/2106.02305 (2021) - [i22]Jianyu Wang, Zachary Charles, Zheng Xu, Gauri Joshi, H. Brendan McMahan, Blaise Agüera y Arcas, Maruan Al-Shedivat, Galen Andrew, Salman Avestimehr, Katharine Daly, Deepesh Data, Suhas N. Diggavi, Hubert Eichner, Advait Gadhikar, Zachary Garrett, Antonious M. Girgis, Filip Hanzely, Andrew Hard, Chaoyang He, Samuel Horváth, Zhouyuan Huo, Alex Ingerman, Martin Jaggi, Tara Javidi, Peter Kairouz, Satyen Kale, Sai Praneeth Karimireddy, Jakub Konecný, Sanmi Koyejo, Tian Li, Luyang Liu, Mehryar Mohri, Hang Qi, Sashank J. Reddi, Peter Richtárik, Karan Singhal, Virginia Smith, Mahdi Soltanolkotabi, Weikang Song, Ananda Theertha Suresh, Sebastian U. Stich, Ameet Talwalkar, Hongyi Wang, Blake E. Woodworth, Shanshan Wu, Felix X. Yu, Honglin Yuan, Manzil Zaheer, Mi Zhang, Tong Zhang, Chunxiang Zheng, Chen Zhu, Wennan Zhu:
A Field Guide to Federated Optimization. CoRR abs/2107.06917 (2021) - [i21]Hakim Sidahmed, Zheng Xu, Ankush Garg, Yuan Cao, Mingqing Chen:
Efficient and Private Federated Learning with Partially Trainable Networks. CoRR abs/2110.03450 (2021) - 2020
- [j2]Zheng Xu, Michael J. Wilber, Chen Fang, Aaron Hertzmann, Hailin Jin:
Adversarial training for fast arbitrary style transfer. Comput. Graph. 87: 1-11 (2020) - [c24]Ali Shafahi, Mahyar Najibi, Zheng Xu, John P. Dickerson, Larry S. Davis, Tom Goldstein:
Universal Adversarial Training. AAAI 2020: 5636-5643 - [c23]Karthik Abinav Sankararaman, Soham De, Zheng Xu, W. Ronny Huang, Tom Goldstein:
The Impact of Neural Network Overparameterization on Gradient Confusion and Stochastic Gradient Descent. ICML 2020: 8469-8479 - [i20]Zheng Xu, Ali Shafahi, Tom Goldstein:
Exploring Model Robustness with Adaptive Networks and Improved Adversarial Training. CoRR abs/2006.00387 (2020) - [i19]Chen Zhu, Zheng Xu, Ali Shafahi, Manli Shu, Amin Ghiasi, Tom Goldstein:
Towards Accurate Quantization and Pruning via Data-free Knowledge Transfer. CoRR abs/2010.07334 (2020)
2010 – 2019
- 2019
- [b1]Zheng Xu:
Alternating Optimization: Constrained Problems, Adversarial Networks, and Robust Models. University of Maryland, College Park, MD, USA, 2019 - [c22]Zheng Xu, Michael J. Wilber, Chen Fang, Aaron Hertzmann, Hailin Jin:
Learning from Multi-domain Artistic Images for Arbitrary Style Transfer. Expressive 2019: 21-31 - [c21]Ali Shafahi, Mahyar Najibi, Amin Ghiasi, Zheng Xu, John P. Dickerson, Christoph Studer, Larry S. Davis, Gavin Taylor, Tom Goldstein:
Adversarial training for free! NeurIPS 2019: 3353-3364 - [i18]Karthik Abinav Sankararaman, Soham De, Zheng Xu, W. Ronny Huang, Tom Goldstein:
The Impact of Neural Network Overparameterization on Gradient Confusion and Stochastic Gradient Descent. CoRR abs/1904.06963 (2019) - [i17]Ali Shafahi, Mahyar Najibi, Amin Ghiasi, Zheng Xu, John P. Dickerson, Christoph Studer, Larry S. Davis, Gavin Taylor, Tom Goldstein:
