Zheng Xu

According to our database, Zheng Xu authored at least 62 papers between 2012 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

FedGKD: Toward Heterogeneous Federated Learning via Global Knowledge Distillation.

Dezhong Yao, Wanning Pan, Yutong Dai, Yao Wan, Xiaofeng Ding, Chen Yu, Hai Jin, Zheng Xu, Lichao Sun

IEEE Trans. Computers, January, 2024

Efficient Language Model Architectures for Differentially Private Federated Learning.

Jae Hun Ro, Srinadh Bhojanapalli, Zheng Xu, Yanxiang Zhang, Ananda Theertha Suresh

CoRR, 2024

Privacy-Preserving Instructions for Aligning Large Language Models.

Da Yu, Peter Kairouz, Sewoong Oh, Zheng Xu

CoRR, 2024

Heterogeneous Low-Rank Approximation for Federated Fine-tuning of On-Device Foundation Models.

Yae Jee Cho, Luyang Liu, Zheng Xu, Aldi Fahrezi, Gauri Joshi

CoRR, 2024

2023

How to DP-fy ML: A Practical Guide to Machine Learning with Differential Privacy.

Natalia Ponomareva, Hussein Hazimeh, Alex Kurakin, Zheng Xu, Carson Denison, H. Brendan McMahan, Sergei Vassilvitskii, Steve Chien, Abhradeep Guha Thakurta

J. Artif. Intell. Res., 2023

User Inference Attacks on Large Language Models.

Nikhil Kandpal, Krishna Pillutla, Alina Oprea, Peter Kairouz, Christopher A. Choquette-Choo, Zheng Xu

CoRR, 2023

Private Federated Learning in Gboard.

Yuanbo Zhang, Daniel Ramage, Zheng Xu, Yanxiang Zhang, Shumin Zhai, Peter Kairouz

CoRR, 2023

(Amplified) Banded Matrix Factorization: A unified approach to private training.

Christopher A. Choquette-Choo, Arun Ganesh, Ryan McKenna, H. Brendan McMahan, Keith Rush, Abhradeep Guha Thakurta, Zheng Xu

CoRR, 2023

Can Public Large Language Models Help Private Cross-device Federated Learning?

Boxin Wang, Jacky Yibo Zhang, Yuan Cao, Bo Li, H. Brendan McMahan, Sewoong Oh, Zheng Xu, Manzil Zaheer

CoRR, 2023

An Empirical Evaluation of Federated Contextual Bandit Algorithms.

Alekh Agarwal, H. Brendan McMahan, Zheng Xu

CoRR, 2023

(Amplified) Banded Matrix Factorization: A unified approach to private training.

Christopher A. Choquette-Choo, Arun Ganesh, Ryan McKenna, H. Brendan McMahan, John Rush, Abhradeep Guha Thakurta, Zheng Xu

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

How to DP-fy ML: A Practical Tutorial to Machine Learning with Differential Privacy.

Natalia Ponomareva, Sergei Vassilvitskii, Zheng Xu, Brendan McMahan, Alexey Kurakin, Chiyaun Zhang

Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2023

Beyond Uniform Lipschitz Condition in Differentially Private Optimization.

Rudrajit Das, Satyen Kale, Zheng Xu, Tong Zhang, Sujay Sanghavi

Proceedings of the International Conference on Machine Learning, 2023

On the Convergence of Federated Averaging with Cyclic Client Participation.

Yae Jee Cho, Pranay Sharma, Gauri Joshi, Zheng Xu, Satyen Kale, Tong Zhang

Proceedings of the International Conference on Machine Learning, 2023

Learning to Generate Image Embeddings with User-Level Differential Privacy.

Zheng Xu, Maxwell D. Collins, Yuxiao Wang, Liviu Panait, Sewoong Oh, Sean Augenstein, Ting Liu, Florian Schroff, H. Brendan McMahan

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

Federated Learning of Gboard Language Models with Differential Privacy.

Zheng Xu, Yanxiang Zhang, Galen Andrew, Christopher A. Choquette-Choo, Peter Kairouz, H. Brendan McMahan, Jesse Rosenstock, Yuanbo Zhang

Proceedings of the The 61st Annual Meeting of the Association for Computational Linguistics: Industry Track, 2023

2022

Motley: Benchmarking Heterogeneity and Personalization in Federated Learning.

Shanshan Wu, Tian Li, Zachary Charles, Yu Xiao, Ken Ziyu Liu, Zheng Xu, Virginia Smith

CoRR, 2022

On the Unreasonable Effectiveness of Federated Averaging with Heterogeneous Data.

