Explore a comprehensive lecture on the contraction of Markov kernels and its connection to differential privacy. Delve into advanced concepts presented by Shahab Asoodeh from McMaster University as part of the Information-Theoretic Methods for Trustworthy Machine Learning series. Gain insights into the mathematical foundations and practical applications of these topics in the field of machine learning and data privacy.
Contraction of Markov Kernels and Differential Privacy - Part I
Overview
Syllabus
Contraction of Markov kernels and differential privacy (PART I)
Taught by
Simons Institute
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Contraction of Markov Kernels and Differential Privacy - Part II
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The Saddle-Point Accountant for Differential Privacy
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Differential Privacy & Variants
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Improving Accuracy-Privacy Tradeoff via Model Reprogramming
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