Követés
Zheng Xu
Cím
Hivatkozott rá
Hivatkozott rá
Év
Advances and open problems in federated learning
P Kairouz, HB McMahan, B Avent, A Bellet, M Bennis, AN Bhagoji, ...
arXiv preprint arXiv:1912.04977, 2019
72582019
Visualizing the loss landscape of neural nets
H Li, Z Xu, G Taylor, C Studer, T Goldstein
Advances in Neural Information Processing Systems, 6391-6401, 2018
23892018
Adversarial Training for Free!
A Shafahi, M Najibi, A Ghiasi, Z Xu, J Dickerson, C Studer, LS Davis, ...
Conference on Neural Information Processing Systems (NeurIPS), 2019
16422019
A Field Guide to Federated Optimization
J Wang, Z Charles, Z Xu, G Joshi, HB McMahan, M Al-Shedivat, G Andrew, ...
arXiv preprint arXiv:2107.06917, 2021
4162021
Training neural networks without gradients: A scalable ADMM approach
G Taylor, R Burmeister, Z Xu, B Singh, A Patel, T Goldstein
International Conference on Machine Learning (ICML), 2722-2731, 2016
3352016
Towards Perceptual Image Dehazing by Physics-based Disentanglement and Adversarial Training
X Yang, Z Xu, J Luo
AAAI Conference on Artificial Intelligence (AAAI), 2018
2772018
Training Quantized Nets: A Deeper Understanding
H Li, S De, Z Xu, C Studer, H Samet, T Goldstein
Conference on Neural Information Processing Systems (NIPS), 2017
2552017
Universal Adversarial Training
A Shafahi, M Najibi, Z Xu, J Dickerson, LS Davis, T Goldstein
AAAI Conference on Artificial Intelligence (AAAI), 2020
2492020
Practical and Private (Deep) Learning without Sampling or Shuffling
P Kairouz, B McMahan, S Song, O Thakkar, A Thakurta, Z Xu
International Conference on Machine Learning (ICML), 2021
2082021
Exploiting Low-Rank Structure from Latent Domains for Domain Generalization
Z Xu, W Li, L Niu, D Xu
European Conference on Computer Vision (ECCV), 628-643, 2014
2042014
How to dp-fy ml: A practical guide to machine learning with differential privacy
N Ponomareva, H Hazimeh, A Kurakin, Z Xu, C Denison, HB McMahan, ...
Journal of Artificial Intelligence Research 77, 1113-1201, 2023
1792023
Training Shallow and Thin Networks for Acceleration via Knowledge Distillation with Conditional Adversarial Networks
Z Xu, YC Hsu, J Huang
arXiv preprint arXiv:1709.00513, 2017
172*2017
Domain Generalization and Adaptation using Low Rank Exemplar SVMs
W Li, Z Xu, D Xu, D Dai, LV Gool
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2017
1422017
Adaptive ADMM with Spectral Penalty Parameter Selection
Z Xu, MAT Figueiredo, T Goldstein
International Conference on Artificial Intelligence and Statistics (AISTATS), 2017
1362017
The Impact of Neural Network Overparameterization on Gradient Confusion and Stochastic Gradient Descent
KA Sankararaman, S De, Z Xu, WR Huang, T Goldstein
International Conference on Machine Learning (ICML), 2020
1242020
Stabilizing Adversarial Nets With Prediction Methods
A Yadav, S Shah, Z Xu, D Jacobs, T Goldstein
International Conference on Learning Representations (ICLR), 2018
1182018
Local-Global Knowledge Distillation in Heterogeneous Federated Learning with Non-IID Data
D Yao, W Pan, Y Dai, Y Wan, X Ding, H Jin, Z Xu, L Sun
arXiv preprint arXiv:2107.00051, 2021
96*2021
Adaptive Consensus ADMM for Distributed Optimization
Z Xu, G Taylor, H Li, M Figueiredo, X Yuan, T Goldstein
International Conference on Machine Learning (ICML), 2017
892017
Federated Learning of Gboard Language Models with Differential Privacy
Z Xu, Y Zhang, G Andrew, CA Choquette-Choo, P Kairouz, HB McMahan, ...
arXiv preprint arXiv:2305.18465, 2023
872023
GradInit: Learning to Initialize Neural Networks for Stable and Efficient Training
C Zhu, R Ni, Z Xu, K Kong, WR Huang, T Goldstein
Conference on Neural Information Processing Systems (NeurIPS), 2021
802021
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