"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
