Zheng Xu - RankMe

110 papers A* 24A 5B 1Journal 61Unranked 18

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	MAPLE: Metadata Augmented Private Language Evolution. CoRR Eli Chien, Yuzheng Hu, Ryan McKenna, Shanshan Wu, Zheng Xu, Peter Kairouz
2025	—	`conf`	A unified framework for Sparse plus Low-Rank Matrix Decomposition for LLMs. CPAL Mehdi Makni, Kayhan Behdin, Zheng Xu, Natalia Ponomareva, Rahul Mazumder
2025	J	`jnl`	ACTG-ARL: Differentially Private Conditional Text Generation with RL-Boosted Control. CoRR Yuzheng Hu, Ryan McKenna, Da Yu, Shanshan Wu, Han Zhao, Zheng Xu, Peter Kairouz
2025	J	`jnl`	An Optimization Framework for Differentially Private Sparse Fine-Tuning. CoRR Mehdi Makni, Kayhan Behdin, Gabriel Afriat, Zheng Xu, Sergei Vassilvitskii, Natalia Ponomareva, Hussein Hazimeh, Rahul Mazumder
2025	A*	`conf`	Debiasing Federated Learning with Correlated Client Participation. ICLR Zhenyu Sun, Ziyang Zhang, Zheng Xu, Gauri Joshi, Pranay Sharma, Ermin Wei
2025	—	`conf`	FedKDD 2025: The 2025 International Joint Workshop on Federated Learning for Data Mining and Graph Analytics. KDD (2) Carl Yang, Guancheng Wan, Zhuangdi Zhu, Zheng Xu, Junyuan Hong, Nathalie Baracaldo, Neil Shah, Salman Avestimehr, Jiayu Zhou
2025	J	`jnl`	HASSLE-free: A unified Framework for Sparse plus Low-Rank Matrix Decomposition for LLMs. CoRR Mehdi Makni, Kayhan Behdin, Zheng Xu, Natalia Ponomareva, Rahul Mazumder
2025	J	`jnl`	How to DP-fy Your Data: A Practical Guide to Generating Synthetic Data With Differential Privacy. CoRR Natalia Ponomareva, Zheng Xu, H. Brendan McMahan, Peter Kairouz, Lucas Rosenblatt, Vincent Cohen-Addad, Cristóbal Guzmán, Ryan McKenna, Galen Andrew, Alex Bie, Da Yu, Alex Kurakin, Morteza Zadimoghaddam, Sergei Vassilvitskii, Andreas Terzis
2025	J	`jnl`	Initialization Matters: Unraveling the Impact of Pre-Training on Federated Learning. CoRR Divyansh Jhunjhunwala, Pranay Sharma, Zheng Xu, Gauri Joshi
2025	J	`jnl`	Initialization Matters: Unraveling the Impact of Pre-Training on Federated Learning. Trans. Mach. Learn. Res. Divyansh Jhunjhunwala, Pranay Sharma, Zheng Xu, Gauri Joshi
2025	A*	`conf`	InstructPipe: Generating Visual Blocks Pipelines with Human Instructions and LLMs. CHI Zhongyi Zhou, Jing Jin, Vrushank Phadnis, Xiuxiu Yuan, Jun Jiang, Xun Qian, Kristen Wright, Mark Sherwood, Jason Mayes, Jingtao Zhou, Yiyi Huang, Zheng Xu, Yinda Zhang, Johnny Lee, Alex Olwal, David Kim, Ram Iyengar, Na Li, Ruofei Du
2025	—	`conf`	SPARTA: An Optimization Framework for Differentially Private Sparse Fine-Tuning. KDD (2) Mehdi Makni, Kayhan Behdin, Gabriel Afriat, Zheng Xu, Sergei Vassilvitskii, Natalia Ponomareva, Rahul Mazumder, Hussein Hazimeh
2025	—	`conf`	Synthesizing and Adapting Error Correction Data for Mobile Large Language Model Applications. ACL (6) Yanxiang Zhang, Zheng Xu, Shanshan Wu, Yuanbo Zhang, Daniel Ramage
2025	J	`jnl`	ToolGrad: Efficient Tool-use Dataset Generation with Textual "Gradients". CoRR Zhongyi Zhou, Kohei Uehara, Haoyu Zhang, Jingtao Zhou, Lin Gu, Ruofei Du, Zheng Xu, Tatsuya Harada
2024	J	`jnl`	A Hassle-free Algorithm for Private Learning in Practice: Don't Use Tree Aggregation, Use BLTs. CoRR H. Brendan McMahan, Zheng Xu, Yanxiang Zhang
