报告题目：Robust machine learning for responsible AI

报告人：王晋东 主管研究员

单位：微软亚洲研究院

报告时间：2023年3月12日（周日）下午2点

报告地点：翡翠科教楼A座902室

报告摘要：

While the increasing popularity of artificial intelligence (AI) has enriched our daily lives, its responsibility

remains an imminent topic in today’s AI research. Responsible AI involves different aspects such as

transparency, security, robustness, and ethics. In this talk, we will introduce some of our work from the

perspective of robustness, which makes machine learning models more robust to unexpected scenarios.

Specifically, three scenarios got our attention: out-of-distribution generalization to distribution shift, semi-

supervised learning to lowresource labeling environment, and adversarial robustness to malicious attack.

After the introduction of these work, we will also introduce some preliminary robustness analysis to the

recent ChatGPT and large models. Finally, I will discuss some potential research topics in robustness.

报告人简介：

Dr. Jindong Wang is currently a Senior Researcher at Microsoft Research Asia. He obtained his Ph.D from

Institute of Computing Technology, Chinese Academy of Sciences in 2019. He visited Qiang Yang’s group at

Hong Kong University of Science and Technology in 2018. His research interest includes robust machine

learning, transfer learning, semisupervised learning, and federated learning. He published over 40 papers

with 5000 citations at leading conferences and journals such as ICLR, NeurIPS, CVPR, IJCAI, UbiComp,

ACMMM, TKDE, TASLP etc. He served as the senior program committee member of IJCAI and AAAI, and

PC members for other conferences like ICML, NeurIPS, ICLR, CVPR etc. He opensourced several projects

to help build a better community, such as transferlearning, torchSSL, USB, personalizedFL, and robustlearn,

which received over 10K stars on Github. He published a textbook called Introduction to Transfer Learning

in 2021 to help starters quickly learn transfer learning.