Xu, Zheng

44 publications

CPAL 2025 A Unified Framework for Sparse Plus Low-Rank Matrix Decomposition for LLMs Mehdi Makni, Kayhan Behdin, Zheng Xu, Natalia Ponomareva, Rahul Mazumder

ICLR 2025 Debiasing Federated Learning with Correlated Client Participation Zhenyu Sun, Ziyang Zhang, Zheng Xu, Gauri Joshi, Pranay Sharma, Ermin Wei

TMLR 2025 Initialization Matters: Unraveling the Impact of Pre-Training on Federated Learning Divyansh Jhunjhunwala, Pranay Sharma, Zheng Xu, Gauri Joshi

NeurIPS 2025 Last-Iterate Convergence of Smooth Regret Matching$^+$ Variants in Learning Nash Equilibria Linjian Meng, Youzhi Zhang, Zhenxing Ge, Tianyu Ding, Shangdong Yang, Zheng Xu, Wenbin Li, Yang Gao

ICML 2025 Synthesizing Privacy-Preserving Text Data via Finetuning *without* Finetuning Billion-Scale LLMs Bowen Tan, Zheng Xu, Eric Xing, Zhiting Hu, Shanshan Wu

ICLRW 2025 Synthesizing Privacy-Preserving Text Data via Finetuning *without* Finetuning Billion-Scale LLMs Bowen Tan, Zheng Xu, Eric P. Xing, Zhiting Hu, Shanshan Wu

ICLRW 2024 Efficient Language Model Architectures for Differentially Private Federated Learning Jae Hun Ro, Srinadh Bhojanapalli, Zheng Xu, Yanxiang Zhang, Ananda Theertha Suresh

ICML 2024 Improved Communication-Privacy Trade-Offs in $l_2$ Mean Estimation Under Streaming Differential Privacy Wei-Ning Chen, Berivan Isik, Peter Kairouz, Albert No, Sewoong Oh, Zheng Xu

TMLR 2024 On the Unreasonable Effectiveness of Federated Averaging with Heterogeneous Data Jianyu Wang, Rudrajit Das, Gauri Joshi, Satyen Kale, Zheng Xu, Tong Zhang

ICML 2024 Privacy-Preserving Instructions for Aligning Large Language Models Da Yu, Peter Kairouz, Sewoong Oh, Zheng Xu

ICML 2024 Safe and Robust Subgame Exploitation in Imperfect Information Games Zhenxing Ge, Zheng Xu, Tianyu Ding, Linjian Meng, Bo An, Wenbin Li, Yang Gao

NeurIPS 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

NeurIPSW 2023 An Empirical Evaluation of Federated Contextual Bandit Algorithms Alekh Agarwal, Hugh McMahan, Zheng Xu

ICML 2023 Beyond Uniform Lipschitz Condition in Differentially Private Optimization Rudrajit Das, Satyen Kale, Zheng Xu, Tong Zhang, Sujay Sanghavi

ICMLW 2023 Can Public Large Language Models Help Private Cross-Device Federated Learning? Boxin Wang, Yibo Jacky Zhang, Yuan Cao, Bo Li, Hugh Brendan McMahan, Sewoong Oh, Zheng Xu, Manzil Zaheer

ICMLW 2023 Can Public Large Language Models Help Private Cross-Device Federated Learning? Boxin Wang, Yibo Jacky Zhang, Yuan Cao, Bo Li, Hugh Brendan McMahan, Sewoong Oh, Zheng Xu, Manzil Zaheer

NeurIPS 2023 Efficient Subgame Refinement for Extensive-Form Games Zhenxing Ge, Zheng Xu, Tianyu Ding, Wenbin Li, Yang Gao

NeurIPSW 2023 Heterogeneous LoRA for Federated Fine-Tuning of On-Device Foundation Models Yae Jee Cho, Luyang Liu, Zheng Xu, Aldi Fahrezi, Matt Barnes, Gauri Joshi

JAIR 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

CVPR 2023 Learning to Generate Image Embeddings with User-Level Differential Privacy Zheng Xu, Maxwell Collins, Yuxiao Wang, Liviu Panait, Sewoong Oh, Sean Augenstein, Ting Liu, Florian Schroff, H. Brendan McMahan

