Machine Learning with Noisy Labels

12pm-1pm, Wednesday 14th Feb 2024

Presented by A/Prof. Tongliang Liu

Venue: Rm 277, Sydney Knowledge Hub

Abstract: With the rise of large AI models, data assets have gained increasing importance. Understanding how to identify and correct label errors in our datasets is crucial. This is because label errors are pervasive in the era of big data, and rectifying them can significantly enhance our knowledge. Moreover, large AI models are prone to overfitting label errors, which hinders their ability to generalize. In this talk, we will present typical approaches to handle label noise, such as extracting confident examples (indicating likely correct/incorrect labels) using deep network properties. Additionally, we will explore methods that focus on directly modelling the label noise, providing theoretical guarantees for designing statistically consistent algorithms. By illustrating the intuitions behind state-of-the-art techniques, we would equip researchers and practitioners with valuable insights into effectively managing label noise.

About the speaker

Tongliang Liu is an Associate Professor with the School of Computer Science and The Director of Sydney AI Centre at the University of Sydney. He is broadly interested in the fields of trustworthy machine learning and its interdisciplinary applications, with a particular emphasis on learning with noisy labels, adversarial learning, causal representation learning, transfer learning, unsupervised learning, and statistical deep learning theory. He has authored and co-authored more than 200 research articles including ICML, NeurIPS, ICLR, CVPR, ICCV, ECCV, AAAI, IJCAI, JMLR, and TPAMI. He is/was a (senior-) meta reviewer for many conferences, such as ICML, NeurIPS, ICLR, UAI, AAAI, IJCAI, and KDD, and was a notable AC for NeurIPS and ICLR. He is an Associate Editor of TMLR and is on the Editorial Boards of JMLR and MLJ. He is a recipient of CORE Award for Outstanding Research Contribution in 2024, the IEEE AI’s 10 to Watch Award in 2023, the Future Fellowship Award from Australian Research Council (ARC) in 2022, and the Discovery Early Career Researcher Award (DECRA) from ARC in 2018.