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AIR Academic Salon Series 16 | LI Bo: Trustworthy Machine Learning: Robustness, Privacy, Generalization, and Intrinsic Connection

Source:       Time:2022-03-03
主讲人 LI Bo 时间 2022.2.23
地点 Turing Lecture Hall 精彩回顾

"The most essential problem of trustworthy machine learning can be reduced to the analysis of 'distribution drift' and 'distribution shift'", said LI Bo.


Overview


On February 23, the 16th AIR Academic Salon was held online as scheduled. In this issue, we are honored to have Prof. LI Bo from the Department of Computer Science, University of Illinois Urbana-Champaign to give a presentation entitled "Trusted Machine Learning: Robustness, Privacy, Generalization, and Their Intrinsic Correlates" online. The event was hosted by LIU Yang, Associate Professor of the Institute for AI Industry Research, Tsinghua University (AIR). Over 1,700 audience joined the livestream, and a total of nearly 3,000 views have accumulated as of now.



Read More: AIR Academic Salon Series 16 | LI Bo: Trustworthy Machine Learning: Robustness, Privacy, Generalization, and Intrinsic Connection

上一条:AIR Academic Salon Series 18 | HUANG Minlie: Emotional Intelligence in Conversational System 下一条:AIR Academic Salon Series 15 | XING Guoliang: Real-time Edge System of Vehicle-Infrastructure Cooperative Autonomous Driving

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