ICML 2023 On the Convergence of Federated Averaging with Cyclic Client Participation Yae Jee Cho, Pranay Sharma, Gauri Joshi, Zheng Xu, Satyen Kale, Tong Zhang

NeurIPSW 2023 User Inference Attacks on LLMs Nikhil Kandpal, Krishna Pillutla, Alina Oprea, Peter Kairouz, Christopher Choquette-Choo, Zheng Xu

NeurIPSW 2023 User Inference Attacks on Large Language Models Nikhil Kandpal, Krishna Pillutla, Alina Oprea, Peter Kairouz, Christopher A. Choquette-Choo, Zheng Xu

NeurIPSW 2022 Adaptive Sparse Federated Learning in Large Output Spaces via Hashing Zhaozhuo Xu, Luyang Liu, Zheng Xu, Anshumali Shrivastava

ICLR 2022 Diurnal or Nocturnal? Federated Learning of Multi-Branch Networks from Periodically Shifting Distributions Chen Zhu, Zheng Xu, Mingqing Chen, Jakub Konečný, Andrew Hard, Tom Goldstein

NeurIPSW 2022 Motley: Benchmarking Heterogeneity and Personalization in Federated Learning Shanshan Wu, Tian Li, Zachary Charles, Yu Xiao, Ken Liu, Zheng Xu, Virginia Smith

FnTML 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

NeurIPSW 2021 Diurnal or Nocturnal? Federated Learning from Periodically Shifting Distributions Chen Zhu, Zheng Xu, Mingqing Chen, Jakub Konečný, Andrew Hard, Tom Goldstein

NeurIPS 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

ICML 2021 Practical and Private (Deep) Learning Without Sampling or Shuffling Peter Kairouz, Brendan Mcmahan, Shuang Song, Om Thakkar, Abhradeep Thakurta, Zheng Xu

CVPRW 2020 Further Non-Local and Channel Attention Networks for Vehicle Re-Identification Kai Liu, Zheng Xu, Zhaohui Hou, Zhicheng Zhao, Fei Su

ICML 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

AAAI 2020 Universal Adversarial Training Ali Shafahi, Mahyar Najibi, Zheng Xu, John P. Dickerson, Larry S. Davis, Tom Goldstein

NeurIPS 2019 Adversarial Training for Free! Ali Shafahi, Mahyar Najibi, Mohammad Amin Ghiasi, Zheng Xu, John Dickerson, Christoph Studer, Larry S. Davis, Gavin Taylor, Tom Goldstein

ICLR 2018 Stabilizing Adversarial Nets with Prediction Methods Abhay Yadav, Sohil Shah, Zheng Xu, David Jacobs, Tom Goldstein

AAAI 2018 Towards Perceptual Image Dehazing by Physics-Based Disentanglement and Adversarial Training Xitong Yang, Zheng Xu, Jiebo Luo

NeurIPS 2018 Visualizing the Loss Landscape of Neural Nets Hao Li, Zheng Xu, Gavin Taylor, Christoph Studer, Tom Goldstein

AAAI 2017 A General Efficient Hyperparameter-Free Algorithm for Convolutional Sparse Learning Zheng Xu, Junzhou Huang

AISTATS 2017 Adaptive ADMM with Spectral Penalty Parameter Selection Zheng Xu, Mário A. T. Figueiredo, Tom Goldstein

ICML 2017 Adaptive Consensus ADMM for Distributed Optimization Zheng Xu, Gavin Taylor, Hao Li, Mário A. T. Figueiredo, Xiaoming Yuan, Tom Goldstein

CVPR 2017 Adaptive Relaxed ADMM: Convergence Theory and Practical Implementation Zheng Xu, Mario A. T. Figueiredo, Xiaoming Yuan, Christoph Studer, Tom Goldstein

NeurIPS 2017 Training Quantized Nets: A Deeper Understanding Hao Li, Soham De, Zheng Xu, Christoph Studer, Hanan Samet, Tom Goldstein

ICML 2016 Training Neural Networks Without Gradients: A Scalable ADMM Approach Gavin Taylor, Ryan Burmeister, Zheng Xu, Bharat Singh, Ankit Patel, Tom Goldstein

ECCV 2014 Exploiting Low-Rank Structure from Latent Domains for Domain Generalization Zheng Xu, Wen Li, Li Niu, Dong Xu