Adversarial Training for Free! CoRR abs/1904.12843 (2019) - [i16]Peter Kairouz, H. Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Kallista A. Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, Rafael G. L. D'Oliveira, Salim El Rouayheb, David Evans, Josh Gardner, Zachary Garrett, Adrià Gascón, Badih Ghazi, Phillip B. Gibbons, Marco Gruteser, Zaïd Harchaoui, Chaoyang He, Lie He, Zhouyuan Huo, Ben Hutchinson, Justin Hsu, Martin Jaggi, Tara Javidi, Gauri Joshi, Mikhail Khodak, Jakub Konecný, Aleksandra Korolova, Farinaz Koushanfar, Sanmi Koyejo, Tancrède Lepoint, Yang Liu, Prateek Mittal, Mehryar Mohri, Richard Nock, Ayfer Özgür, Rasmus Pagh, Mariana Raykova, Hang Qi, Daniel Ramage, Ramesh Raskar, Dawn Song, Weikang Song, Sebastian U. Stich, Ziteng Sun, Ananda Theertha Suresh, Florian Tramèr, Praneeth Vepakomma, Jianyu Wang, Li Xiong, Zheng Xu, Qiang Yang, Felix X. Yu, Han Yu, Sen Zhao:
Advances and Open Problems in Federated Learning. CoRR abs/1912.04977 (2019) - 2018
- [j1]Wen Li, Zheng Xu, Dong Xu, Dengxin Dai, Luc Van Gool:
Domain Generalization and Adaptation Using Low Rank Exemplar SVMs. IEEE Trans. Pattern Anal. Mach. Intell. 40(5): 1114-1127 (2018) - [c20]Xitong Yang, Zheng Xu, Jiebo Luo:
Towards Perceptual Image Dehazing by Physics-Based Disentanglement and Adversarial Training. AAAI 2018: 7485-7492 - [c19]Zheng Xu, Yen-Chang Hsu, Jiawei Huang:
Training Student Networks for Acceleration with Conditional Adversarial Networks. BMVC 2018: 61 - [c18]Zheng Xu, Xitong Yang, Xue Li, Xiaoshuai Sun:
Strong Baseline for Single Image Dehazing with Deep Features and Instance Normalization. BMVC 2018: 243 - [c17]Zheng Xu, Yen-Chang Hsu, Jiawei Huang:
Training Shallow and Thin Networks for Acceleration via Knowledge Distillation with Conditional Adversarial Networks. ICLR (Workshop) 2018 - [c16]Abhay Kumar Yadav, Sohil Shah, Zheng Xu, David W. Jacobs, Tom Goldstein:
Stabilizing Adversarial Nets with Prediction Methods. ICLR (Poster) 2018 - [c15]Yen-Chang Hsu, Zheng Xu, Zsolt Kira, Jiawei Huang:
Learning to Cluster for Proposal-Free Instance Segmentation. IJCNN 2018: 1-8 - [c14]Hao Li, Zheng Xu, Gavin Taylor, Christoph Studer, Tom Goldstein:
Visualizing the Loss Landscape of Neural Nets. NeurIPS 2018: 6391-6401 - [i15]Yen-Chang Hsu, Zheng Xu, Zsolt Kira, Jiawei Huang:
Learning to Cluster for Proposal-Free Instance Segmentation. CoRR abs/1803.06459 (2018) - [i14]Zheng Xu, Xitong Yang, Xue Li, Xiaoshuai Sun:
The Effectiveness of Instance Normalization: a Strong Baseline for Single Image Dehazing. CoRR abs/1805.03305 (2018) - [i13]Zheng Xu, Michael J. Wilber, Chen Fang, Aaron Hertzmann, Hailin Jin:
Beyond Textures: Learning from Multi-domain Artistic Images for Arbitrary Style Transfer. CoRR abs/1805.09987 (2018) - [i12]Ali Shafahi, Mahyar Najibi, Zheng Xu, John P. Dickerson, Larry S. Davis, Tom Goldstein:
Universal Adversarial Training. CoRR abs/1811.11304 (2018) - 2017
- [c13]Gavin Taylor, Zheng Xu, Tom Goldstein:
Scalable Classifiers with ADMM and Transpose Reduction. AAAI Workshops 2017 - [c12]Zheng Xu, Mário A. T. Figueiredo, Tom Goldstein:
Adaptive ADMM with Spectral Penalty Parameter Selection. AISTATS 2017: 718-727 - [c11]Zheng Xu, Mário A. T. Figueiredo, Xiaoming Yuan, Christoph Studer, Tom Goldstein:
Adaptive Relaxed ADMM: Convergence Theory and Practical Implementation. CVPR 2017: 7234-7243 - [c10]Zheng Xu, Gavin Taylor, Hao Li, Mário A. T. Figueiredo, Xiaoming Yuan, Tom Goldstein:
Adaptive Consensus ADMM for Distributed Optimization. ICML 2017: 3841-3850 - [c9]Hao Li, Soham De, Zheng Xu, Christoph Studer, Hanan Samet, Tom Goldstein:
