| Learning with noisy labels revisited: A study using real-world human annotations J Wei*, Z Zhu*, H Cheng, T Liu, G Niu, Y Liu ICLR 2022, 2022 | 208 | 2022 |
| Learning with Instance-Dependent Label Noise: A Sample Sieve Approach H Cheng*, Z Zhu*, X Li, Y Gong, X Sun, Y Liu ICLR 2021, 2021 | 199 | 2021 |
| A Second-Order Approach to Learning with Instance-Dependent Label Noise Z Zhu, T Liu, Y Liu CVPR 2021 (oral), 2021 | 132 | 2021 |
| Federated bandit: A gossiping approach Z Zhu, J Zhu, J Liu, Y Liu Proceedings of the 2021 ACM SIGMETRICS/International Conference on …, 2021 | 86 | 2021 |
| Clusterability as an alternative to anchor points when learning with noisy labels Z Zhu, Y Song, Y Liu ICML 2021 139, 12912-12923, 2021 | 84 | 2021 |
| Detecting corrupted labels without training a model to predict Z Zhu, Z Dong, Y Liu International conference on machine learning, 27412-27427, 2022 | 58 | 2022 |
| BLOT: Bandit learning-based offloading of tasks in fog-enabled networks Z Zhu, T Liu, Y Yang, X Luo IEEE Transactions on Parallel and Distributed Systems 30 (12), 2636-2649, 2019 | 52 | 2019 |
| Beyond images: Label noise transition matrix estimation for tasks with lower-quality features Z Zhu, J Wang, Y Liu International Conference on Machine Learning, 27633-27653, 2022 | 39 | 2022 |
| Mitigating Memorization of Noisy Labels via Regularization between Representations H Cheng*, Z Zhu*, X Sun, Y Liu ICLR 2023, 2023 | 34* | 2023 |
| The rich get richer: Disparate impact of semi-supervised learning Z Zhu, T Luo, Y Liu ICLR 2022, 2022 | 33 | 2022 |
| To aggregate or not? learning with separate noisy labels J Wei, Z Zhu, T Luo, E Amid, A Kumar, Y Liu Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and …, 2023 | 22 | 2023 |
| Time reusing in D2D-enabled cooperative networks Z Zhu, S Jin, Y Yang, H Hu, X Luo IEEE Transactions on Wireless Communications 17 (5), 3185-3200, 2018 | 20 | 2018 |
| Learn and pick right nodes to offload Z Zhu, T Liu, S Jin, X Luo 2018 IEEE Global Communications Conference (GLOBECOM), 1-6, 2018 | 15 | 2018 |
| Weak proxies are sufficient and preferable for fairness with missing sensitive attributes Z Zhu, Y Yao, J Sun, H Li, Y Liu International Conference on Machine Learning, 43258-43288, 2023 | 13 | 2023 |
| Optimal task offloading in fog-enabled networks via index policies F Yang, Z Zhu, S Zhao, Y Yang, X Luo 2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP …, 2018 | 12 | 2018 |
| Policy Learning Using Weak Supervision J Wang*, H Guo*, Z Zhu*, Y Liu Advances in Neural Information Processing Systems 34, 19960-19973, 2021 | 11 | 2021 |
| CSI based high accuracy device free passive localization system Y Liu, W Xiong, Z Zhu, S Li 2018 IEEE 88th Vehicular Technology Conference (VTC-Fall), 1-5, 2018 | 11 | 2018 |
| Online optimal task offloading with one-bit feedback S Zhao, Z Zhu, F Yang, X Luo 2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP …, 2018 | 8 | 2018 |
| Alternate distributed allocation of time reuse patterns in Fog-enabled cooperative D2D networks S Jin, Z Zhu, Y Yang, MT Zhou, X Luo 2017 IEEE Fog World Congress (FWC), 1-6, 2017 | 8 | 2017 |
| Unmasking and improving data credibility: A study with datasets for training harmless language models Z Zhu, J Wang, H Cheng, Y Liu arXiv preprint arXiv:2311.11202, 2023 | 6 | 2023 |
Yang LiuComputer Science and Engineering, UC Santa Cruz在 ucsc.edu 的电子邮件经过验证
