Yuxin Tian
My name is Yuxin Tian. I obtained my D.Eng. in Computer Science and Technology from Sichuan University, where I was advised by Prof. Jiancheng Lv and collaborated with Prof. Xi Peng. Before my graduate studies, I received my B.S. from the same university. My research interests center on LLM pre-training, efficient model architecture, and robust learning in open scenarios, with a focus on Multi-modal Learning, Large Language Models (LLMs), Multi-task Learning, and Learning with Noisy Labels.
Currently, I am a researcher at the pre-training team of inclusionAI, Ant Group.
Service:
- Conference: Reviewer of ICLR, NeurIPS, ICML, CVPR, ICCV, ECCV, AISTATS, AAAI, IJCAI, ACM MM, and etc.
- Journal: Reviewer of IEEE-TNNLS, IEEE-TCYB, IEEE-TSMC:Systems, and IEEE-TCE
News
| May 01, 2026 | One first-authored paper was accepted by Forty-Third International Conference on Machine Learning (ICML 2026). Congrats to all authors! |
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| Feb 22, 2026 | One co-authored papers have been accepted by The IEEE/CVF Conference on Computer Vision and Pattern Recognition 2026 (CVPR 2026). Congrats to all my co-authors! |
| Jan 26, 2026 | One co-authored papers have been accepted by The Fourteenth International Conference on Learning Representations (ICLR 2026). Congrats to all my co-authors! |
| Feb 27, 2025 | Two co-authored papers have been accepted by Conference on Computer Vision and Pattern Recognition 2025 (CVPR 2025). Congrats to all my co-authors! |
| Feb 05, 2025 | One co-fisrt-authored paper has been accepted by IEEE Transactions on Neural Networks and Learning Systems (IEEE T-NNLS). Congrats to all my co-authors! |
Selected publications
- E-3SFC: Communication-Efficient Federated Learning With Double-Way Features SynthesizingIEEE Transactions on Neural Networks and Learning Systems, 2025
- An Empirical Study of Parameter Efficient Fine-tuning on Vision-Language Pre-train ModelIn IEEE International Conference on Multimedia and Expo, ICME 2024, Niagara Falls, ON, Canada, July 15-19, 2024, 2024
- UNITE: Multitask Learning With Sufficient Feature for Dense PredictionIEEE Trans. Syst. Man Cybern. Syst., 2024