| 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 | 6120 | 2019 |
| 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 | 2119 | 2018 |
| 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 | 1486 | 2019 |
| 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 | 362 | 2021 |
| 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 | 314 | 2016 |
| Towards Perceptual Image Dehazing by Physics-based Disentanglement and Adversarial Training X Yang, Z Xu, J Luo AAAI Conference on Artificial Intelligence (AAAI), 2018 | 263 | 2018 |
| 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 | 244 | 2017 |
| Universal Adversarial Training A Shafahi, M Najibi, Z Xu, J Dickerson, LS Davis, T Goldstein AAAI Conference on Artificial Intelligence (AAAI), 2020 | 232 | 2020 |
| 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 | 204 | 2014 |
| 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 | 171* | 2017 |
| 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 | 164 | 2021 |
| 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 | 135 | 2017 |
| Adaptive ADMM with Spectral Penalty Parameter Selection Z Xu, MAT Figueiredo, T Goldstein International Conference on Artificial Intelligence and Statistics (AISTATS), 2017 | 125 | 2017 |
| 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 | 113 | 2020 |
| Stabilizing Adversarial Nets With Prediction Methods A Yadav, S Shah, Z Xu, D Jacobs, T Goldstein International Conference on Learning Representations (ICLR), 2018 | 113 | 2018 |
| 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 | 109 | 2023 |
| 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 | 87 | 2017 |
| Learning to Cluster for Proposal-Free Instance Segmentation YC Hsu, Z Xu, Z Kira, J Huang 2018 International Joint Conference on Neural Networks (IJCNN), 1-8, 2018 | 69 | 2018 |
| 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 | 68 | 2021 |
| Mining Visualness Z Xu, XJ Wang, CW Chen IEEE International Conference on Multimedia and Expo (ICME), 2013 | 65* | 2013 |
Tom GoldsteinVolpi-Cupal Professor of Computer Science, University of MarylandVerified email at cs.umd.edu
