| Model compression with adversarial robustness: A unified optimization framework S Gui, H Wang, H Yang, C Yu, Z Wang, J Liu NeurIPS 2019, 1285-1296, 2019 | 162 | 2019 |
| AugMax: Adversarial Composition of Random Augmentations for Robust Training H Wang, C Xiao, J Kossaifi, Z Yu, A Anandkumar, Z Wang Advances in Neural Information Processing Systems 34, 2021 | 105 | 2021 |
| Autogan-distiller: Searching to compress generative adversarial networks Y Fu, W Chen, H Wang, H Li, Y Lin, Z Wang ICML 2020, 2020 | 102 | 2020 |
| Triple wins: Boosting accuracy, robustness and efficiency together by enabling input-adaptive inference TK Hu, T Chen, H Wang, Z Wang ICLR 2020, 2020 | 97 | 2020 |
| Once-for-All Adversarial Training: In-Situ Tradeoff between Robustness and Accuracy for Free H Wang, T Chen, S Gui, TK Hu, J Liu, Z Wang NeurIPS 2020, 2020 | 84* | 2020 |
| Privacy-Preserving Deep Action Recognition: An Adversarial Learning Framework and A New Dataset Z Wu, H Wang, Z Wang, Z Wang, H Jin IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020 | 80 | 2020 |
| GAN Slimming: All-in-One GAN Compression by A Unified Optimization Framework H Wang, S Gui, H Yang, J Liu, Z Wang ECCV 2020, 2020 | 68 | 2020 |
| Taxonomy of machine learning safety: A survey and primer S Mohseni, H Wang, C Xiao, Z Yu, Z Wang, J Yadawa ACM Computing Surveys 55 (8), 1-38, 2022 | 53* | 2022 |
| Partial and asymmetric contrastive learning for out-of-distribution detection in long-tailed recognition H Wang, A Zhang, Y Zhu, S Zheng, M Li, AJ Smola, Z Wang International Conference on Machine Learning, 23446-23458, 2022 | 43 | 2022 |
| Efficient split-mix federated learning for on-demand and in-situ customization J Hong, H Wang, Z Wang, J Zhou International Conference on Learning Representations, 2022 | 43 | 2022 |
| Federated robustness propagation: sharing adversarial robustness in heterogeneous federated learning J Hong, H Wang, Z Wang, J Zhou Proceedings of the AAAI Conference on Artificial Intelligence 37 (7), 7893-7901, 2023 | 41* | 2023 |
| Removing batch normalization boosts adversarial training H Wang, A Zhang, S Zheng, X Shi, M Li, Z Wang International Conference on Machine Learning, 23433-23445, 2022 | 40 | 2022 |
| I Am Going MAD: Maximum Discrepancy Competition for Comparing Classifiers Adaptively H Wang, T Chen, Z Wang, K Ma ICLR 2020, 2020 | 21 | 2020 |
| Troubleshooting Blind Image Quality Models in the Wild Z Wang, H Wang, T Chen, Z Wang, K Ma CVPR 2021, 2021 | 20 | 2021 |
| Real-time rogue ONU identification with 1D-CNN-based optical spectrum analysis for secure PON Y Li, N Hua, C Zhao, H Wang, R Luo, X Zheng 2019 Optical Fiber Communications Conference and Exhibition (OFC), 1-3, 2019 | 12 | 2019 |
| Learning model-based privacy protection under budget constraints J Hong, H Wang, Z Wang, J Zhou Proceedings of the AAAI Conference on Artificial Intelligence 35 (9), 7702-7710, 2021 | 11 | 2021 |
| Trap and Replace: Defending Backdoor Attacks by Trapping Them into an Easy-to-Replace Subnetwork H Wang, J Hong, A Zhang, J Zhou, Z Wang NeurIPS 2022, 2022 | 10 | 2022 |
| How robust is your fairness? evaluating and sustaining fairness under unseen distribution shifts H Wang, J Hong, J Zhou, Z Wang Transactions on machine learning research 2023, 2023 | 9 | 2023 |
| Turning the curse of heterogeneity in federated learning into a blessing for out-of-distribution detection S Yu, J Hong, H Wang, Z Wang, J Zhou 2023 International Conference on Learning Representations, 2023 | 7 | 2023 |
| Troubleshooting image segmentation models with human-in-the-loop H Wang, T Chen, Z Wang, K Ma Machine Learning 112, 1033-1051, 2022 | 7* | 2022 |