Jianyu Wang, Rudrajit Das, Gauri Joshi, Satyen Kale, Zheng Xu, Tong Zhang

CoRR, 2022

Diurnal or Nocturnal? Federated Learning of Multi-branch Networks from Periodically Shifting Distributions.

Chen Zhu, Zheng Xu, Mingqing Chen, Jakub Konecný, Andrew Hard, Tom Goldstein

Proceedings of the Tenth International Conference on Learning Representations, 2022

2021

Advances and Open Problems in Federated Learning.

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

Found. Trends Mach. Learn., 2021

Efficient and Private Federated Learning with Partially Trainable Networks.

Hakim Sidahmed, Zheng Xu, Ankush Garg, Yuan Cao, Mingqing Chen

CoRR, 2021

A Field Guide to Federated Optimization.

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

CoRR, 2021

Local Adaptivity in Federated Learning: Convergence and Consistency.

Jianyu Wang, Zheng Xu, Zachary Garrett, Zachary Charles, Luyang Liu, Gauri Joshi

CoRR, 2021

GradInit: Learning to Initialize Neural Networks for Stable and Efficient Training.

Chen Zhu, Renkun Ni, Zheng Xu, Kezhi Kong, W. Ronny Huang, Tom Goldstein

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Practical and Private (Deep) Learning Without Sampling or Shuffling.

Peter Kairouz, Brendan McMahan, Shuang Song, Om Thakkar, Abhradeep Thakurta, Zheng Xu

Proceedings of the 38th International Conference on Machine Learning, 2021

2020

Towards Accurate Quantization and Pruning via Data-free Knowledge Transfer.

Chen Zhu, Zheng Xu, Ali Shafahi, Manli Shu, Amin Ghiasi, Tom Goldstein

CoRR, 2020

Exploring Model Robustness with Adaptive Networks and Improved Adversarial Training.

Zheng Xu, Ali Shafahi, Tom Goldstein

CoRR, 2020

Adversarial training for fast arbitrary style transfer.

Zheng Xu, Michael J. Wilber, Chen Fang, Aaron Hertzmann, Hailin Jin

Comput. Graph., 2020

The Impact of Neural Network Overparameterization on Gradient Confusion and Stochastic Gradient Descent.

Karthik Abinav Sankararaman, Soham De, Zheng Xu, W. Ronny Huang, Tom Goldstein

Proceedings of the 37th International Conference on Machine Learning, 2020

Universal Adversarial Training.

Ali Shafahi, Mahyar Najibi, Zheng Xu, John P. Dickerson, Larry S. Davis, Tom Goldstein

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

2019

Alternating Optimization: Constrained Problems, Adversarial Networks, and Robust Models.

Zheng Xu

PhD thesis, 2019

Advances and Open Problems in Federated Learning.

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

CoRR, 2019

Adversarial training for free!

Ali Shafahi, Mahyar Najibi, Amin Ghiasi, Zheng Xu, John P. Dickerson, Christoph Studer, Larry S. Davis, Gavin Taylor, Tom Goldstein

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Learning from Multi-domain Artistic Images for Arbitrary Style Transfer.

Zheng Xu, Michael J. Wilber, Chen Fang, Aaron Hertzmann, Hailin Jin

Proceedings of the 8th ACM/Eurographics Expressive Symposium, 2019

2018

Domain Generalization and Adaptation Using Low Rank Exemplar SVMs.

Wen Li, Zheng Xu, Dong Xu, Dengxin Dai, Luc Van Gool

IEEE Trans. Pattern Anal. Mach. Intell., 2018

Beyond Textures: Learning from Multi-domain Artistic Images for Arbitrary Style Transfer.

Zheng Xu, Michael J. Wilber, Chen Fang, Aaron Hertzmann, Hailin Jin

CoRR, 2018

The Effectiveness of Instance Normalization: a Strong Baseline for Single Image Dehazing.

Zheng Xu, Xitong Yang, Xue Li, Xiaoshuai Sun

CoRR, 2018

Visualizing the Loss Landscape of Neural Nets.

Hao Li, Zheng Xu, Gavin Taylor, Christoph Studer, Tom Goldstein

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

Learning to Cluster for Proposal-Free Instance Segmentation.

Yen-Chang Hsu, Zheng Xu, Zsolt Kira, Jiawei Huang

Proceedings of the 2018 International Joint Conference on Neural Networks, 2018

Stabilizing Adversarial Nets with Prediction Methods.

Abhay Kumar Yadav, Sohil Shah, Zheng Xu, David W. Jacobs, Tom Goldstein

Proceedings of the 6th International Conference on Learning Representations, 2018

Training Shallow and Thin Networks for Acceleration via Knowledge Distillation with Conditional Adversarial Networks.

