Recent Advance of Two-Sample Testing and Its Application in AI Security
Recent Advance of Two-Sample Testing and Its Application in AI Security
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, the speaker will introduce one advance in the two-sample testing field: Two-sample testing under high dimensionality. The speaker will 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.
About the speaker
Lecturer, University of Melbourne
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 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 top-tier journals, such as JMLR, IEEE-TPAMI, IEEE-TNNLS, and IEEE-TFS. 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.
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