Zheng Xu

ORCID

Publication Activity (10 Years)

Years Active: 2012-2024
Publications (10 Years): 70

Top Venues

University of Maryland, College Park, MD, USA (PHD)

Education History

University of Maryland, College Park, MD, USA

PHD

Publications

Bin Yuan, Maogen Yang, Zheng Xu, Qunjinming Chen, Zhanxiang Song, Zhen Li, Deqing Zou, Hai Jin
Leakage of Authorization-Data in IoT Device Sharing: New Attacks and Countermeasure. IEEE Trans. Dependable Secur. Comput. 21 (4) (2024)
Jae Hun Ro, Srinadh Bhojanapalli, Zheng Xu, Yanxiang Zhang, Ananda Theertha Suresh
Efficient Language Model Architectures for Differentially Private Federated Learning. CoRR (2024)
Zhongyi Zhou, Jing Jin, Vrushank Phadnis, Xiuxiu Yuan, Jun Jiang, Xun Qian, Jingtao Zhou, Yiyi Huang, Zheng Xu, Yinda Zhang, Kristen Wright, Jason Mayes, Mark Sherwood, Johnny Lee, Alex Olwal, David Kim, Ram Iyengar, Na Li, Ruofei Du
Experiencing InstructPipe: Building Multi-modal AI Pipelines via Prompting LLMs and Visual Programming. CHI Extended Abstracts (2024)
Dezhong Yao, Wanning Pan, Yutong Dai, Yao Wan, Xiaofeng Ding, Chen Yu, Hai Jin, Zheng Xu, Lichao Sun
: Toward Heterogeneous Federated Learning via Global Knowledge Distillation. IEEE Trans. Computers 73 (1) (2024)
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 (2024)
Da Yu, Peter Kairouz, Sewoong Oh, Zheng Xu
Privacy-Preserving Instructions for Aligning Large Language Models. CoRR (2024)
Junyuan Hong, Carl Yang, Zhuangdi Zhu, Zheng Xu, Nathalie Baracaldo, Neil Shah, Salman Avestimehr, Jiayu Zhou
FedKDD: International Joint Workshop on Federated Learning for Data Mining and Graph Analytics. KDD (2024)
Shanshan Wu, Zheng Xu, Yanxiang Zhang, Yuanbo Zhang, Daniel Ramage
Prompt Public Large Language Models to Synthesize Data for Private On-device Applications. CoRR (2024)
Jianyu Wang, Rudrajit Das, Gauri Joshi, Satyen Kale, Zheng Xu, Tong Zhang
On the Unreasonable Effectiveness of Federated Averaging with Heterogeneous Data. Trans. Mach. Learn. Res. 2024 (2024)
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 (2023)
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 (2023)
Yuanbo Zhang, Daniel Ramage, Zheng Xu, Yanxiang Zhang, Shumin Zhai, Peter Kairouz
Private Federated Learning in Gboard. CoRR (2023)
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)
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)
Yae Jee Cho, Pranay Sharma, Gauri Joshi, Zheng Xu, Satyen Kale, Tong Zhang
On the Convergence of Federated Averaging with Cyclic Client Participation. CoRR (2023)
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 (2023)
Nikhil Kandpal, Krishna Pillutla, Alina Oprea, Peter Kairouz, Christopher A. Choquette-Choo, Zheng Xu
User Inference Attacks on Large Language Models. CoRR (2023)
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 (2023)
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 (2023)
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)
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)
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)
Alekh Agarwal, H. Brendan McMahan, Zheng Xu
An Empirical Evaluation of Federated Contextual Bandit Algorithms. CoRR (2023)
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 (2022)
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)
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 Horvath, 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 (2021)
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 (2021)
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)
Chen Zhu, Zheng Xu, Ali Shafahi, Manli Shu, Amin Ghiasi, Tom Goldstein
Towards Accurate Quantization and Pruning via Data-free Knowledge Transfer. CoRR (2020)
Zheng Xu, Ali Shafahi, Tom Goldstein
Exploring Model Robustness with Adaptive Networks and Improved Adversarial Training. CoRR (2020)
Ali Shafahi, Mahyar Najibi, Zheng Xu, John P. Dickerson, Larry S. Davis, Tom Goldstein
Universal Adversarial Training. AAAI (2020)
Zheng Xu, Michael J. Wilber, Chen Fang, Aaron Hertzmann, Hailin Jin
Adversarial training for fast arbitrary style transfer. Comput. Graph. 87 (2020)
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)
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 (2019)
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)
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 (2019)
Zheng Xu, Michael J. Wilber, Chen Fang, Aaron Hertzmann, Hailin Jin
Learning from Multi-domain Artistic Images for Arbitrary Style Transfer. Expressive (2019)
Abhay Kumar Yadav, Sohil Shah, Zheng Xu, David W. Jacobs, Tom Goldstein
Stabilizing Adversarial Nets with Prediction Methods. ICLR (Poster) (2018)
Zheng Xu, Michael J. Wilber, Chen Fang, Aaron Hertzmann, Hailin Jin
Beyond Textures: Learning from Multi-domain Artistic Images for Arbitrary Style Transfer. CoRR (2018)
