Revealing Causal Information from Data - DARE Symposium 2024 скачать в хорошем качестве

Revealing Causal Information from Data - DARE Symposium 2024

DARE Chief Investigator Presentation by A/Prof Tongliang Liu DARE Symposium February 2024 Abstract: Many tasks in sciences or engineering require the underlying causal information. Since it is typically expensive and time-consuming to conduct randomised 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 research in this line and introduce our recent work, the latter requires less restrictive assumption and hence can handle more general cases. A/Prof Tongliang Liu: 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 a co-Editor-in-Chief for Neural Networks, an Associate Editor of TMLR and ACM Computing Surveys, and is on the Editorial Boards of JMLR and MLJ.