At this webinar we unpack how robust mean estimation algorithm can improve the performance of AI classification in the scenarios of adversarial attacks.
About the webinar
The talk is based on the published paper “Trimming Data Sets: a Verified Algorithm for Robust Mean Estimation” (Ieva Daukantas, Alessandro Bruni, Carsten Schürmann), where we introduce a mechanized proof of robustness of the trimmed mean algorithm. This algorithm has high applicability as a statistical technique and can be used in many complex applications of deep learning. We combine theoretical and practical approaches: the Coq proof assistant is used to formalize the robustness of the trimmed mean algorithm and Python Naïve Bayes experiments to illustrate the applicability in AI systems.
• The Context of the Problem
• Theory and Formal verification of Trimmed Mean Algorithm
• Empirical Examples
This webinar is a part of our Academic Corner series where we dive into the newest research from scholars within the field of IT research.