2024	—	`conf`	A Hassle-free Algorithm for Strong Differential Privacy in Federated Learning Systems. EMNLP (Industry Track) Hugh McMahan, Zheng Xu, Yanxiang Zhang
2024	—	`conf`	Can Public Large Language Models Help Private Cross-device Federated Learning? NAACL-HLT (Findings) Boxin Wang, Yibo Zhang, Yuan Cao, Bo Li, Hugh McMahan, Sewoong Oh, Zheng Xu, Manzil Zaheer
2024	J	`jnl`	Debiasing Federated Learning with Correlated Client Participation. CoRR Zhenyu Sun, Ziyang Zhang, Zheng Xu, Gauri Joshi, Pranay Sharma, Ermin Wei
2024	J	`jnl`	Efficient Language Model Architectures for Differentially Private Federated Learning. CoRR Jae Hun Ro, Srinadh Bhojanapalli, Zheng Xu, Yanxiang Zhang, Ananda Theertha Suresh
2024	—	`conf`	Experiencing InstructPipe: Building Multi-modal AI Pipelines via Prompting LLMs and Visual Programming. CHI Extended Abstracts 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
2024	J	`jnl`	FedGKD: Toward Heterogeneous Federated Learning via Global Knowledge Distillation. IEEE Trans. Computers Dezhong Yao, Wanning Pan, Yutong Dai, Yao Wan, Xiaofeng Ding, Chen Yu, Hai Jin, Zheng Xu, Lichao Sun
2024	A*	`conf`	FedKDD: International Joint Workshop on Federated Learning for Data Mining and Graph Analytics. KDD Junyuan Hong, Carl Yang, Zhuangdi Zhu, Zheng Xu, Nathalie Baracaldo, Neil Shah, Salman Avestimehr, Jiayu Zhou
2024	—	`conf`	Federated Learning in Practice: Reflections and Projections. TPS-ISA Katharine Daly, Hubert Eichner, Peter Kairouz, H. Brendan McMahan, Daniel Ramage, Zheng Xu
2024	J	`jnl`	Federated Learning in Practice: Reflections and Projections. CoRR Katharine Daly, Hubert Eichner, Peter Kairouz, H. Brendan McMahan, Daniel Ramage, Zheng Xu
2024	A*	`conf`	Heterogeneous LoRA for Federated Fine-tuning of On-Device Foundation Models. EMNLP Yae Jee Cho, Luyang Liu, Zheng Xu, Aldi Fahrezi, Gauri Joshi
2024	J	`jnl`	Heterogeneous Low-Rank Approximation for Federated Fine-tuning of On-Device Foundation Models. CoRR Yae Jee Cho, Luyang Liu, Zheng Xu, Aldi Fahrezi, Gauri Joshi
2024	J	`jnl`	Improved Communication-Privacy Trade-offs in L CoRR Wei-Ning Chen, Berivan Isik, Peter Kairouz, Albert No, Sewoong Oh, Zheng Xu
2024	A*	`conf`	Improved Communication-Privacy Trade-offs in L2 Mean Estimation under Streaming Differential Privacy. ICML Wei-Ning Chen, Berivan Isik, Peter Kairouz, Albert No, Sewoong Oh, Zheng Xu
2024	J	`jnl`	On the Unreasonable Effectiveness of Federated Averaging with Heterogeneous Data. Trans. Mach. Learn. Res. Jianyu Wang, Rudrajit Das, Gauri Joshi, Satyen Kale, Zheng Xu, Tong Zhang
2024	A*	`conf`	Privacy-Preserving Instructions for Aligning Large Language Models. ICML Da Yu, Peter Kairouz, Sewoong Oh, Zheng Xu
2024	J	`jnl`	Privacy-Preserving Instructions for Aligning Large Language Models. CoRR Da Yu, Peter Kairouz, Sewoong Oh, Zheng Xu
2024	J	`jnl`	Prompt Public Large Language Models to Synthesize Data for Private On-device Applications. CoRR Shanshan Wu, Zheng Xu, Yanxiang Zhang, Yuanbo Zhang, Daniel Ramage
2024	J	`jnl`	Randomization Techniques to Mitigate the Risk of Copyright Infringement. CoRR Wei-Ning Chen, Peter Kairouz, Sewoong Oh, Zheng Xu
