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The TrustML Young Scientist Seminars (TrustML YSS) started from January 28, 2022.
The TrustML YSS is a video series that features young scientists giving talks and discoveries in relation with Trustworthy Machine Learning.
Timetable for the TrustML YSS online seminars from Jan. to Feb. 2023.
For more information please see the following site.
This network is funded by RIKEN-AIP's subsidy and JST, ACT-X Grant Number JPMJAX21AF, Japan.
【The 54th Seminar】
Date and Time: February 8th 2:00 pm - 3:00 pm(JST)
Venue: Zoom webinar
Speaker: Zhen Fang (University of Technology Sydney)
Title: Understanding Generalized Out-of-Distribution Detection: A Theoretical View
Out-of-distribution (OOD) detection is vital to ensuring the safety and reliability of artificial intelligence systems. It is a novel but trending area in machine learning and artificial intelligence. OOD detection was proposed in 2017 and since then has shown great potential to ensure the reliable deployment of machine learning models in the real world. In the past few years, a rich line of algorithms have been developed to empirically address the OOD detection problem. However, very few works study the fundamental principles of OOD detection, which hinders the rigorous path forward for the field. In this talk, we will introduce the novel progress related to theoretical understanding of OOD detection.
Dr Zhen Fang is currently a Research Fellow at the Australian Artificial Intelligence Institute, University of Technology Sydney (UTS), working with Prof. Jie Lu. He received his master degree in pure mathematics from Xiamen University (2014-2017), working with Prof. Bo Guan. He received his Ph.D degree in artificial intelligence from UTS (2018-2022), working with Prof. Jie Lu. His research interests include transfer learning, statistical learning theory and out-of-distribution learning, and his works have been published in leading journals and conferences e.g., NeurIPS, ICML and IEEE-TPAMI. Recently, Zhen also received the Outstanding Paper Award in NeurIPS 2022 for his work related to out-of-distribution learning.
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Please see the URL below.
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