| 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 | 333 | 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 | 108 | 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 | 44* | 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 | 36 | 2024 |
| Flow: Modularized Agentic Workflow Automation B Niu, Y Song, K Lian, Y Shen, Y Yao, K Zhang, T Liu ICLR, 2025 | 27* | 2025 |
| 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 | 20 | 2023 |
| Learning the Latent Causal Structure for Modeling Label Noise Y Lin, Y Yao, T Liu NeurIPS, 2024 | 18 | 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 | 18* | 2023 |
| Understanding how pretraining regularizes deep learning algorithms Y Yao, B Yu, C Gong, T Liu IEEE TNNLS, 2021 | 18 | 2021 |
| Enhancing Contrastive Learning for Ordinal Regression via Ordinal Content Preserved Data Augmentation J Zheng, Y Yao, B Han, D Wang, T Liu ICLR, 2024 | 16 | 2024 |
| 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 | 15 | 2024 |
| 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 | 9 | 2023 |
| 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 | 7 | 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 | 6* | 2023 |
| Chain-of-Focus Prompting: Leveraging Sequential Visual Cues to Prompt Large Autoregressive Vision Models J Zheng, J Shen, Y Yao, M Wang, Y Yang, D Wang, T Liu ICLR, 2025 | 5 | 2025 |
| 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 |
| Ranked from within: Ranking large multimodal models for visual question answering without labels W Tu, W Deng, D Campbell, Y Yao, J Zheng, T Gedeon, T Liu arXiv preprint arXiv:2412.06461, 2024 | 4 | 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 | 4 | 2024 |
| 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 | 4 | 2022 |
| Aligning What Matters: Masked Latent Adaptation for Text-to-Audio-Video Generation J Zheng, S Pan, Y Yao, Z Wang, D Wang, T Liu NeurIPS, 2025 | 2 | 2025 |
Tongliang LiuDirector, Sydney AI Centre, University of Sydney & Mohamed bin Zayed University of AIVerified email at sydney.edu.au