2024	A*	`conf`	User Inference Attacks on Large Language Models. EMNLP Nikhil Kandpal, Krishna Pillutla, Alina Oprea, Peter Kairouz, Christopher A. Choquette-Choo, Zheng Xu
2023	A*	`conf`	(Amplified) Banded Matrix Factorization: A unified approach to private training. NeurIPS Christopher A. Choquette-Choo, Arun Ganesh, Ryan McKenna, H. Brendan McMahan, John Rush, Abhradeep Guha Thakurta, Zheng Xu
2023	J	`jnl`	(Amplified) Banded Matrix Factorization: A unified approach to private training. CoRR Christopher A. Choquette-Choo, Arun Ganesh, Ryan McKenna, H. Brendan McMahan, Keith Rush, Abhradeep Guha Thakurta, Zheng Xu
2023	J	`jnl`	An Empirical Evaluation of Federated Contextual Bandit Algorithms. CoRR Alekh Agarwal, H. Brendan McMahan, Zheng Xu
2023	A*	`conf`	Beyond Uniform Lipschitz Condition in Differentially Private Optimization. ICML Rudrajit Das, Satyen Kale, Zheng Xu, Tong Zhang, Sujay Sanghavi
2023	J	`jnl`	Can Public Large Language Models Help Private Cross-device Federated Learning? CoRR Boxin Wang, Jacky Yibo Zhang, Yuan Cao, Bo Li, H. Brendan McMahan, Sewoong Oh, Zheng Xu, Manzil Zaheer
2023	—	`conf`	Federated Learning of Gboard Language Models with Differential Privacy. ACL (industry) Zheng Xu, Yanxiang Zhang, Galen Andrew, Christopher A. Choquette-Choo, Peter Kairouz, H. Brendan McMahan, Jesse Rosenstock, Yuanbo Zhang
2023	J	`jnl`	Federated Learning of Gboard Language Models with Differential Privacy. CoRR Zheng Xu, Yanxiang Zhang, Galen Andrew, Christopher A. Choquette-Choo, Peter Kairouz, H. Brendan McMahan, Jesse Rosenstock, Yuanbo Zhang
2023	J	`jnl`	How to DP-fy ML: A Practical Guide to Machine Learning with Differential Privacy. CoRR Natalia Ponomareva, Hussein Hazimeh, Alex Kurakin, Zheng Xu, Carson Denison, H. Brendan McMahan, Sergei Vassilvitskii, Steve Chien, Abhradeep Thakurta
2023	J	`jnl`	How to DP-fy ML: A Practical Guide to Machine Learning with Differential Privacy. J. Artif. Intell. Res. Natalia Ponomareva, Hussein Hazimeh, Alex Kurakin, Zheng Xu, Carson Denison, H. Brendan McMahan, Sergei Vassilvitskii, Steve Chien, Abhradeep Guha Thakurta
2023	A*	`conf`	How to DP-fy ML: A Practical Tutorial to Machine Learning with Differential Privacy. KDD Natalia Ponomareva, Sergei Vassilvitskii, Zheng Xu, Brendan McMahan, Alexey Kurakin, Chiyaun Zhang
2023	J	`jnl`	InstructPipe: Building Visual Programming Pipelines with Human Instructions. CoRR 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
2023	A*	`conf`	Learning to Generate Image Embeddings with User-Level Differential Privacy. CVPR Zheng Xu, Maxwell D. Collins, Yuxiao Wang, Liviu Panait, Sewoong Oh, Sean Augenstein, Ting Liu, Florian Schroff, H. Brendan McMahan
2023	A*	`conf`	On the Convergence of Federated Averaging with Cyclic Client Participation. ICML Yae Jee Cho, Pranay Sharma, Gauri Joshi, Zheng Xu, Satyen Kale, Tong Zhang
2023	J	`jnl`	On the Convergence of Federated Averaging with Cyclic Client Participation. CoRR Yae Jee Cho, Pranay Sharma, Gauri Joshi, Zheng Xu, Satyen Kale, Tong Zhang
2023	J	`jnl`	Private Federated Learning in Gboard. CoRR Yuanbo Zhang, Daniel Ramage, Zheng Xu, Yanxiang Zhang, Shumin Zhai, Peter Kairouz
