Revealing Causal Information from Data

Speaker:  TongLiang Liu, The University of Sydney.

Time:        10:00 am, Jan.3rd.

Location: SIST 1A-200

Host:        Jingya Wang

Abstract:

Many tasks in sciences or engineering require the underlying causal information. Since it is typically expensive and time-consuming to conduct randomized experiments, there has been significant attention towards revealing causal relations through the analysis of purely observational data, commonly known as causal discovery. Over the past few years, with the rapid development of big data, causal discovery is facing great opportunities and challenges. In this talk, I will first introduce some classical causal discovery methods, including PC algorithm and LiNGAM, which has been successfully applied to the cases without latent variable. However, in complex systems, we typically fail to collect and measure all task-relevant variables. In the second part of the talk, I will focus on causal structure recovery in the presence of latent variables. In particular, I will briefly review some researches in this line and introduce our recent work, the latter requires less restrictive assumption and hence can handle more general cases.

Bio:

Tongliang Liu is 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, TPAMI, and JMLR. He is/was a senior meta reviewer for many conferences, such as NeurIPS, ICLR, AAAI, and IJCAI. He is a co-Editor-in-Chief for Neural Networks, an Associate Editor of IEEE TPAMI, TIP, TMLR, and ACM Computing Surveys, 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 2022, the Future Fellowship Award from Australian Research Council (ARC) in 2022, the Top-40 Early Achievers by The Australian in 2020, and the Discovery Early Career Researcher Award (DECRA) from ARC in 2018.