Zheng Xu, Yen-Chang Hsu, Jiawei Huang

Proceedings of the 6th International Conference on Learning Representations, 2018

Strong Baseline for Single Image Dehazing with Deep Features and Instance Normalization.

Zheng Xu, Xitong Yang, Xue Li, Xiaoshuai Sun

Proceedings of the British Machine Vision Conference 2018, 2018

Training Student Networks for Acceleration with Conditional Adversarial Networks.

Zheng Xu, Yen-Chang Hsu, Jiawei Huang

Proceedings of the British Machine Vision Conference 2018, 2018

Towards Perceptual Image Dehazing by Physics-Based Disentanglement and Adversarial Training.

Xitong Yang, Zheng Xu, Jiebo Luo

Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, 2018

2017

Visualizing the Loss Landscape of Neural Nets.

Hao Li, Zheng Xu, Gavin Taylor, Tom Goldstein

CoRR, 2017

Learning Loss for Knowledge Distillation with Conditional Adversarial Networks.

Zheng Xu, Yen-Chang Hsu, Jiawei Huang

CoRR, 2017

Exploring Financial Relationships Using Probabilistic Topic Models (Demonstration Paper).

Louiqa Raschid, Zheng Xu, Elena Zotkina

Proceedings of the 3rd International Workshop on Data Science for Macro-Modeling with Financial and Economic Datasets, 2017

Training Quantized Nets: A Deeper Understanding.

Hao Li, Soham De, Zheng Xu, Christoph Studer, Hanan Samet, Tom Goldstein

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Adaptive Consensus ADMM for Distributed Optimization.

Zheng Xu, Gavin Taylor, Hao Li, Mário A. T. Figueiredo, Xiaoming Yuan, Tom Goldstein

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

Adaptive Relaxed ADMM: Convergence Theory and Practical Implementation.

Zheng Xu, Mário A. T. Figueiredo, Xiaoming Yuan, Christoph Studer, Tom Goldstein

Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition, 2017

Adaptive ADMM with Spectral Penalty Parameter Selection.

Zheng Xu, Mário A. T. Figueiredo, Tom Goldstein

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

Scalable Classifiers with ADMM and Transpose Reduction.

Gavin Taylor, Zheng Xu, Tom Goldstein

Proceedings of the Workshops of the The Thirty-First AAAI Conference on Artificial Intelligence, 2017

2016

Exploiting Lists of Names for Named Entity Identification of Financial Institutions from Unstructured Documents.

Zheng Xu, Douglas Burdick, Louiqa Raschid

CoRR, 2016

Non-negative Factorization of the Occurrence Tensor from Financial Contracts.

Zheng Xu, Furong Huang, Louiqa Raschid, Tom Goldstein

CoRR, 2016

An Empirical Study of ADMM for Nonconvex Problems.

Zheng Xu, Soham De, Mário A. T. Figueiredo, Christoph Studer, Tom Goldstein

CoRR, 2016

Training Neural Networks Without Gradients: A Scalable ADMM Approach.

Gavin Taylor, Ryan Burmeister, Zheng Xu, Bharat Singh, Ankit B. Patel, Tom Goldstein

Proceedings of the 33nd International Conference on Machine Learning, 2016

Probabilistic Financial Community Models with Latent Dirichlet Allocation for Financial Supply Chains.

Zheng Xu, Louiqa Raschid

Proceedings of the Second International Workshop on Data Science for Macro-Modeling, 2016

resMBS: Constructing a Financial Supply Chain from Prospectus.

Douglas Burdick, Soham De, Louiqa Raschid, Mingchao Shao, Zheng Xu, Elena Zotkina

Proceedings of the Second International Workshop on Data Science for Macro-Modeling, 2016

2015

Exploiting Low-rank Structure for Discriminative Sub-categorization.

Zheng Xu, Xue Li, Kuiyuan Yang, Thomas A. Goldstein

Proceedings of the British Machine Vision Conference 2015, 2015

2014

Exploiting Low-Rank Structure from Latent Domains for Domain Generalization.

Zheng Xu, Wen Li, Li Niu, Dong Xu

Proceedings of the Computer Vision - ECCV 2014, 2014

2013

Mining visualness.

Zheng Xu, Xin-Jing Wang, Chang Wen Chen

Proceedings of the 2013 IEEE International Conference on Multimedia and Expo, 2013

2012

Towards indexing representative images on the web.

Xin-Jing Wang, Zheng Xu, Lei Zhang, Ce Liu, Yong Rui

Proceedings of the 20th ACM Multimedia Conference, MM '12, Nara, Japan, October 29, 2012