| Learning with Noisy Labels via Sparse Regularization X Zhou, X Liu, C Wang, D Zhai, J Jiang, X Ji ICCV 2021, 2021 | 59 | 2021 |
| Asymmetric Loss Functions for Learning with Noisy Labels X Zhou, X Liu, J Jiang, X Gao, X Ji Proceedings of the 38th International Conference on Machine Learning 139 …, 2021 | 48 | 2021 |
| Asymmetric loss functions for noise-tolerant learning: Theory and applications X Zhou, X Liu, D Zhai, J Jiang, X Ji IEEE Transactions on Pattern Analysis and Machine Intelligence 45 (7), 8094-8109, 2023 | 20 | 2023 |
| Learning Towards the Largest Margins X Zhou, X Liu, D Zhai, J Jiang, X Gao, X Ji The Tenth International Conference on Learning Representations, 2022 | 12 | 2022 |
| No one idles: Efficient heterogeneous federated learning with parallel edge and server computation F Zhang, X Liu, S Lin, G Wu, X Zhou, J Jiang, X Ji International Conference on Machine Learning, 41399-41413, 2023 | 7 | 2023 |
| Prototype-Anchored Learning for Learning with Imperfect Annotations X Zhou, X Liu, D Zhai, J Jiang, X Gao, X Ji Proceedings of the 39th International Conference on Machine Learning, 27245 …, 2022 | 6 | 2022 |
| Resmooth: Detecting and utilizing ood samples when training with data augmentation C Wang, J Jiang, X Zhou, X Liu IEEE Transactions on Neural Networks and Learning Systems, 2022 | 4 | 2022 |
| Zero-Mean Regularized Spectral Contrastive Learning: Implicitly Mitigating Wrong Connections in Positive-Pair Graphs X Zhou, X Liu, F Zhang, G Wu, D Zhai, J Jiang, X Ji The Twelfth International Conference on Learning Representations, 2024 | 1 | 2024 |
| On the dynamics under the unhinged loss and beyond X Zhou, X Liu, H Wang, D Zhai, J Jiang, X Ji The Journal of Machine Learning Research 24 (1), 18048-18109, 2023 | 1 | 2023 |
| Neural Field Classifiers via Target Encoding and Classification Loss X Yang, Z Xie, X Zhou, B Liu, B Liu, Y Liu, H Wang, Y CAI, M Sun The Twelfth International Conference on Learning Representations, 2024 | | 2024 |
| Variance-enlarged Poisson Learning for Graph-based Semi-Supervised Learning with Extremely Sparse Labeled Data X Zhou, X Liu, H Yu, J Wang, Z Xie, J Jiang, X Ji The Twelfth International Conference on Learning Representations, 2024 | | 2024 |
| Mix-DDPM: Enhancing Diffusion Models through Fitting Mixture Noise with Global Stochastic Offset H Wang, D Zhai, X Zhou, J Jiang, X Liu ACM Transactions on Multimedia Computing, Communications and Applications, 0 | | |
| GM-DDPM: Denoising diffusion probabilistic models with Gaussian Mixture Noise H Wang, X Liu, X Zhou, J Jiang, D Zhai, W Gao | | |
| Parallel Federated Learning over Heterogeneous Devices F Zhang, X Liu, S Lin, G Wu, X Zhou, J Jiang, X Ji | | |
| On the Dynamics under the Averaged Sample Margin Loss and Beyond X Zhou, X Liu, H Wang, D Zhai, J Jiang, X Ji | | |
