| Are anchor points really indispensable in label-noise learning? X Xia, T Liu, N Wang, B Han, C Gong, G Niu, M Sugiyama NeurIPS 2019, 2019 | 417 | 2019 |
| Part-dependent label noise: Towards instance-dependent label noise X Xia, T Liu, B Han, N Wang, M Gong, H Liu, G Niu, D Tao, M Sugiyama NeurIPS 2020 (spotlight), 2020 | 329 | 2020 |
| Robust early-learning: Hindering the memorization of noisy labels X Xia, T Liu, B Han, C Gong, N Wang, Z Ge, Y Chang ICLR 2021, 2021 | 320 | 2021 |
| Selective-supervised contrastive learning with noisy labels S Li, X Xia, S Ge, T Liu CVPR 2022, 2022 | 245 | 2022 |
| Sample selection with uncertainty of losses for learning with noisy labels X Xia, T Liu, B Han, M Gong, J Yu, G Niu, M Sugiyama ICLR 2022, 2022 | 145 | 2022 |
| Moderate coreset: A universal method of data selection for real-world data-efficient deep learning X Xia, J Liu, J Yu, X Shen, B Han, T Liu ICLR 2023, 2023 | 81 | 2023 |
| Class2Simi: A noise reduction perspective on learning with noisy labels S Wu, X Xia, T Liu, B Han, M Gong, N Wang, H Liu, G Niu ICML 2021, 2021 | 77 | 2021 |
| Estimating noise transition matrix with label correlations for noisy multi-label learning S Li, X Xia, H Zhang, Y Zhan, S Ge, T Liu NeurIPS 2022 (spotlight), 2022 | 73 | 2022 |
| Extended T: Learning with mixed closed-set and open-set noisy labels X Xia, B Han, N Wang, J Deng, J Li, Y Mao, T Liu TPAMI 2023, 2023 | 63 | 2023 |
| HumanMAC: Masked motion completion for human motion prediction L Chen, J Zhang, Y Li, Y Pang, X Xia, T Liu ICCV 2023, 2023 | 62 | 2023 |
| Learning lightweight super-resolution networks with weight pruning X Jiang, N Wang, J Xin, X Xia, X Yang, X Gao Neural Network 2021, 2021 | 60 | 2021 |
| Harnessing out-of-distribution examples via augmenting content and style Z Huang, X Xia, L Shen, B Han, M Gong, C Gong, T Liu ICLR 2023, 2023 | 46 | 2023 |
| Objects in semantic topology S Yang, P Sun, Y Jiang, X Xia, R Zhang, Z Yuan, C Wang, P Luo, M Xu ICLR 2022, 2022 | 44 | 2022 |
| One shot learning as instruction data prospector for large language models Y Li, B Hui, X Xia, J Yang, M Yang, L Zhang, S Si, J Liu, T Liu, F Huang, ... ACL 2024, 2024 | 43* | 2024 |
| Combating noisy labels with sample selection by mining high-discrepancy examples X Xia, B Han, Y Zhan, J Yu, M Gong, C Gong, T Liu ICCV 2023, 2023 | 36 | 2023 |
| Robust generalization against photon-limited corruptions via worst-case sharpness minimization Z Huang, M Zhu, X Xia, L Shen, J Yu, C Gong, B Han, B Du, T Liu CVPR 2023, 2023 | 29 | 2023 |
| IDEAL: Influence-driven selective annotations empower in-context learners in large language models S Zhang, X Xia, Z Wang, L Chen, J Liu, Q Wu, T Liu ICLR 2024, 2024 | 26 | 2024 |
| A holistic view of label noise transition matrix in deep learning and beyond LIN Yong, R Pi, W Zhang, X Xia, J Gao, X Zhou, T Liu, B Han ICLR 2023 (spotlight), 2023 | 25* | 2023 |
| Out-of-distribution detection with an adaptive likelihood ratio on informative hierarchical vae Y Li, C Wang, X Xia, T Liu, X Miao, B An NeurIPS 2022, 2022 | 23 | 2022 |
| Out-of-distribution detection learning with unreliable out-of-distribution sources H Zheng, Q Wang, Z Fang, X Xia, F Liu, T Liu, B Han NeurIPS 2023, 2023 | 20 | 2023 |
Tongliang LiuDirector, Sydney AI Centre, University of Sydney & Mohamed bin Zayed University of AIVerified email at sydney.edu.au