2023	J	`jnl`	User Inference Attacks on Large Language Models. CoRR Nikhil Kandpal, Krishna Pillutla, Alina Oprea, Peter Kairouz, Christopher A. Choquette-Choo, Zheng Xu
2022	J	`jnl`	Beyond Uniform Lipschitz Condition in Differentially Private Optimization. CoRR Rudrajit Das, Satyen Kale, Zheng Xu, Tong Zhang, Sujay Sanghavi
2022	A*	`conf`	Diurnal or Nocturnal? Federated Learning of Multi-branch Networks from Periodically Shifting Distributions. ICLR Chen Zhu, Zheng Xu, Mingqing Chen, Jakub Konecný, Andrew Hard, Tom Goldstein
2022	J	`jnl`	Learning to Generate Image Embeddings with User-level Differential Privacy. CoRR Zheng Xu, Maxwell D. Collins, Yuxiao Wang, Liviu Panait, Sewoong Oh, Sean Augenstein, Ting Liu, Florian Schroff, H. Brendan McMahan
2022	J	`jnl`	Motley: Benchmarking Heterogeneity and Personalization in Federated Learning. CoRR Shanshan Wu, Tian Li, Zachary Charles, Yu Xiao, Ken Ziyu Liu, Zheng Xu, Virginia Smith
2022	J	`jnl`	On the Unreasonable Effectiveness of Federated Averaging with Heterogeneous Data. CoRR Jianyu Wang, Rudrajit Das, Gauri Joshi, Satyen Kale, Zheng Xu, Tong Zhang
2021	J	`jnl`	A Field Guide to Federated Optimization. CoRR 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
2021	J	`jnl`	Advances and Open Problems in Federated Learning. Found. Trends Mach. Learn. 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
2021	J	`jnl`	Efficient and Private Federated Learning with Partially Trainable Networks. CoRR Hakim Sidahmed, Zheng Xu, Ankush Garg, Yuan Cao, Mingqing Chen
2021	A*	`conf`	GradInit: Learning to Initialize Neural Networks for Stable and Efficient Training. NeurIPS Chen Zhu, Renkun Ni, Zheng Xu, Kezhi Kong, W. Ronny Huang, Tom Goldstein
2021	J	`jnl`	GradInit: Learning to Initialize Neural Networks for Stable and Efficient Training. CoRR Chen Zhu, Renkun Ni, Zheng Xu, Kezhi Kong, W. Ronny Huang, Tom Goldstein
2021	J	`jnl`	Local Adaptivity in Federated Learning: Convergence and Consistency. CoRR Jianyu Wang, Zheng Xu, Zachary Garrett, Zachary Charles, Luyang Liu, Gauri Joshi
2021	A*	`conf`	Practical and Private (Deep) Learning Without Sampling or Shuffling. ICML Peter Kairouz, Brendan McMahan, Shuang Song, Om Thakkar, Abhradeep Thakurta, Zheng Xu
2021	J	`jnl`	Practical and Private (Deep) Learning without Sampling or Shuffling. CoRR Peter Kairouz, Brendan McMahan, Shuang Song, Om Thakkar, Abhradeep Thakurta, Zheng Xu
2020	J	`jnl`	Adversarial training for fast arbitrary style transfer. Comput. Graph. Zheng Xu, Michael J. Wilber, Chen Fang, Aaron Hertzmann, Hailin Jin
2020	J	`jnl`	Exploring Model Robustness with Adaptive Networks and Improved Adversarial Training. CoRR Zheng Xu, Ali Shafahi, Tom Goldstein
2020	A*	`conf`	The Impact of Neural Network Overparameterization on Gradient Confusion and Stochastic Gradient Descent. ICML Karthik Abinav Sankararaman, Soham De, Zheng Xu, W. Ronny Huang, Tom Goldstein
2020	J	`jnl`	Towards Accurate Quantization and Pruning via Data-free Knowledge Transfer. CoRR Chen Zhu, Zheng Xu, Ali Shafahi, Manli Shu, Amin Ghiasi, Tom Goldstein
2020	A*	`conf`	Universal Adversarial Training. AAAI Ali Shafahi, Mahyar Najibi, Zheng Xu, John P. Dickerson, Larry S. Davis, Tom Goldstein
