报告题目: Recent Advances and Applications of Two-sample Testing

报告时间：2022年9月15日（星期四），上午10:50-11:50

腾讯会议：444-430-635

报告简介：Two-sample tests 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 machine learning for domain adaptation, causal discovery, generative modeling, fairness, adversarial learning, and more. In this talk, I will introduce two advances in the two-sample testing field: Two-sample testing under high dimensionality and few observations. I also present how to use advanced two-sample tests to defend against the adversarial attacks, which justified the significance of two-sample testing in the AI security area.

报告人简介：Dr. Feng Liu is a machine learning researcher with research interests in hypothesis testing and trustworthy machine learning. His long-term goal is to develop trustworthy intelligent systems that can learn reliable knowledge from massive related but different domains automatically. Currently, he is a Lecturer at the University of Melbourne, Australia, and a Visiting Scientist at RIKEN-AIP, Japan. He was the recipient of Australian Laureate postdoctoral fellowship.

He has served as program committee (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 Reviewer Award of NeurIPS (2021), the Outstanding Reviewer Award of ICLR (2021), the UTS-FEIT HDR Research Excellence Award (2019), the Best Student Paper Award of FUZZ-IEEE (2019) and the UTS Research Publication Award (2018). Until now, he has published over 40 papers in high-quality journals or conferences, such as IEEE-TPAMI, IEEE-TNNLS, IEEE-TFS, NeurIPS, ICML, ICLR, KDD, AAAI and IJCAI.