| Dual t: Reducing estimation error for transition matrix in label-noise learning Y Yao, T Liu, B Han, M Gong, J Deng, G Niu, M Sugiyama NeurIPS, 2020 | 270 | 2020 |
| Instance-dependent label-noise learning under a structural causal model Y Yao, T Liu, M Gong, B Han, G Niu, K Zhang NeurIPS, 2021 | 79 | 2021 |
| Rethinking class-prior estimation for positive-unlabeled learning Y Yao, T Liu, B Han, M Gong, G Niu, M Sugiyama, D Tao ICLR, 2022 | 33* | 2022 |
| Improving Non-Transferable Representation Learning by Harnessing Content and Style Z Hong, Z Wang, L Shen, Y Yao, Z Huang, S Chen, C Yang, M Gong, ... ICLR (spotlight), 2024 | 17 | 2024 |
| CS-Isolate: Extracting Hard Confident Examples by Content and Style Isolation Y Lin, Y Yao, X Shi, M Gong, X Shen, D Xu, T Liu NeurIPS, 2023 | 13 | 2023 |
| Understanding how pretraining regularizes deep learning algorithms Y Yao, B Yu, C Gong, T Liu IEEE TNNLS, 2021 | 13 | 2021 |
| Multi-scale cooperative multimodal transformers for multimodal sentiment analysis in videos L Ma, Y Yao, T Liang, T Liu Australasian Joint Conference on Artificial Intelligence, 281-297, 2024 | 11 | 2024 |
| Which is Better for Learning with Noisy Labels: The Semi-supervised Method or Modeling Label Noise? Y Yao, M Gong, Y Du, J Yu, B Han, K Zhang, T Liu ICML, 2023 | 10 | 2023 |
| PNT-Edge: Towards Robust Edge Detection with Noisy Labels by Learning Pixel-level Noise Transitions W Xuan, S Zhao, Y Yao, J Liu, T Liu, Y Chen, B Du, D Tao ACM Multimedia, 2023 | 6 | 2023 |
| Enhancing Contrastive Learning for Ordinal Regression via Ordinal Content Preserved Data Augmentation J Zheng, Y Yao, B Han, D Wang, T Liu ICLR, 2024 | 5 | 2024 |
| Causality Encourages the Identifiability of Instance-Dependent Label Noise Y Yao, T Liu, M Gong, B Han, G Niu, K Zhang Machine Learning for Causal Inference, 2023 | 5* | 2023 |
| Learning the Latent Causal Structure for Modeling Label Noise Y Lin, Y Yao, T Liu NeurIPS, 2024 | 3 | 2024 |
| ProtoSimi: label correction for fine-grained visual categorization J Shen, Y Yao, S Huang, Z Wang, J Zhang, R Wang, J Yu, T Liu Machine Learning 113 (4), 1903-1920, 2023 | 3 | 2023 |
| StarmapVis: An interactive and narrative visualisation tool for single-cell and spatial data S Ma, X Fang, Y Yao, J Li, DC Morgan, Y Xia, CSM Kwok, MCK Lo, ... Computational and Structural Biotechnology Journal 21, 1598-1605, 2023 | 3* | 2023 |
| Flow: A Modular Approach to Automated Agentic Workflow Generation B Niu, Y Song, K Lian, Y Shen, Y Yao, K Zhang, T Liu ICLR, 2025 | 1 | 2025 |
| Do We Need to Penalize Variance of Losses for Learning with Label Noise? Y Lin, Y Yao, Y Du, J Yu, B Han, M Gong, T Liu arXiv preprint arXiv:2201.12739, 2022 | 1 | 2022 |
| A Robust Method to Discover Causal or Anticausal Relation by Noise Injection Y Yao, Y Zhou, M Gong, K Zhang, T Liu ICLR, 2025 | | 2025 |
| A Conditional Independence Test in the Presence of Discretization B Sun, Y Yao, H Hao, Y Qiu, K Zhang ICLR, 2025 | | 2025 |
| Trustworthy and Responsible AI for Information and Knowledge Management System H Chen, J Zhuang, Y Yao, W Jin, H Wang, Y Xie, CH Chi, KKR Choo Proceedings of the 33rd ACM International Conference on Information and …, 2024 | | 2024 |
| Identifying Latent State-Transition Processes for Individualized Reinforcement Learning Y Sun, B Huang, Y Yao, D Zeng, X Dong, R Legaspi, K Ikeda, P Spirtes, ... NeurIPS, 2024 | | 2024 |
Tongliang LiuDirector, Sydney AI Centre, University of Sydney & Mohamed bin Zayed University of AIEmail verificata su sydney.edu.au