2019	J	`jnl`	Advances and Open Problems in Federated Learning. CoRR 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
2019	J	`jnl`	Adversarial Training for Free! CoRR Ali Shafahi, Mahyar Najibi, Amin Ghiasi, Zheng Xu, John P. Dickerson, Christoph Studer, Larry S. Davis, Gavin Taylor, Tom Goldstein
2019	A*	`conf`	Adversarial training for free! NeurIPS Ali Shafahi, Mahyar Najibi, Amin Ghiasi, Zheng Xu, John P. Dickerson, Christoph Studer, Larry S. Davis, Gavin Taylor, Tom Goldstein
2019	—	`—`	Alternating Optimization: Constrained Problems, Adversarial Networks, and Robust Models. Zheng Xu
2019	—	`conf`	Learning from Multi-domain Artistic Images for Arbitrary Style Transfer. Expressive Zheng Xu, Michael J. Wilber, Chen Fang, Aaron Hertzmann, Hailin Jin
2019	J	`jnl`	The Impact of Neural Network Overparameterization on Gradient Confusion and Stochastic Gradient Descent. CoRR Karthik Abinav Sankararaman, Soham De, Zheng Xu, W. Ronny Huang, Tom Goldstein
2018	J	`jnl`	Beyond Textures: Learning from Multi-domain Artistic Images for Arbitrary Style Transfer. CoRR Zheng Xu, Michael J. Wilber, Chen Fang, Aaron Hertzmann, Hailin Jin
2018	J	`jnl`	Domain Generalization and Adaptation Using Low Rank Exemplar SVMs. IEEE Trans. Pattern Anal. Mach. Intell. Wen Li, Zheng Xu, Dong Xu, Dengxin Dai, Luc Van Gool
2018	B	`conf`	Learning to Cluster for Proposal-Free Instance Segmentation. IJCNN Yen-Chang Hsu, Zheng Xu, Zsolt Kira, Jiawei Huang
2018	J	`jnl`	Learning to Cluster for Proposal-Free Instance Segmentation. CoRR Yen-Chang Hsu, Zheng Xu, Zsolt Kira, Jiawei Huang
2018	—	`conf`	Stabilizing Adversarial Nets with Prediction Methods. ICLR (Poster) Abhay Kumar Yadav, Sohil Shah, Zheng Xu, David W. Jacobs, Tom Goldstein
2018	A	`conf`	Strong Baseline for Single Image Dehazing with Deep Features and Instance Normalization. BMVC Zheng Xu, Xitong Yang, Xue Li, Xiaoshuai Sun
2018	J	`jnl`	The Effectiveness of Instance Normalization: a Strong Baseline for Single Image Dehazing. CoRR Zheng Xu, Xitong Yang, Xue Li, Xiaoshuai Sun
2018	A*	`conf`	Towards Perceptual Image Dehazing by Physics-Based Disentanglement and Adversarial Training. AAAI Xitong Yang, Zheng Xu, Jiebo Luo
2018	—	`conf`	Training Shallow and Thin Networks for Acceleration via Knowledge Distillation with Conditional Adversarial Networks. ICLR (Workshop) Zheng Xu, Yen-Chang Hsu, Jiawei Huang
2018	A	`conf`	Training Student Networks for Acceleration with Conditional Adversarial Networks. BMVC Zheng Xu, Yen-Chang Hsu, Jiawei Huang
2018	J	`jnl`	Universal Adversarial Training. CoRR Ali Shafahi, Mahyar Najibi, Zheng Xu, John P. Dickerson, Larry S. Davis, Tom Goldstein
2018	A*	`conf`	Visualizing the Loss Landscape of Neural Nets. NeurIPS Hao Li, Zheng Xu, Gavin Taylor, Christoph Studer, Tom Goldstein
2017	A	`conf`	Adaptive ADMM with Spectral Penalty Parameter Selection. AISTATS Zheng Xu, Mário A. T. Figueiredo, Tom Goldstein
2017	A*	`conf`	Adaptive Consensus ADMM for Distributed Optimization. ICML Zheng Xu, Gavin Taylor, Hao Li, Mário A. T. Figueiredo, Xiaoming Yuan, Tom Goldstein
