Linwei Tao <br>陶林伟

Linwei Tao
陶林伟

University of Sydney

Hi there! I am a Ph.D. student at the University of Sydney, supervised by A/Prof. Chang Xu. I am honored to collaborate with Prof. Philip Torr at Oxford, Dr. Minjing Dong at CityU and Dr. Tao Huang at SJTU. I received my bachelor’s degree from HUST in 2017. My research focuses on Confidence Calibration and Uncertainty Estimation in Deep Learning and Large (Vision) Language Models.

Google Scholar GitHub CV

🔥 News

  • 2026.02: I completed my Research Scientist Internship at Google Research Australia! Great thanks to my mentors Prof. Trevor Cohn and Dr. Hisham Husain.
  • 2025.11: 🚀🚀 I start my journey at Google as a Research Scientist Intern!
  • 2025.06: 🎉🎉 I’m honored to have been elected to the Student Committee of the AAAI 2026.
  • 2025.05: 🎉🎉 One paper is accepted by ICML 2025.
  • 2025.02: 🎉🎉 One paper is accepted by CVPR 2025.
  • 2025.01: 🎉🎉 One paper is accepted by ICLR 2025.
  • 2024.11: 🎉🎉 One paper is accepted by AAAI 2025.

📝 Publications

WATS: Wavelet-Aware Temperature Scaling for Reliable Graph Neural Networks
Xiaoyang Li, Linwei Tao, Haohui Lu, Minjing Dong, Junbin Gao, Chang Xu.
ICLR, 2026.
Task-Adaptive Continual Learning of Vision Language Models via Prototype Routing and Prompt
Lei Pan, Z. Lu, Yuan Zheng, C. Yan, H. Wen, Linwei Tao, Chang Xu.
Neurocomputing, 2026.
Confidence Calibration under Ambiguous Ground Truth
Linwei Tao, Haoyang Luo, Minjing Dong, Chang Xu.
Preprint, 2026. [Paper] [Code]
Beyond One-Hot Labels: Semantic Mixing for Model Calibration
Haoyang Luo, Linwei Tao, Minjing Dong, Chang Xu.
ICML, 2025.
Uncertainty Weighted Gradients for Model Calibration
Jinxu Lin, Linwei Tao, Minjing Dong, Chang Xu.
CVPR, 2025.
Diffusion Attribution Score: Evaluating Training Data Influence in Diffusion Model
Jinxu Lin, Linwei Tao, Minjing Dong, Chang Xu.
ICLR, 2025. (Spotlight, 5.1% of 11,500 submissions) [Paper]
Feature Clipping for Uncertainty Calibration
Linwei Tao, Minjing Dong, Chang Xu.
AAAI, 2025. [Paper] [Code]
KG-UQ: Knowledge Graph-Based Uncertainty Quantification for Long Text in Large Language Models
Yingqing Yuan, Linwei Tao, Haohui Lu, Matloob Khushi, Imran Razzak, Mark Dras, Jian Yang, Usman Naseem.
WWW Workshop, 2025.
Can Large Language Models Express Uncertainty Like Human?
Linwei Tao, Yi-Fan Yeh, Bo Kai, Minjing Dong, Tao Huang, Tom A. Lamb, Jialin Yu, Philip H.S. Torr, Chang Xu.
Preprint, 2025. [Paper]
Revisiting Uncertainty Estimation and Calibration of Large Language Models
Linwei Tao, Yi-Fan Yeh, Minjing Dong, Tao Huang, Philip Torr, Chang Xu.
Preprint, 2025. [Paper]
Sample Margin-Aware Recalibration of Temperature Scaling
Haolan Guo, Linwei Tao, Haoyang Luo, Minjing Dong, Chang Xu.
Preprint, 2025. [Paper]
Mitigating Object Hallucinations in Large Vision-Language Models via Attention Calibration
Younan Zhu, Linwei Tao, Minjing Dong, Chang Xu.
Preprint, 2025. [Paper]
Consistency Calibration: Improving Uncertainty Calibration via Consistency among Perturbed Neighbors
Linwei Tao, Haolan Guo, Minjing Dong, Chang Xu.
Preprint, 2025. [Paper]
A Benchmark Study on Calibration
Linwei Tao, Younan Zhu, Haolan Guo, Minjing Dong, Chang Xu.
ICLR, 2024. [Paper] [Dataset] [Code] [Project Page] [Poster] [Slides]
GraphFusion: Integrating Multi-Level Semantic Information with Graph Computing for Enhanced 3D Instance Segmentation
Lei Pan, Wuyang Luan, Yuan Zheng, Junhui Li, Linwei Tao, Chang Xu.
Neurocomputing, 2024. [Paper] [Code]
Visual Imitation Learning with Calibrated Contrastive Representation
Yunke Wang, Linwei Tao, Bo Du, Yutian Lin, Chang Xu.
Preprint, 2024. [Paper]
Dual Focal Loss for Calibration
Linwei Tao, Minjing Dong, Chang Xu.
ICML, 2023. [Paper] [Code] [Poster] [Slides]
Calibrating a Deep Neural Network with Its Predecessors
Linwei Tao, Minjing Dong, Daochang Liu, Changming Sun, Chang Xu.
IJCAI, 2023. [Paper] [Code] [Playground] [Slides]

No matching publications.

📖 Education

  • 2023.10 - Present, Ph.D in Computer Vision, University of Sydney
  • 2021.10 - 2023.03, M.Phil in Computer Vision, University of Sydney
  • 2020.02 - 2021.07, Master of Data Science, University of Sydney
  • 2013.09 - 2017.06, B.Eng in Communication and Engineering, Huazhong University of Science and Technology

🧑‍🏫 Teaching

  • 2022 S1, 2023 S1, 2024 S1, 2025 S1, COMP5329 Deep Learning
  • 2025 S2, COMP5318 Machine Learning and Data Mining
  • 2022 S2, 2023 S2, HTIN5005 Applied Healthcare Data Science
  • 2024 S1, 2024 S2, 2025 S1, 2025 S2, BUSS6002 Data Science in Business
  • 2024 S2, 2025 S2, QBUS5010 Intro to Dashboarding and Data Visualisation
  • 2024 S2, OCMP5329 Deep Learning (Online)

💼 Internship

  • 2025.11 - 2026.02, Research Scientist Intern, Google Research Australia
  • 2015.06 - 2015.09, IOS developer Intern, Ctrip.com
  • 2014.06 - 2014.09, Frontend developer Intern, Sunallies.com

🎖 Honors and Awards

  • 2025.06, Student Committee of the AAAI 2026 (1 of 3 in the world)
  • 2024.05, Google Cloud Research Credits Award ($2340 AUD)
  • 2023.06, International Tuition Fee Scholarship, University of Sydney
  • 2023.05, Faculty of Engineering Research Support Scholarship, University of Sydney
  • 2015.09, Excellent Student Cadre, Huazhong University of Science and Technology
  • 2014.06, Science and Technology Scholarship, Huazhong University of Science and Technology

💻 Academic Service

  • Conference Reviewer: ICML, NeuIPS, AAAI, ICLR, CVPR, ICML, IJCAI, ACMMM, AISTATS
  • Journal Reviewer: T-MM, TMLR, DAMI, TPAMI
  • Organizer: Sydney AI Meetup