Yen-Chang Hsu, Zheng Xu, Zsolt Kira, Jiawei Huang
Learning to Cluster for Proposal-Free Instance Segmentation. IJCNN (2018)
Zheng Xu, Xitong Yang, Xue Li, Xiaoshuai Sun
The Effectiveness of Instance Normalization: a Strong Baseline for Single Image Dehazing. CoRR (2018)
Zheng Xu, Yen-Chang Hsu, Jiawei Huang
Training Shallow and Thin Networks for Acceleration via Knowledge Distillation with Conditional Adversarial Networks. ICLR (Workshop) (2018)
Xitong Yang, Zheng Xu, Jiebo Luo
Towards Perceptual Image Dehazing by Physics-Based Disentanglement and Adversarial Training. AAAI (2018)
Zheng Xu, Xitong Yang, Xue Li, Xiaoshuai Sun
Strong Baseline for Single Image Dehazing with Deep Features and Instance Normalization. BMVC (2018)
Hao Li, Zheng Xu, Gavin Taylor, Christoph Studer, Tom Goldstein
Visualizing the Loss Landscape of Neural Nets. NeurIPS (2018)
Yen-Chang Hsu, Zheng Xu, Zsolt Kira, Jiawei Huang
Learning to Cluster for Proposal-Free Instance Segmentation. CoRR (2018)
Zheng Xu, Yen-Chang Hsu, Jiawei Huang
Training Student Networks for Acceleration with Conditional Adversarial Networks. BMVC (2018)
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) (2018)
Ali Shafahi, Mahyar Najibi, Zheng Xu, John P. Dickerson, Larry S. Davis, Tom Goldstein
Universal Adversarial Training. CoRR (2018)
Hao Li, Soham De, Zheng Xu, Christoph Studer, Hanan Samet, Tom Goldstein
Training Quantized Nets: A Deeper Understanding. CoRR (2017)
Zheng Xu, Gavin Taylor, Hao Li, Mário A. T. Figueiredo, Xiaoming Yuan, Tom Goldstein
Adaptive Consensus ADMM for Distributed Optimization. ICML (2017)
Hao Li, Zheng Xu, Gavin Taylor, Tom Goldstein
Visualizing the Loss Landscape of Neural Nets. CoRR (2017)
Zheng Xu, Mário A. T. Figueiredo, Xiaoming Yuan, Christoph Studer, Tom Goldstein
Adaptive Relaxed ADMM: Convergence Theory and Practical Implementation. CoRR (2017)
Zheng Xu, Mário A. T. Figueiredo, Tom Goldstein
Adaptive ADMM with Spectral Penalty Parameter Selection. AISTATS (2017)
Zheng Xu, Gavin Taylor, Hao Li, Mário A. T. Figueiredo, Xiaoming Yuan, Tom Goldstein
Adaptive Consensus ADMM for Distributed Optimization. CoRR (2017)
Zheng Xu, Mário A. T. Figueiredo, Xiaoming Yuan, Christoph Studer, Tom Goldstein
Adaptive Relaxed ADMM: Convergence Theory and Practical Implementation. CVPR (2017)
Zheng Xu, Yen-Chang Hsu, Jiawei Huang
Learning Loss for Knowledge Distillation with Conditional Adversarial Networks. CoRR (2017)
Gavin Taylor, Zheng Xu, Tom Goldstein
Scalable Classifiers with ADMM and Transpose Reduction. AAAI Workshops (2017)
Abhay Kumar Yadav, Sohil Shah, Zheng Xu, David W. Jacobs, Tom Goldstein
Stabilizing Adversarial Nets With Prediction Methods. CoRR (2017)
Louiqa Raschid, Zheng Xu, Elena Zotkina
Exploring Financial Relationships Using Probabilistic Topic Models (Demonstration Paper). DSMM@SIGMOD (2017)
Hao Li, Soham De, Zheng Xu, Christoph Studer, Hanan Samet, Tom Goldstein
Training Quantized Nets: A Deeper Understanding. NIPS (2017)
Gavin Taylor, Ryan Burmeister, Zheng Xu, Bharat Singh, Ankit B. Patel, Tom Goldstein
Training Neural Networks Without Gradients: A Scalable ADMM Approach. CoRR (2016)
Zheng Xu, Mário A. T. Figueiredo, Thomas Goldstein
Adaptive ADMM with Spectral Penalty Parameter Selection. CoRR (2016)
Zheng Xu, Louiqa Raschid
Probabilistic Financial Community Models with Latent Dirichlet Allocation for Financial Supply Chains. DSMM@SIGMOD (2016)
Gavin Taylor, Ryan Burmeister, Zheng Xu, Bharat Singh, Ankit B. Patel, Tom Goldstein
Training Neural Networks Without Gradients: A Scalable ADMM Approach. ICML (2016)
Zheng Xu, Douglas Burdick, Louiqa Raschid
Exploiting Lists of Names for Named Entity Identification of Financial Institutions from Unstructured Documents. CoRR (2016)
Zheng Xu, Soham De, Mário A. T. Figueiredo, Christoph Studer, Tom Goldstein
An Empirical Study of ADMM for Nonconvex Problems. CoRR (2016)
Douglas Burdick, Soham De, Louiqa Raschid, Mingchao Shao, Zheng Xu, Elena Zotkina
resMBS: Constructing a Financial Supply Chain from Prospectus. DSMM@SIGMOD (2016)
Zheng Xu, Furong Huang, Louiqa Raschid, Tom Goldstein
Non-negative Factorization of the Occurrence Tensor from Financial Contracts. CoRR (2016)
Zheng Xu, Xue Li, Kuiyuan Yang, Thomas Goldstein
Exploiting Low-rank Structure for Discriminative Sub-categorization. BMVC (2015)
Zheng Xu, Wen Li, Li Niu, Dong Xu
Exploiting Low-Rank Structure from Latent Domains for Domain Generalization. ECCV (3) (2014)
Zheng Xu, Xin-Jing Wang, Chang Wen Chen
Mining visualness. ICME (2013)
Xin-Jing Wang, Zheng Xu, Lei Zhang, Ce Liu, Yong Rui
Towards indexing representative images on the web. ACM Multimedia (2012)

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Publication Activity (10 Years)

Top Topics

Top Venues

Zheng Xu