- The University of Melbourne, Australia
Two-sample tests, as one of the most important hypothesis testing techniques, ask, "given samples from each, are these two populations the same?" For instance, one might wish to know whether a treatment and control group differ. With very low-dimensional data and/or strong parametric assumptions, methods such as t-tests or Kolmogorov-Smirnov tests are widespread. Recent work in statistics and machine learning has sought tests that cover situations not well-handled by these classic methods, providing tools useful in adversarial machine learning, causal discovery, generative modeling, and more. In this talk, I will introduce recent advance in the two-sample testing field and present how to use advanced tests to defend against the adversarial attacks, which justified the significance of two-sample testing in the AI security area.
Dr Feng Liu is a machine learning researcher with research interests in hypothesis testing and trustworthy machine learning. Currently, he is a Lecturer at The University of Melbourne, Australia, a Visiting Scientist at RIKEN-AIP, Japan, and a Visiting Research Fellow at AAII, UTS, Australia. He received his PhD degree in Computer Science from UTS in 2020. He has served as SPC members for IJCAI and ECAI, and PC members for NeurIPS, ICML, ICLR, AISTATS, ACML, and KDD. He also serves as a reviewer for many academic journals, such as JMLR, IEEE-TPAMI, IEEE-TNNLS, IEEE-TFS, and AMM. He has received the Outstanding Paper Award of NeurIPS (2022), the Outstanding Reviewer Award of NeurIPS (2021), the Outstanding Reviewer Award of ICLR (2021), and UTS Best Thesis Award (Dean’s List). Until now, he has published over 50 papers in high-quality journals or conferences, such as Nature Communications, IEEE-TPAMI, IEEE-TNNLS, IEEE-TFS, NeurIPS, ICML, ICLR, KDD, AAAI and IJCAI.