2017	J	`jnl`	Adaptive Consensus ADMM for Distributed Optimization. CoRR Zheng Xu, Gavin Taylor, Hao Li, Mário A. T. Figueiredo, Xiaoming Yuan, Tom Goldstein
2017	A*	`conf`	Adaptive Relaxed ADMM: Convergence Theory and Practical Implementation. CVPR Zheng Xu, Mário A. T. Figueiredo, Xiaoming Yuan, Christoph Studer, Tom Goldstein
2017	J	`jnl`	Adaptive Relaxed ADMM: Convergence Theory and Practical Implementation. CoRR Zheng Xu, Mário A. T. Figueiredo, Xiaoming Yuan, Christoph Studer, Tom Goldstein
2017	—	`conf`	Exploring Financial Relationships Using Probabilistic Topic Models (Demonstration Paper). DSMM@SIGMOD Louiqa Raschid, Zheng Xu, Elena Zotkina
2017	J	`jnl`	Learning Loss for Knowledge Distillation with Conditional Adversarial Networks. CoRR Zheng Xu, Yen-Chang Hsu, Jiawei Huang
2017	—	`conf`	Scalable Classifiers with ADMM and Transpose Reduction. AAAI Workshops Gavin Taylor, Zheng Xu, Tom Goldstein
2017	J	`jnl`	Stabilizing Adversarial Nets With Prediction Methods. CoRR Abhay Kumar Yadav, Sohil Shah, Zheng Xu, David W. Jacobs, Tom Goldstein
2017	—	`conf`	Training Quantized Nets: A Deeper Understanding. NIPS Hao Li, Soham De, Zheng Xu, Christoph Studer, Hanan Samet, Tom Goldstein
2017	J	`jnl`	Training Quantized Nets: A Deeper Understanding. CoRR Hao Li, Soham De, Zheng Xu, Christoph Studer, Hanan Samet, Tom Goldstein
2017	J	`jnl`	Visualizing the Loss Landscape of Neural Nets. CoRR Hao Li, Zheng Xu, Gavin Taylor, Tom Goldstein
2016	J	`jnl`	Adaptive ADMM with Spectral Penalty Parameter Selection. CoRR Zheng Xu, Mário A. T. Figueiredo, Tom Goldstein
2016	J	`jnl`	An Empirical Study of ADMM for Nonconvex Problems. CoRR Zheng Xu, Soham De, Mário A. T. Figueiredo, Christoph Studer, Tom Goldstein
2016	J	`jnl`	Exploiting Lists of Names for Named Entity Identification of Financial Institutions from Unstructured Documents. CoRR Zheng Xu, Douglas Burdick, Louiqa Raschid
2016	J	`jnl`	Non-negative Factorization of the Occurrence Tensor from Financial Contracts. CoRR Zheng Xu, Furong Huang, Louiqa Raschid, Tom Goldstein
2016	—	`conf`	Probabilistic Financial Community Models with Latent Dirichlet Allocation for Financial Supply Chains. DSMM@SIGMOD Zheng Xu, Louiqa Raschid
2016	A*	`conf`	Training Neural Networks Without Gradients: A Scalable ADMM Approach. ICML Gavin Taylor, Ryan Burmeister, Zheng Xu, Bharat Singh, Ankit B. Patel, Tom Goldstein
2016	J	`jnl`	Training Neural Networks Without Gradients: A Scalable ADMM Approach. CoRR Gavin Taylor, Ryan Burmeister, Zheng Xu, Bharat Singh, Ankit B. Patel, Tom Goldstein
2016	—	`conf`	resMBS: Constructing a Financial Supply Chain from Prospectus. DSMM@SIGMOD Douglas Burdick, Soham De, Louiqa Raschid, Mingchao Shao, Zheng Xu, Elena Zotkina
2015	A	`conf`	Exploiting Low-rank Structure for Discriminative Sub-categorization. BMVC Zheng Xu, Xue Li, Kuiyuan Yang, Thomas A. Goldstein
2014	—	`conf`	Exploiting Low-Rank Structure from Latent Domains for Domain Generalization. ECCV (3) Zheng Xu, Wen Li, Li Niu, Dong Xu
2013	A	`conf`	Mining visualness. ICME Zheng Xu, Xin-Jing Wang, Chang Wen Chen
2012	A*	`conf`	Towards indexing representative images on the web. ACM Multimedia Xin-Jing Wang, Zheng Xu, Lei Zhang, Ce Liu, Yong Rui