Training Quantized Nets: A Deeper Understanding. NIPS 2017: 5811-5821 - [c8]Louiqa Raschid, Zheng Xu, Elena Zotkina:
Exploring Financial Relationships Using Probabilistic Topic Models (Demonstration Paper). DSMM@SIGMOD 2017: 9:1-9:5 - [i11]Zheng Xu, Mário A. T. Figueiredo, Xiaoming Yuan, Christoph Studer, Tom Goldstein:
Adaptive Relaxed ADMM: Convergence Theory and Practical Implementation. CoRR abs/1704.02712 (2017) - [i10]Abhay Kumar Yadav, Sohil Shah, Zheng Xu, David W. Jacobs, Tom Goldstein:
Stabilizing Adversarial Nets With Prediction Methods. CoRR abs/1705.07364 (2017) - [i9]Hao Li, Soham De, Zheng Xu, Christoph Studer, Hanan Samet, Tom Goldstein:
Training Quantized Nets: A Deeper Understanding. CoRR abs/1706.02379 (2017) - [i8]Zheng Xu, Gavin Taylor, Hao Li, Mário A. T. Figueiredo, Xiaoming Yuan, Tom Goldstein:
Adaptive Consensus ADMM for Distributed Optimization. CoRR abs/1706.02869 (2017) - [i7]Zheng Xu, Yen-Chang Hsu, Jiawei Huang:
Learning Loss for Knowledge Distillation with Conditional Adversarial Networks. CoRR abs/1709.00513 (2017) - [i6]Hao Li, Zheng Xu, Gavin Taylor, Tom Goldstein:
Visualizing the Loss Landscape of Neural Nets. CoRR abs/1712.09913 (2017) - 2016
- [c7]Douglas Burdick, Soham De, Louiqa Raschid, Mingchao Shao, Zheng Xu, Elena Zotkina:
resMBS: Constructing a Financial Supply Chain from Prospectus. DSMM@SIGMOD 2016: 7:1-7:6 - [c6]Zheng Xu, Louiqa Raschid:
Probabilistic Financial Community Models with Latent Dirichlet Allocation for Financial Supply Chains. DSMM@SIGMOD 2016: 8:1-8:6 - [c5]Gavin Taylor, Ryan Burmeister, Zheng Xu, Bharat Singh, Ankit B. Patel, Tom Goldstein:
Training Neural Networks Without Gradients: A Scalable ADMM Approach. ICML 2016: 2722-2731 - [i5]Zheng Xu, Douglas Burdick, Louiqa Raschid:
Exploiting Lists of Names for Named Entity Identification of Financial Institutions from Unstructured Documents. CoRR abs/1602.04427 (2016) - [i4]Gavin Taylor, Ryan Burmeister, Zheng Xu, Bharat Singh, Ankit B. Patel, Tom Goldstein:
Training Neural Networks Without Gradients: A Scalable ADMM Approach. CoRR abs/1605.02026 (2016) - [i3]Zheng Xu, Mário A. T. Figueiredo, Tom Goldstein:
Adaptive ADMM with Spectral Penalty Parameter Selection. CoRR abs/1605.07246 (2016) - [i2]Zheng Xu, Soham De, Mário A. T. Figueiredo, Christoph Studer, Tom Goldstein:
An Empirical Study of ADMM for Nonconvex Problems. CoRR abs/1612.03349 (2016) - [i1]Zheng Xu, Furong Huang, Louiqa Raschid, Tom Goldstein:
Non-negative Factorization of the Occurrence Tensor from Financial Contracts. CoRR abs/1612.03350 (2016) - 2015
- [c4]Zheng Xu, Xue Li, Kuiyuan Yang, Thomas A. Goldstein:
Exploiting Low-rank Structure for Discriminative Sub-categorization. BMVC 2015: 149.1-149.12 - 2014
- [c3]Zheng Xu, Wen Li, Li Niu, Dong Xu:
Exploiting Low-Rank Structure from Latent Domains for Domain Generalization. ECCV (3) 2014: 628-643 - 2013
- [c2]Zheng Xu, Xin-Jing Wang, Chang Wen Chen:
Mining visualness. ICME 2013: 1-6 - 2012
- [c1]Xin-Jing Wang, Zheng Xu, Lei Zhang, Ce Liu, Yong Rui:
Towards indexing representative images on the web. ACM Multimedia 2012: 1229-1238
Coauthor Index
Tom Goldstein
aka: Thomas A. Goldstein
aka: Thomas A. Goldstein
H. Brendan McMahan
aka: Brendan McMahan
aka: Brendan McMahan
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