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3 March 2022 - 14h30
Machine Learning Safety
by Xiaowei Huang from University of Liverpool

Abstract: Machine learning has been proven practical in solving complex problems that cannot be solved before, but was also found to be not without any shortfall. This talk will address the safety and security risks of applying machine learning to safety critical systems. We will provide an overview of the known risks in the machine learning development cycle, discussing a number of properties such as generalisation, uncertainty, robustness, poisoning, backdoor, and some privacy-related properties. Based on the properties, we will discuss the existing methods, including verification and validation methods, that have been intensively developed in the past years to work with some of these properties. Future research directions will also be discussed.

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